MULTI DEGREE-OF-FREEDOM HYDRAULIC HUMAN POWER AMPLIFIER WITH RENDERING OF ASSISTIVE DYNAMICS
|
|
- Johnathan Craig
- 5 years ago
- Views:
Transcription
1 Proceedings of the ASME 216 Dynamic Systems and Control Conference DSCC216 October 12-14, 216, Minneapolis, Minnesota, USA DSCC MULTI DEGREE-OF-FREEDOM HYDRAULIC HUMAN POWER AMPLIFIER WITH RENDERING OF ASSISTIVE DYNAMICS Sangyoon Lee ERC for Compact and Efficient Fluid Power Department of Mechanical Engineering University of Minnesota Minneapolis, Minnesota Fredrik Eskilsson SAAB Aerospace Fo rmansgatan 11 Linko ping, Sweden ABSTRACT The hydraulic human power amplifier (HPA) is a tool similar to exoskeleton that uses hydraulic actuation to amplify the applied human force. The control objective is to make the system behave like a passive mechanical tool that interacts with the human and the environment passively with a specified power scaling factor. In our previous work, a virtual velocity coordination approach recasts the single degree-of-freedom human power amplifier control problem into a velocity coordination with a fictitious reference mechanical system. Force amplification becomes a natural consequence of the velocity coordination. In this paper, this control approach is extended for fully coupled multidof systems. A passivity based control approach that uses the natural energy storage of the hydraulic actuator to take full account of the nonlinear pressure dynamics is used to define the flow requirement. Additional passive assistance dynamics are designed and implemented to enable the user to perform specific tasks more easily. Guidance is achieved using a passive velocity field controller (PVFC), and obstacle avoidance is achieved using a potential field. Experimental results demonstrate good performance on a 2-DoF Human Power Amplifier. Perry Y. Li ERC for Compact and Efficient Fluid Power Department of Mechanical Engineering University of Minnesota Minneapolis, Minnesota lixxx99@umn.edu machine is an extension of his body while amplifying the applied human effort. The control objective is identical to that of a wearable exoskeleton. The only difference is that the human can let go of the HPA, but is an exoskeleton is always attached. In both cases, since the operator participates physically and directly in controlling the machine, it is more intuitive than using a remote joystick. Because of the direct, physical interaction, energetic passivity, which limits the amount of energy that can be transferred to the human and the environment, is a useful property for HPAs to ensure coupling stability and safety. In [1] it was shown that a direct approach to control actuator force as an amplified human force leads to a positive velocity feedback which is not robust in the presence of uncertainty, slow sampling or feedback noise. In [2] and [3], an alternate controller was proposed which models the actuator as a combination of an ideal velocity source and a nonlinear spring, the latter captures the compressibility effects of the fluid medium. Instead of controlling the actuator to track the desired force directly, the controller coordinates the velocities of the system and of a fictitious virtual mass whose dynamics are influenced by the hydraulic actuator and the human force. The control law for achieving coordination is accomplished via a passive decomposition [4 6] into a shape system and a locked system. This control is more robust as the passivity property is enforced by the control structure itself. A variety of control laws can be designed to stabilize the shape system to coordinate the velocities of the actual and virtual systems. In particular, a control law was derived in [7] using the natural compressibility energy of the hydraulic actuator [8] 1 Introduction The goal of the human power amplifier (HPA) is to enable a human operator to directly interact with the machine as if the Fredrik Eskilsson was an international exchange student at the University of Minnesota. 1 Copyright 216 by ASME
2 A Force Handle B Pm1 rm Pm2 Hydraulic Motor, Reach xp Dm xθ Qθ Pθ, A1 PT, A2 Hydraulic Actuator, Pitch θp FIGURE 1. Picture of the Human Power Amplifier by: FIGURE 2. Schematic of the Human Power Amplifier instead of approximating the hydraulic actuator by a nonlinear mechanical spring. In these previous works, the control was developed for each individual degree-of-freedom assumed to be decoupled. In this paper, we expand the results from [7] to a fully coupled multi-dof HPA. In addition, we also develop additional human-machine shared control strategies that render useful passive dynamics to assist the human to execute the task more easily. Guidance is achieved using the Passive Velocity Field Controller (PVFC) [9] to guide the HPA to move along the direction of a specified velocity field. Obstacle avoidance is achieved by incorporating potential fields [1] to prohibit the machine from entering prohibited zones. With these task oriented passive dynamics, the operator can execute tasks accurately with less attention while remaining in direct control since the he/she must supply a portion of the physical power. The rest of paper is organized as follow. System dynamics and control objectives are stated in Section 2. In Section 3, the reformulation of the problem as a velocity coordination is reviewed, followed by the presentation for the proposed flow requirement. Guidance dynamics in the form of PVFC and obstacle avoidance are presented in sections 4 and 5. Experimental results and concluding remarks are given in sections 6 and 7. 2 System Description and Model We consider a 2-DoF human power amplifier (HPA) shown in Fig. 1-2 with generalized coordinate q = [θ p,x p ] T where θ p describes the angular position of the pitch movement and x p describes the linear position of the reach movement. The pitch angular motion is actuated by a linear hydraulic actuator whereas the reach linear motion is actuated by a hydraulic motor via a pulley and belt mechanism. The dynamics of the HPA are given M p (q) q +C p ( q,q) q + G p (q) = F human + F env + F a (1) where M p (q) R 2X2 is the symmetric and positive definite inertia matrix, C p ( q,q) R 2X2 is the Coriolis matrix such that Ṁ p (q) 2C p ( q,q) is skew-symmetric, and G p (q) R 2X1 is the gravity vector. F human is the generalized torque/force applied by the human on a handle instrumented with force sensors; F env is the force exerted by the environment which is not measured. F a is the generalized actuator force/torque: F a = [ Tθ F x ] = [ JA (θ p ) 1 rm ][ Fθ where T θ and F x are the torque and force applied to the pitch and reach directions. As seen in Figure. 2, the pitch torque T θ is generated by a hydraulic cylinder with force F θ given by T x ] (2) F θ = P θ A 1 P T A 2 (3) where A 1 and A 2 are the cap side and piston side areas, P θ and P T are the supply and tank pressures on the cap and rod sides of the actuator. The Jacobian J A (θ p ) is used to translate linear force generated into a generalized torque. The reach force F x is generated by a fixed displacement hydraulic motor with torque T x given by: T x = P x D m where D m is the motor displacement, P x = P m1 P m2 is the pressure across the motor. The motor is connected to a belt via a pulley with a radius r m. The cap side of the hydraulic cylinder for the pitch motion is connected to the output of a hydraulic transformer [7] with pressure dynamics given by: (4) Ṗ θ = β(p θ ) V 1 (x θ ) (Q θ A 1 ẋ θ ) (5) 2 Copyright 216 by ASME
3 where Q θ is the flow input to the cap side chamber, V 1 (x θ ) = V 1 + A 1 x θ (6) is the fluid volume in the cap-side chamber and the hose dependent on the linear displacement of the cylinder x θ, β(p θ ) is the pressure dependent fluid bulk modulus. The rod side is connected to the lower common pressure rail so that P T is assumed to be constant. It is assumed that the gravity load (in F env ) is sufficiently large such that over-running load and cavitation will not occur. The pressure dynamics for the two sides of the hydraulic motor are: Ṗ m1 = β(p ( m1) V m Ṗ m2 = β(p m2) V m ) ẋ p r m ẋ p r m Q m1 D m ( Q m2 + D m where V m are the fixed fluid volumes in the motor and the hose which, for simplicity, are the same on both sides of the motor, β( ) is the pressure dependent bulk modulus. Flow Q m1 = Q m2 are the equal input and return flows in the servo valve. Let P x = P m1 P m2 and taking the difference between these dynamics, the motor pressure dynamics are: Ṗ x = Ṗ m1 Ṗ m2 = β e(p x,p m1 ) V m ) ( Q x D m ẋ p r m where β e (P x,p m1 ) = β(p m1 )+β(p m2 ) is the effective pressure dependent bulk modulus rising from the pressure difference across the motor, and Q x := Q m1 = Q m2 is the flow input to the motor. In the experiment, Q θ and Q x are respectively controlled by a custom built hydraulic transformer [11] and a servo valve. Readers are referred to [7, 11, 12] and [13] for details of how these flows are achieved with these devices. Control Objective The control objective is to control flow inputs to the hydraulic actuators Q θ and Q x in (5) and (9) such that the applied human force is amplified by a factor of (ρ + 1), which results in the target dynamics of a passive mechanical tool M L (q) q+c L ( q,q) q = (ρ +1)F human +F env G(q)+F guide (1) where M L (q) and C L ( q,q) are the apparent inertia and associated Coriolis matrix of the tool to be designed. The human would feel that he/she is interacting with an inertia and an environment force that are attenuated as M L /(ρ +1), F env /(ρ +1) and G(q)/(ρ +1) respectively. For M L (q) M p (q), this can be achieved if the generalized actuator force satisfies: F a ρf human ) (7) (8) (9) Fd=ρFhuman MV qv Fhuman+Fenv FIGURE 3. Model of hydraulic actuator with the ideal velocity command provided by the velocity of a virtual inertia, and the virtual inertia affected by the actuator force. In a human power amplifier F d = ρf human F guide is the additional task specific guidance dynamics to provide assistance to the user to operate the HPA. Fa 3 Virtual Coordination Control Approach to Force Tracking Instead of directly controlling the actuator force F a to track the desired force ρf human, the virtual coordination approach in [2] converts the problem into one of coordinating velocities of two coupled mechanical systems - the plant and a virtual inertia. Besides avoiding the need for positive velocity feedback, this approach can also be interpreted physically as an interconnection of passive components so that it is more robust and safer to operate. With the actuator compressibility represented by a springlike object that interacts with the inertia of the machine M p, the approach is to control F a such that the other end of the springlike object is interacting with a small virtual inertia M v R acted on by a set of desired forces (Fig. 3). Let the dynamics of a virtual inertia M v (implemented as part of the controller) be given by: qi MP M v q v = F d F a + w + F guide (11) where q v = [θ v,x v ] T is the generalized coordinate for the virtual inertia, F d = ρf human is the desired force, F a is the generalized actuator force (Eq. (2)) and w and F guide are the additional controls to be designed for the locked system or for task guidance. If exact coordination between the virtual inertia M v and M p (q), such that q v (t) q(t) (i.e. they become a single rigid inertia), then comparing (1) and (11), and the fact that w will be defined such that w when coordinated, the resulting dynamics becomes: (M v + M p (q)) q +C p ( q,q) q = (ρ + 1)F human + F env G p (q) + F guide which is the desired target dynamics in (1) with the apparent inertia being M L (q) = M v + M p (q), and C L (q, q) = C p (q, q). 1 For simplicity, M v is a constant inertia represented by a positive definite matrix. 3 Copyright 216 by ASME
4 As will be seen, the guidance force F guide will be designed to satisfy a passivity property: t [ q T F guide ]dτ c 2 g (12) Then, after coordination q v (t) q(t), the closed loop system is energetically passive with respect to the scaled power input by the human and environment such that there exists c 2 > so that for all F human ( ), F env ( ), t q T [(ρ + 1)F human + F env ]dτ c 2 (13) We have used the fact that gravity is conservative such that G p (q) = V G(q) q (14) where V G (q) R is the gravitational potential field and q lies in a compact work space. In the following, we extend the virtual coordination controller in [7] that uses the natural energy storage function for the hydraulic actuators to fully coupled multi-dof systems. 3.1 Passive Decomposition into Locked and Shape Systems The coupled system of Eq. (1) and (11) is given by M p (q) q +C p ( q,q) q + G p (q) = F human + F env + F a M v q v = F d F a + w + F guide (15) where the generalized coordinates for the physical system are q = [θ p,x p ] T and for the virtual system are q v = [θ v,x v ] T. As we are interested in coordination between q and q v, i.e, V E := q q v it is desirable to study the problem in relative coordinates. However, we also do not wish to disturb the energetics of the desired target dynamics in Eq. (1). Therefore, we apply the passive decomposition [4 6] to transform the velocities into locked and shape coordinates: VL I φ φ q = (16) V E I I q v }{{} S(q) where I φ = [M p (q) + M v ] 1 M p (q) φ = [M p (q) + M v ] 1 M v and M L (q) = M p (q) + M v is the inertia corresponding to the locked system. V L (locked system velocity) is the velocity of the center of mass of the combined virtual and actual system, whereas V E (shape system velocity) is the velocity coordination error. The dynamics in the transformed coordinates are given by: ML (q) V L + M E (q) V E [ CL ( q,q) C LE ( q,q) C EL ( q,q) C E ( q,q) ][ VL V E ] = ψ (17) where the inertia matrix and Coriolis matrix are transformed according to the definition of the passive decomposition [4], ML (q) M E (q) = S T Mp (q) M v = CL ( q,q) C LE ( q,q) C EL ( q,q) C E ( q,q) + d dt S T [ Mp (q) M v S 1 [ Mp (q) + M v (I φ) T M v (I φ) ] (18) = S T Mp (q) d M v dt (S 1 ) ] S 1 + S T C( q,q) S 1 (19) Forces acting on the virtual and actual inertia are: ψ = S T Fhuman + F env + F a G p (q) F d F a + w + F guide The transformed system is represented as M L (q) V L +C L ( q,q)v L +C LE ( q,q)v E = (2) F d + F env + F human G(q) + w + F guide (21) M E (q) V E +C E ( q,q)v E +C EL ( q,q)v L = F a + φ(f env ) }{{} F E1 +φ(f human G(q)) (I φ)(f d + w + F guide ) (22) }{{} F E2 3.2 Shape System Control From (22), the shape system dynamics are: M E (q) V E +C E ( q,q)v E +C EL ( q,q)v L = F a + F E1 + F E2 (23) where F E2 contains measurable or known terms and F E1 is potentially unknown. We define the desired actuator force to achieve shape system control to be: Fa,d1 F a,d = = C EL ( q,q)v L λv E ˆF E1 F E2 (24) F a,d2 4 Copyright 216 by ASME
5 where ˆF E1 is an estimate of F E1 and λ >. The nonlinear decoupling term C EL ( q,q)v L is needed to decouple the locked system dynamics from the shape system dynamics, which is not necessary for single DoF control. The following input flows for each degree of freedom in (5) and (9) are proposed: Q d θ = A 1J A (θ p ) θ v + V 1(x θ ) β(p d,θ )Ṗd,θ λ pθ P θ (25) Q d x = D m ẋ v r m + V x β e (P d,x )Ṗd,x λ px P x (26) where P d,θ and P d,x are the desired actuator pressures in the pitch and reach directions given by: P d,θ = 1 [ P T A ] F a,d1 (27) A 1 J A P d,x = r m D m F a,d2 (28) The estimate for the external force Ḟ E1 is obtained from the adaptation algorithm, ˆF E1 = σv E + Ḟ E1 (29) where Ḟ E1 is the best estimate of the time derivative of F E1 ; λ, λ pθ, λ px, and σ are all positive constants. To see that this control is appropriate, consider the following Lyapunov function, W = 1 2 V T E M E V E + 1 σ F T E1 F E1 +V 1 (x θ )W V ( P θ,p d,θ ) +V m W V ( P x,p d,x ) (3) where F E1 is the error in estimating the unknown external force; W V ( P θ,p d,θ ) is the volumetric pressure error energy density associated with compressing the fluid from pressure P d,θ to P d,θ + P θ as defined in [8]. Likewise, W V ( P x,p d,x ) is defined in the same manner. Differentiating the above Lyapunov function (See [8] and [7] for details and proof), and with λ p,θ and λ p,x sufficiently large we get, Ẇ V T E λv E m θ (λ p,θ ) P 2 θ m x(λ p,x ) P 2 x such that m θ (λ p,θ ) > and m x (λ p,x ) >. This in turn shows that V E, P θ, P x. 3.3 Locked System Control From (22), the Locked system dynamics are: M L (q) V L +C L ( q,q)v L +C LE ( q,q)v E = F d + F env + F human G(q) + w + F guide (31) We can design w to cancel out the coupling dynamics w = C LE ( q,q)v E (32) Note that w is also used in shape system control because of w in F E2 (see (22)). If desired, G(q) could be included in w to cancel out the effect of gravity. With F d = ρf human, and after coordination (i.e. V L = q = q v ): M L (q) q+c L ( q,q) q = (ρ +1)F human +F env G(q)+F guide (33) which is the target dynamics we wanted in Eq. (1) 4 Passive Velocity Field Controller In this and next section, we design F guide n Eq. (11) to provide useful dynamics to assist the human operator in his/her task. This section discusses the use of Passive Velocity Field Control (PVFC) to impart guidance; [9] [14]. Obstacle avoidance strategy will be discussed in section 5. An earlier attempt to implement useful dynamics for HPA can be found in [15]. In PVFC, passive dynamics are incorporated into the machine to guide the operator to follow a scaled copy of a desired velocity field - i.e. a desired velocity at each position, while allowing the machine to remain passive. This can guide the human operator to follow a desired path which is the flow of the velocity field. An example velocity field is shown in Fig. 4 which guides the HPA to converge to and follow a circle. The speed at which the field is followed is determined by the kinematic energy available in this system. In this way, it is possible to provide path guidance without violating passivity. In particular, the energy input into the system must be provided either by the human operator or the environment. As the operator is physically connected to the machine in HPA operation, PVFC works as a feedback to the operator informing whether he is on the right track. For safety and comfort, it is still important that the machine remains passive. An outline of how PVFC incorporates into HPA control is given below. Readers are referred to [9] [14] for detailed proofs. In this paper, we incorporate this guidance dynamics to the locked system dynamics in (31) and use the virtual coordination scheme rather than directly to the physical system. 4.1 Desired Velocity Field and Augmented System Let the desired velocity field be V (q) R 2 which defines at each configuration q a desired velocity V (q). In this paper, the example velocity field depicted in Fig. 4 is used to assist the user to perform a circular motion. In the absence of any human or environmental input, the PVFC controller will cause q(t) β(t)v (q(t)) where β 2 (t) is proportional to the kinetic energy in the system. 5 Copyright 216 by ASME
6 Y movement [m] Velocity Field Desired Path X movement [m] FIGURE 4. Velocity field for tracing a circle such that the kinetic energy of the augmented system is constant when the augmented field is tracked. This can be accomplished by ensuring for all q R 2, Ē = 1 2 V T (q) M(q) V (q) where Ē is a sufficiently large constant. In other words, the desired flywheel velocity field is given by: V F (q) = 2 M F ( Ē 1 ) 2 V (q)t M L (q)v (q) (4) 4.2 PVF Controller With the augmented system and augmented velocity field, the coupling control τ can be designed as To define PVFC, we first augment the system dynamics with a 1 DoF fictitious flywheel dynamics: M F q F = τ F (34) where M F is the apparent inertia of this virtual flywheel, q F is the position of the flywheel, and τ F is the coupling control input to the flywheel. Combined with the locked system in (31), the augmented system becomes: M(q) q + C(q, q) q = τ + τ e (35) where q = [ V T L q F] T are the augmented velocity, τ = [ F T guide τ T F ] T being the augmented control input, τe = [ τ T e ] T being the augmented external force where τ e = F env + (ρ + 1)F human G(q) (36) ML (q) M(q) =, C(q, q) = M F CL (q, q) (37) are the augmented inertia matrix and the augmented Coriolis matrix. The kinetic energy of the augmented system is k( q, q) = 1 2 q T M( q) q = 1 2 V L T M L (q)v L + 1 }{{} 2 M F q 2 F }{{} Locked System flywheel (38) In order to control and utilize the virtual flywheel, the desired velocity field V (q) needs to be augmented as: V (q) = [ V (q) T V F (q) ] T (39) τ = Ω(q, q) q (41) where Ω(q, q) R (n+1) (n+1) is skew symmetric. This ensures that q T τ = so that the PVFC control is passive. The coupling force re-distributes energy between the locked system and the fictitious flywheel conservatively. To find suitable Ω(q, q), the followings are defined: P(q) = M(q) V (q) (42) p(q, q) = M( q) q (43) w(q, q) = M(q) V (q) + C(q, q) V (q) (44) where P(q) is the desired momentum field, p(q, q) is the actual momentum, and w(q, q) is the covariant derivative of the desired momentum field. With these, the coupling control law is given by where or τ( q, q) = τ c ( q, q) + τ f ( q, q) (45) τ c = 1 2Ē ( w P T P w T ) q (46) τ f = γ( P p T p P T ) q (47) Ω(q, q) = 1 2Ē ( w P T P w T ) + γ( P p T p P T ) (48) τ c corresponds to a feedfoward control, giving information about the desired system dynamics, and τ f is a feedback control which vanishes when q = α(t) V (q(t)) for some scalar α(t), γ is a control gain that determines the convergence rate and the sense in which the desired velocity will be followed. 6 Copyright 216 by ASME
7 The PVFC component of F guide is then the first two elements of the coupling control in (45) such that.12 Potential Field 1 9 [ FPV FC τ F ] = τ( q, q) (49) Y Properties of PVFC With the control in (41)-(48), the closed loop dynamics for the coupled augmented system can be written as M( q) q +Ȳ ( q, q) q = τ e (5) X where τ e is the augmented external force and Ȳ R 3X3 is: Ȳ (q, q) = C(q, q) 1 2Ē ( w P T P w T ) γ ( P p T p P T ) }{{}}{{} skew symmetric skew symmetric (51) Notice that since both control terms in (46) and (47) are skew symmetric, M 2Ȳ is also skew symmetric, just as Ṁ 2C is skew symmetric for the robot manipulator. Utilizing this skew symmetric structure and (5), to differentiate kinetic energy in (38), we obtain d dt k( q, q) = V T L (t)τ e (t) (52) where VL T τ e = q T τ e because τ e = [ τe T ] T. Integrating (52) w.r.t time gives t V T L (t)τ e (t)dτ c 2 (53) This means that the closed loop dynamics of the augmented system given by (5) is passive with respect to the supply rate V T L τ e (the power produced by the external forces), and its kinetic energy in (38) is its storage function. With the PVFC controller, it can be shown that, in the absence of τ e, q β(t) V (q(t)) (54) FIGURE 5. An example potential field for a point obstacle in Cartesian (workspace) coordinates. 5 Obstacle Avoidance 5.1 Obstacle Avoidance Control The aim of Obstacle Avoidance Control is to prevent the machine from entering prohibited area in the workspace to protect itself or other objects. Here we utilize an artificial potential field approach [1] [16] to provide the operator a tactile feedback to repel the machine from the obstacle. The potential field is designed to be non-negative continuous and differentiable function that tends to infinity as the machine approaches the obstacle. It is also designed such that the influence of the potential field is limited to certain to avoid having undesirable perturbing forces beyond the obstacle s vicinity. For a point obstacle, an example potential field U oa (q) expressed in the Cartesian (workspace) coordinates (X(q),Y (q)) of the tip of the HPA is shown in (Fig. 5). This field is exponentially decaying (with distance) and is radially symmetric in the workspace coordinates. The force arising from this potential function is the negative gradient of the above function such that: where β(t) = sign(γ) k(q, q) Ē Thus, as the kinetic energy of the system increases (such as with input by the human operator or the environment, the speed at which the desired velocity is tracked will also increase. F OA = U oa(q) q The combined guidance control is: F guide = F PV FC + F OA (55) 7 Copyright 216 by ASME
8 6 Results and Discussion 6.1 Energetic Passivity Property With the total energy function: W total = 1 2 V T E M E V E + 1 σ F T E1 F E1 +V 1 (x θ )W V ( P θ,p d,θ ) +V m W V ( P x,p d,x ) V T L M L V L M F q 2 F +U oa (q) +V G (q) (56) which includes the kinetic and potential energies of the physical system, the kinetic energies of the virtual inertia and the fictitious flyhweel of PVFC, and the obstacle avoidance potential field, it can be shown (by differentiating W total and integrating over time) that: t VL T [(ρ + 1)F human + F env ]dτ c 2. (57) This shows that after the coordination, i.e. V L q (ensured by the shape system control), and the closed loop system achieves the target energetic passivity in Eq. (13) with the supply rate being the scaled power input from the human and the environment DoF Virtual Coordination The controller in Section 3 has been experimentally implemented on a 2-DoF Human Power Amplifier (HPA) in Fig. 2. The pitch motion (Fig. 6) is actuated by a hydraulic transformer and the reach motion (Fig. 7) is actuated by a servo valve. Velocities of the virtual inertia and the actual system are coordinated for each DoF, showing RMS error of.86 rad/s and.11 m/s for the pitch and reach directions, respectively. With F d = ρf human, where ρ = 7, the pitch direction shows 6.9 Nm of RMS torque error and the reach direction shows 1.75 N of RMS force error. 6.3 PVFC Figure 8 shows PVFC converging to a desired path through desired direction and tracing a circle continuously afterwards. This is achieved through following a desired velocity shown in Fig. 9, which shows RMS error of.15 rad/s for pitch movement,.69 m/s for reach movement, and.135 rad/s for the virtual flywheel. [Nm] [rad/s] 1 5 Pitch Actual Desired Pitch motion θ p ; Top: torque tracking; Bottom: coordi- FIGURE 6. nation. [N] [m/s] 1 5 Virtual Actual Time [s] 5 Reach 1 Actual Desired Reach motion x p ; Top: force tracking; Bottom: coordi- FIGURE 7. nation. Virtual Actual Time [s] 6.4 Obstacle Avoidance Figure 1 shows the results for the Obstacle Avoidance control. A symmetric potential field was defined (in the Cartesian coordinates) and as a result, the machine is not allowed to enter into the circular prohibited region. 7 Conclusions In this paper, the results of virtual coordination framework from [7] is extended for fully coupled multi-dof system. A passivity based control approach that uses natural energy storage of the hydraulic actuator is used to define the flow requirement. Additional passive dynamics that helps the user to perform specific tasks, previously implemented on direct force control framework, are implemented with the virtual coordination framework. Guidance is achieved using a passive velocity field controller (PVFC), while the obstacle avoidance is achieved using a potential field. Experimental results demonstrate good performance on a 2-DoF Human Power Amplifier. 8 Copyright 216 by ASME
9 .1.5 Pitch Y movement [m] Velocity Field.7 Circle Actual Path Range of Motion X movement [m] FIGURE 8. Guidance velocity field and resulting motion in Cartesian coordinates (of the tip) Reach Flywheel ACKNOWLEDGMENT This work is performed within the Center for Compact and Efficient Fluid Power (CCEFP) supported by the National Science Foundation under grant EEC Donation of components from Takako Industries is gratefully acknowledged. 3 V k( q, q) Ē Time [s] q REFERENCES [1] Li, P. Y., 24. Design and control of a hydraulic human power amplifier. In ASME 24 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, pp [2] Li, P. Y., 26. A new passive controller for a hydraulic human power amplifier. In ASME 26 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, pp [3] Li, P. Y., and Durbha, V., 28. Passive control of fluid powered human power amplifiers. In Proceedings of the JFPS International Symposium on Fluid Power, no. 7-1,, pp [4] Lee, D. J., and Li, P. Y., 25. Passive bilateral control and tool dynamics rendering for nonlinear mechanical teleoperators. Robotics, IEEE Transactions on, 21(5), pp [5] Lee, D. J., and Li, P. Y., 27. Passive decomposition approach to formation and maneuver control of multiple rigidbodies. ASME Journal of Dynamic Systems, Measurement and Control, 129, September, pp [6] Lee, D. J., and Li, P. Y., 213. Passive decomposition of multiple mechanical systems under coordination requirements. IEEE Transactions on Automatic Control, 58, January, pp FIGURE 9. Y movement [m] Actual velocity vs scaled desired velocity HPA Movement.8 Obstacle Range of Motion X movement [m] FIGURE 1. Obstacle Avoidance [7] Lee, S., and Li, P. Y., 215. Passive control of a hydraulic human power amplifier using a hydraulic transformer. In ASME 215 Dynamic Systems and Control Conference, American Society of Mechanical Engineers, pp. V2T27A4 V2T27A4. 9 Copyright 216 by ASME
10 [8] Li, P. Y., and Wang, M., 214. Natural storage function for passivity-based trajectory control of hydraulic actuators. IEEE/ASME Transactions on Mechatronics, 19(3), July, pp [9] Li, P. Y., and Horowitz, R., Passive velocity field control of mechanical manipulators. Robotics and Automation, IEEE Transactions on, 15(4), pp [1] Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots. The international journal of robotics research, 5(1), pp [11] Lee, S., and Li, P. Y., 215. Passivity based backstepping control for trajectory tracking using a hydraulic transformer. In ASME/BATH 215 Symposium on Fluid Power and Motion Control, American Society of Mechanical Engineers, pp. V1T1A64 V1T1A64. [12] Lee, S., and Li, P. Y., 214. Trajectory tracking control using a hydraulic transformer. 214 International Symposium on Flexible Automation, Awaji Island, Japan. [13] Li, P. Y., 26. A new passive controller for a hydraulic human power amplifier. In ASME 26 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, pp [14] Lee, D., 24. Passive decomposition and control of interactive mechanical systems under motion coordination requirements. PhD thesis, University of Minnesota. [15] Eskilsson, F., 211. Passive control for a human power amplifier, providing force amplification, guidance and obstacle avoidance. Master s thesis, Linkping University. (Research performed as part of an international exchange at the University of Minnesota.). [16] Rimon, E., and Koditschek, D. E., Exact robot navigation using artificial potential functions. Robotics and Automation, IEEE Transactions on, 8(5), pp Copyright 216 by ASME
MULTI DEGREE-OF-FREEDOM HYDRAULIC HUMAN POWER AMPLIFIER WITH RENDERING OF ASSISTIVE DYNAMICS
Proceedings of Dynamic Systems and Control Conference DSCC 216 October 12-14, 216, Minneapolis, MN, USA DSCC216-9781 MULTI DEGREE-OF-FREEDOM HYDRAULIC HUMAN POWER AMPLIFIER WITH RENDERING OF ASSISTIVE
More informationDSCC PASSIVE CONTROL OF A HYDRAULIC HUMAN POWER AMPLIFIER USING A HYDRAULIC TRANSFORMER
Proceedings of the ASME 25 Dynamic Systems and Control Conference DSCC25 October 28-3, 25, Columbus, Ohio, USA DSCC25-9734 PASSIVE CONTROL OF A HYDRAULIC HUMAN POWER AMPLIFIER USING A HYDRAULIC TRANSFORMER
More informationPASSIVE CONTROL OF FLUID POWERED HUMAN POWER AMPLIFIERS
OS9-3 Proceedings of the 7th JFPS International Symposium on Fluid Power, TOYAMA 28 September 5-8, 28 PASSIVE CONTROL OF FLUID POWERED HUMAN POWER AMPLIFIERS Perry Y. Li and Venkat Durbha Center for Compact
More informationFPMC PASSIVITY BASED BACKSTEPPING CONTROL FOR TRAJECTORY TRACKING USING A HYDRAULIC TRANSFORMER
Proceedings of the ASME/BATH 25 Symposium on Fluid Power & Motion Control FPMC25 October 2-4, 25, Chicago, Illinois, United States FPMC25-968 PASSIVITY BASED BACKSTEPPING CONTROL FOR TRAJECTORY TRACKING
More informationA NEW PASSIVE CONTROLLER FOR A HYDRAULIC HUMAN POWER AMPLIFIER
Proceedings of IMECE26 26 ASME International Mechanical Engineering Congress and Exposition November 5-1, 26, Chicago, Illinois, USA IMECE26-1556 A NEW PASSIVE CONTROLLER FOR A HYDRAULIC HUMAN POWER AMPLIFIER
More informationIndependent Metering of Pneumatic Actuator for Passive Human Power Amplification
2010 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 30-July 02, 2010 ThB05.6 Independent Metering of Pneumatic Actuator for Passive Human Power Amplification Venkat Durbha and
More informationPassive Bilateral Control of Nonlinear Mechanical Teleoperators
1 Passive Bilateral Control of Nonlinear Mechanical Teleoperators Dongjun Lee Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 117 Transportation Building, 14 S. Mathews Ave.,
More informationDESIGN AND CONTROL OF A HYDRAULIC HUMAN POWER AMPLIFIER
Proceedings of IMECE4 24 ASME International Mechanical Engineering Congress and Exposition November 13-2, 24, Anaheim, California USA IMECE24-6868 DESIGN AND CONTROL OF A HYDRAULIC HUMAN POWER AMPLIFIER
More informationBond graph Based Approach To Passive Teleoperation Of A Hydraulic Backhoe
Bond graph Based Approach To Passive Teleoperation Of A Hydraulic Backhoe Kailash Krishnaswamy and Perry Y. Li Abstract Human operated, hydraulic actuated machines are widely used in many high-power applications.
More informationDSCC2012-MOVIC
ASME 5th Annual Dynamic Systems and Control Conference joint with the JSME th Motion and Vibration Conference DSCC-MOVIC October 7-9,, Fort Lauderdale, Florida, USA DSCC-MOVIC-8784 DISPLACEMENT CONTROL
More informationIMECE BONDGRAPH BASED APPROACH TO PASSIVE TELEOPERATION OF A HYDRAULIC BACKHOE
Proceedings of IMECE ASME International Mechanical Engineering Congress and Exposition November -,, Anaheim, California USA IMECE-6 BONDGRAPH BASED APPROACH TO PASSIVE TEEOPERATION OF A HYDRAUIC BACKHOE
More informationq 1 F m d p q 2 Figure 1: An automated crane with the relevant kinematic and dynamic definitions.
Robotics II March 7, 018 Exercise 1 An automated crane can be seen as a mechanical system with two degrees of freedom that moves along a horizontal rail subject to the actuation force F, and that transports
More information936 IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 5, OCTOBER 2005
936 IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 5, OCTOBER 2005 Passive Bilateral Control and Tool Dynamics Rendering for Nonlinear Mechanical Teleoperators Dongjun Lee, Member, IEEE, and Perry Y. Li,
More informationTrajectory-tracking control of a planar 3-RRR parallel manipulator
Trajectory-tracking control of a planar 3-RRR parallel manipulator Chaman Nasa and Sandipan Bandyopadhyay Department of Engineering Design Indian Institute of Technology Madras Chennai, India Abstract
More informationPassive Bilateral Feedforward Control of Linear Dynamically Similar Teleoperated Manipulators
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 19, NO. 3, JUNE 2003 443 Passive Bilateral Feedforward Control of Linear Dynamically Similar Teleoperated Manipulators Dongjun Lee, Student Member, IEEE,
More informationmatic scaling, ii) it can provide or bilateral power amplication / attenuation; iii) it ensures the passivity o the closed loop system with respect to
Passive Control o Bilateral Teleoperated Manipulators Perry Y. Li Department o Mechanical Engineering University o Minnesota 111 Church St. SE Minneapolis MN 55455 pli@me.umn.edu Abstract The control o
More informationCase Study: The Pelican Prototype Robot
5 Case Study: The Pelican Prototype Robot The purpose of this chapter is twofold: first, to present in detail the model of the experimental robot arm of the Robotics lab. from the CICESE Research Center,
More informationControlling the Apparent Inertia of Passive Human- Interactive Robots
Controlling the Apparent Inertia of Passive Human- Interactive Robots Tom Worsnopp Michael Peshkin J. Edward Colgate Kevin Lynch Laboratory for Intelligent Mechanical Systems: Mechanical Engineering Department
More informationPASSIFICATION OF ELECTROHYDRAULIC VALVES USING BOND GRAPHS
Copyright 22 IFAC 5th Triennial World Congress, Barcelona, Spain PASSIFICATION OF ELECTROHYDRAULIC VALVES USING BOND GRAPHS Perry Y. Li Roger F. Ngwompo 2 Department of Mechanical Engineering, University
More informationRobust Control of Cooperative Underactuated Manipulators
Robust Control of Cooperative Underactuated Manipulators Marcel Bergerman * Yangsheng Xu +,** Yun-Hui Liu ** * Automation Institute Informatics Technology Center Campinas SP Brazil + The Robotics Institute
More informationControl of Robot. Ioannis Manganas MCE Master Thesis. Aalborg University Department of Energy Technology
Control of Robot Master Thesis Ioannis Manganas MCE4-3 Aalborg University Department of Energy Technology Copyright c Aalborg University 8 LATEXhas been used for typesetting this document, using the TeXstudio
More informationGAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL
GAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL 1 KHALED M. HELAL, 2 MOSTAFA R.A. ATIA, 3 MOHAMED I. ABU EL-SEBAH 1, 2 Mechanical Engineering Department ARAB ACADEMY
More informationA Design Method of A Robust Controller for Hydraulic Actuation with Disturbance Observers
A Design Method of A Robust Controller for Hydraulic Actuation with Disturbance Observers Hiroaki Kuwahara, Fujio Terai Corporate Manufacturing Engineering Center, TOSHIBA Corporation, Yokohama, Japan
More informationTTK4150 Nonlinear Control Systems Solution 6 Part 2
TTK4150 Nonlinear Control Systems Solution 6 Part 2 Department of Engineering Cybernetics Norwegian University of Science and Technology Fall 2003 Solution 1 Thesystemisgivenby φ = R (φ) ω and J 1 ω 1
More informationPort-based Modeling and Control for Efficient Bipedal Walking Machines
Port-based Modeling and Control for Efficient Bipedal Walking Machines Vincent Duindam vincentd@eecs.berkeley.edu Control Laboratory, EE-Math-CS University of Twente, Netherlands Joint work with Stefano
More informationCONTROL OF ROBOT CAMERA SYSTEM WITH ACTUATOR S DYNAMICS TO TRACK MOVING OBJECT
Journal of Computer Science and Cybernetics, V.31, N.3 (2015), 255 265 DOI: 10.15625/1813-9663/31/3/6127 CONTROL OF ROBOT CAMERA SYSTEM WITH ACTUATOR S DYNAMICS TO TRACK MOVING OBJECT NGUYEN TIEN KIEM
More informationRobotics. Dynamics. Marc Toussaint U Stuttgart
Robotics Dynamics 1D point mass, damping & oscillation, PID, dynamics of mechanical systems, Euler-Lagrange equation, Newton-Euler recursion, general robot dynamics, joint space control, reference trajectory
More informationMCE/EEC 647/747: Robot Dynamics and Control. Lecture 12: Multivariable Control of Robotic Manipulators Part II
MCE/EEC 647/747: Robot Dynamics and Control Lecture 12: Multivariable Control of Robotic Manipulators Part II Reading: SHV Ch.8 Mechanical Engineering Hanz Richter, PhD MCE647 p.1/14 Robust vs. Adaptive
More informationReal-time Motion Control of a Nonholonomic Mobile Robot with Unknown Dynamics
Real-time Motion Control of a Nonholonomic Mobile Robot with Unknown Dynamics TIEMIN HU and SIMON X. YANG ARIS (Advanced Robotics & Intelligent Systems) Lab School of Engineering, University of Guelph
More informationIROS 16 Workshop: The Mechatronics behind Force/Torque Controlled Robot Actuation Secrets & Challenges
Arne Wahrburg (*), 2016-10-14 Cartesian Contact Force and Torque Estimation for Redundant Manipulators IROS 16 Workshop: The Mechatronics behind Force/Torque Controlled Robot Actuation Secrets & Challenges
More informationRobust Model Free Control of Robotic Manipulators with Prescribed Transient and Steady State Performance
Robust Model Free Control of Robotic Manipulators with Prescribed Transient and Steady State Performance Charalampos P. Bechlioulis, Minas V. Liarokapis and Kostas J. Kyriakopoulos Abstract In this paper,
More informationNonlinear PD Controllers with Gravity Compensation for Robot Manipulators
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 4, No Sofia 04 Print ISSN: 3-970; Online ISSN: 34-408 DOI: 0.478/cait-04-00 Nonlinear PD Controllers with Gravity Compensation
More informationNONLINEAR MECHANICAL SYSTEMS (MECHANISMS)
NONLINEAR MECHANICAL SYSTEMS (MECHANISMS) The analogy between dynamic behavior in different energy domains can be useful. Closer inspection reveals that the analogy is not complete. One key distinction
More informationRESEARCH ON AIRBORNE INTELLIGENT HYDRAULIC PUMP SYSTEM
8 TH INTERNATIONAL CONGRESS OF THE AERONAUTICAL SCIENCES RESEARCH ON AIRBORNE INTELLIGENT HYDRAULIC PUMP SYSTEM Jungong Ma, Xiaoye Qi, Juan Chen BeiHang University,Beijing,China jgma@buaa.edu.cn;qixiaoye@buaa.edu.cn;sunchenjuan@hotmail.com
More informationNonlinear disturbance observers Design and applications to Euler-Lagrange systems
This paper appears in IEEE Control Systems Magazine, 2017. DOI:.19/MCS.2017.2970 Nonlinear disturbance observers Design and applications to Euler-Lagrange systems Alireza Mohammadi, Horacio J. Marquez,
More informationLinear Feedback Control Using Quasi Velocities
Linear Feedback Control Using Quasi Velocities Andrew J Sinclair Auburn University, Auburn, Alabama 36849 John E Hurtado and John L Junkins Texas A&M University, College Station, Texas 77843 A novel approach
More informationPassive Velocity Field Control (PVFC): Part I Geometry and Robustness
1346 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 46, NO. 9, SEPTEMBER 2001 Passive Velocity Field Control (PVFC): Part I Geometry and Robustness Perry Y. Li, Member, IEEE and Roberto Horowitz, Member,
More informationfor Articulated Robot Arms and Its Applications
141 Proceedings of the International Conference on Information and Automation, December 15-18, 25, Colombo, Sri Lanka. 1 Forcefree Control with Independent Compensation for Articulated Robot Arms and Its
More information1 Introduction Hydraulic systems have been used in industry in a wide number of applications by virtue of their small size-to-power ratios and the abi
NONLINEAR ADAPTIVE ROBUST CONTROL OF ONE-DOF ELECTRO-HYDRAULIC SERVO SYSTEMS Λ Bin Yao George T. C. Chiu John T. Reedy School of Mechanical Engineering Purdue University West Lafayette, IN 47907 Abstract
More informationRobotics. Dynamics. University of Stuttgart Winter 2018/19
Robotics Dynamics 1D point mass, damping & oscillation, PID, dynamics of mechanical systems, Euler-Lagrange equation, Newton-Euler, joint space control, reference trajectory following, optimal operational
More informationNonlinear Adaptive Robust Control. Theory and Applications to the Integrated Design of Intelligent and Precision Mechatronic Systems.
A Short Course on Nonlinear Adaptive Robust Control Theory and Applications to the Integrated Design of Intelligent and Precision Mechatronic Systems Bin Yao Intelligent and Precision Control Laboratory
More informationBilateral teleoperation system for a mini crane
Scientific Journals of the Maritime University of Szczecin Zeszyty Naukowe Akademii Morskiej w Szczecinie 19, 7 (129), 63 69 ISSN 1733-867 (Printed) Received:.12.18 ISSN 2392-378 (Online) Accepted: 12.3.19
More information(W: 12:05-1:50, 50-N202)
2016 School of Information Technology and Electrical Engineering at the University of Queensland Schedule of Events Week Date Lecture (W: 12:05-1:50, 50-N202) 1 27-Jul Introduction 2 Representing Position
More informationSensorless Torque/Force Control
4 Sensorless Torque/Force Control Islam S. M. Khalil and Asif Sabanovic Sabanci University Turkey 1. Introduction Motion control systems represent a main subsystem for majority of processing systems that
More information1. Consider the 1-DOF system described by the equation of motion, 4ẍ+20ẋ+25x = f.
Introduction to Robotics (CS3A) Homework #6 Solution (Winter 7/8). Consider the -DOF system described by the equation of motion, ẍ+ẋ+5x = f. (a) Find the natural frequency ω n and the natural damping ratio
More informationDSCC2012-MOVIC
ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference DSCC2012-MOVIC2012 October 17-19, 2012, Fort Lauderdale, Florida, USA DSCC2012-MOVIC2012-8753
More informationRobust Control of Robot Manipulator by Model Based Disturbance Attenuation
IEEE/ASME Trans. Mechatronics, vol. 8, no. 4, pp. 511-513, Nov./Dec. 2003 obust Control of obot Manipulator by Model Based Disturbance Attenuation Keywords : obot manipulators, MBDA, position control,
More informationAdaptive Robust Tracking Control of Robot Manipulators in the Task-space under Uncertainties
Australian Journal of Basic and Applied Sciences, 3(1): 308-322, 2009 ISSN 1991-8178 Adaptive Robust Tracking Control of Robot Manipulators in the Task-space under Uncertainties M.R.Soltanpour, M.M.Fateh
More informationProgrammable Valves: a Solution to Bypass Deadband Problem of Electro-Hydraulic Systems
Programmable Valves: a Solution to Bypass Deadband Problem of Electro-Hydraulic Systems Song Liu and Bin Yao Abstract The closed-center PDC/servo valves have overlapped spools to prevent internal leakage
More informationSelection of Servomotors and Reducer Units for a 2 DoF PKM
Selection of Servomotors and Reducer Units for a 2 DoF PKM Hermes GIBERTI, Simone CINQUEMANI Mechanical Engineering Department, Politecnico di Milano, Campus Bovisa Sud, via La Masa 34, 20156, Milano,
More informationLecture Schedule Week Date Lecture (M: 2:05p-3:50, 50-N202)
J = x θ τ = J T F 2018 School of Information Technology and Electrical Engineering at the University of Queensland Lecture Schedule Week Date Lecture (M: 2:05p-3:50, 50-N202) 1 23-Jul Introduction + Representing
More informationRobot Dynamics II: Trajectories & Motion
Robot Dynamics II: Trajectories & Motion Are We There Yet? METR 4202: Advanced Control & Robotics Dr Surya Singh Lecture # 5 August 23, 2013 metr4202@itee.uq.edu.au http://itee.uq.edu.au/~metr4202/ 2013
More informationMCE493/593 and EEC492/592 Prosthesis Design and Control
MCE493/593 and EEC492/592 Prosthesis Design and Control Control Systems Part 3 Hanz Richter Department of Mechanical Engineering 2014 1 / 25 Electrical Impedance Electrical impedance: generalization of
More information3 Space curvilinear motion, motion in non-inertial frames
3 Space curvilinear motion, motion in non-inertial frames 3.1 In-class problem A rocket of initial mass m i is fired vertically up from earth and accelerates until its fuel is exhausted. The residual mass
More informationDesign Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain
World Applied Sciences Journal 14 (9): 1306-1312, 2011 ISSN 1818-4952 IDOSI Publications, 2011 Design Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain Samira Soltani
More informationGeneral procedure for formulation of robot dynamics STEP 1 STEP 3. Module 9 : Robot Dynamics & controls
Module 9 : Robot Dynamics & controls Lecture 32 : General procedure for dynamics equation forming and introduction to control Objectives In this course you will learn the following Lagrangian Formulation
More informationMEAM 520. More Velocity Kinematics
MEAM 520 More Velocity Kinematics Katherine J. Kuchenbecker, Ph.D. General Robotics, Automation, Sensing, and Perception Lab (GRASP) MEAM Department, SEAS, University of Pennsylvania Lecture 12: October
More informationAdvanced Robotic Manipulation
Advanced Robotic Manipulation Handout CS37A (Spring 017 Solution Set # Problem 1 - Redundant robot control The goal of this problem is to familiarize you with the control of a robot that is redundant with
More informationOn-line Learning of Robot Arm Impedance Using Neural Networks
On-line Learning of Robot Arm Impedance Using Neural Networks Yoshiyuki Tanaka Graduate School of Engineering, Hiroshima University, Higashi-hiroshima, 739-857, JAPAN Email: ytanaka@bsys.hiroshima-u.ac.jp
More informationArtificial Intelligence & Neuro Cognitive Systems Fakultät für Informatik. Robot Dynamics. Dr.-Ing. John Nassour J.
Artificial Intelligence & Neuro Cognitive Systems Fakultät für Informatik Robot Dynamics Dr.-Ing. John Nassour 25.1.218 J.Nassour 1 Introduction Dynamics concerns the motion of bodies Includes Kinematics
More informationIn this section of notes, we look at the calculation of forces and torques for a manipulator in two settings:
Introduction Up to this point we have considered only the kinematics of a manipulator. That is, only the specification of motion without regard to the forces and torques required to cause motion In this
More informationMotion Control of Passive Haptic Device Using Wires with Servo Brakes
The IEEE/RSJ International Conference on Intelligent Robots and Systems October 8-,, Taipei, Taiwan Motion Control of Passive Haptic Device Using Wires with Servo Brakes Yasuhisa Hirata, Keitaro Suzuki
More informationTrajectory Tracking Control of a Very Flexible Robot Using a Feedback Linearization Controller and a Nonlinear Observer
Trajectory Tracking Control of a Very Flexible Robot Using a Feedback Linearization Controller and a Nonlinear Observer Fatemeh Ansarieshlaghi and Peter Eberhard Institute of Engineering and Computational
More informationGain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control
Gain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control Khaled M. Helal, 2 Mostafa R.A. Atia, 3 Mohamed I. Abu El-Sebah, 2 Mechanical Engineering Department ARAB ACADEMY FOR
More informationAutonomous Helicopter Landing A Nonlinear Output Regulation Perspective
Autonomous Helicopter Landing A Nonlinear Output Regulation Perspective Andrea Serrani Department of Electrical and Computer Engineering Collaborative Center for Control Sciences The Ohio State University
More informationRobot Manipulator Control. Hesheng Wang Dept. of Automation
Robot Manipulator Control Hesheng Wang Dept. of Automation Introduction Industrial robots work based on the teaching/playback scheme Operators teach the task procedure to a robot he robot plays back eecute
More informationFigure 1 Answer: = m
Q1. Figure 1 shows a solid cylindrical steel rod of length =.0 m and diameter D =.0 cm. What will be increase in its length when m = 80 kg block is attached to its bottom end? (Young's modulus of steel
More informationContents. Dynamics and control of mechanical systems. Focus on
Dynamics and control of mechanical systems Date Day 1 (01/08) Day 2 (03/08) Day 3 (05/08) Day 4 (07/08) Day 5 (09/08) Day 6 (11/08) Content Review of the basics of mechanics. Kinematics of rigid bodies
More informationNONLINEAR CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEMS USING THE IMMERSION AND INVARIANCE METHOD
NONLINEAR CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEMS USING THE IMMERSION AND INVARIANCE METHOD Ponesit Santhanapipatkul Watcharapong Khovidhungij Abstract: We present a controller design based on
More informationIntroduction to Robotics
J. Zhang, L. Einig 277 / 307 MIN Faculty Department of Informatics Lecture 8 Jianwei Zhang, Lasse Einig [zhang, einig]@informatik.uni-hamburg.de University of Hamburg Faculty of Mathematics, Informatics
More informationCooperative Control and Mobile Sensor Networks
Cooperative Control and Mobile Sensor Networks Cooperative Control, Part I, A-C Naomi Ehrich Leonard Mechanical and Aerospace Engineering Princeton University and Electrical Systems and Automation University
More informationDistributed Structural Stabilization and Tracking for Formations of Dynamic Multi-Agents
CDC02-REG0736 Distributed Structural Stabilization and Tracking for Formations of Dynamic Multi-Agents Reza Olfati-Saber Richard M Murray California Institute of Technology Control and Dynamical Systems
More informationDifferential Kinematics
Differential Kinematics Relations between motion (velocity) in joint space and motion (linear/angular velocity) in task space (e.g., Cartesian space) Instantaneous velocity mappings can be obtained through
More informationDecentralized PD Control for Non-uniform Motion of a Hamiltonian Hybrid System
International Journal of Automation and Computing 05(2), April 2008, 9-24 DOI: 0.007/s633-008-09-7 Decentralized PD Control for Non-uniform Motion of a Hamiltonian Hybrid System Mingcong Deng, Hongnian
More informationSpacecraft Attitude Control with RWs via LPV Control Theory: Comparison of Two Different Methods in One Framework
Trans. JSASS Aerospace Tech. Japan Vol. 4, No. ists3, pp. Pd_5-Pd_, 6 Spacecraft Attitude Control with RWs via LPV Control Theory: Comparison of Two Different Methods in One Framework y Takahiro SASAKI,),
More informationAdaptive Jacobian Tracking Control of Robots With Uncertainties in Kinematic, Dynamic and Actuator Models
104 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 51, NO. 6, JUNE 006 Adaptive Jacobian Tracking Control of Robots With Uncertainties in Kinematic, Dynamic and Actuator Models C. C. Cheah, C. Liu, and J.
More informationIn most robotic applications the goal is to find a multi-body dynamics description formulated
Chapter 3 Dynamics Mathematical models of a robot s dynamics provide a description of why things move when forces are generated in and applied on the system. They play an important role for both simulation
More informationRobust Control Design for a Wheel Loader Using Mixed Sensitivity H-infinity and Feedback Linearization Based Methods
25 American Control Conference June 8-, 25. Portland, OR, USA FrB2.5 Robust Control Design for a Wheel Loader Using Mixed Sensitivity H-infinity and Feedback Linearization Based Methods Roger Fales and
More informationSCHOOL OF COMPUTING, ENGINEERING AND MATHEMATICS SEMESTER 1 EXAMINATIONS 2012/2013 XE121. ENGINEERING CONCEPTS (Test)
s SCHOOL OF COMPUTING, ENGINEERING AND MATHEMATICS SEMESTER EXAMINATIONS 202/203 XE2 ENGINEERING CONCEPTS (Test) Time allowed: TWO hours Answer: Attempt FOUR questions only, a maximum of TWO questions
More informationMixed Control Moment Gyro and Momentum Wheel Attitude Control Strategies
AAS03-558 Mixed Control Moment Gyro and Momentum Wheel Attitude Control Strategies C. Eugene Skelton II and Christopher D. Hall Department of Aerospace & Ocean Engineering Virginia Polytechnic Institute
More informationBalancing of an Inverted Pendulum with a SCARA Robot
Balancing of an Inverted Pendulum with a SCARA Robot Bernhard Sprenger, Ladislav Kucera, and Safer Mourad Swiss Federal Institute of Technology Zurich (ETHZ Institute of Robotics 89 Zurich, Switzerland
More informationPassivity-Based Control of an Overhead Travelling Crane
Proceedings of the 17th World Congress The International Federation of Automatic Control Passivity-Based Control of an Overhead Travelling Crane Harald Aschemann Chair of Mechatronics University of Rostock
More informationLecture «Robot Dynamics»: Dynamics and Control
Lecture «Robot Dynamics»: Dynamics and Control 151-0851-00 V lecture: CAB G11 Tuesday 10:15 12:00, every week exercise: HG E1.2 Wednesday 8:15 10:00, according to schedule (about every 2nd week) Marco
More informationNeural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot
Vol.3 No., 27 مجلد 3 العدد 27 Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot Abdul-Basset A. AL-Hussein Electrical Engineering Department Basrah
More informationVariable Radius Pulley Design Methodology for Pneumatic Artificial Muscle-based Antagonistic Actuation Systems
211 IEEE/RSJ International Conference on Intelligent Robots and Systems September 25-3, 211. San Francisco, CA, USA Variable Radius Pulley Design Methodology for Pneumatic Artificial Muscle-based Antagonistic
More informationThe basic principle to be used in mechanical systems to derive a mathematical model is Newton s law,
Chapter. DYNAMIC MODELING Understanding the nature of the process to be controlled is a central issue for a control engineer. Thus the engineer must construct a model of the process with whatever information
More informationLecture 9 Nonlinear Control Design
Lecture 9 Nonlinear Control Design Exact-linearization Lyapunov-based design Lab 2 Adaptive control Sliding modes control Literature: [Khalil, ch.s 13, 14.1,14.2] and [Glad-Ljung,ch.17] Course Outline
More informationReal-Time Obstacle Avoidance for trailer-like Systems
Real-Time Obstacle Avoidance for trailer-like Systems T.A. Vidal-Calleja, M. Velasco-Villa,E.Aranda-Bricaire. Departamento de Ingeniería Eléctrica, Sección de Mecatrónica, CINVESTAV-IPN, A.P. 4-74, 7,
More informationLine following of a mobile robot
Line following of a mobile robot May 18, 004 1 In brief... The project is about controlling a differential steering mobile robot so that it follows a specified track. Steering is achieved by setting different
More informationInverse differential kinematics Statics and force transformations
Robotics 1 Inverse differential kinematics Statics and force transformations Prof Alessandro De Luca Robotics 1 1 Inversion of differential kinematics! find the joint velocity vector that realizes a desired
More informationChapter 10. Rotation of a Rigid Object about a Fixed Axis
Chapter 10 Rotation of a Rigid Object about a Fixed Axis Angular Position Axis of rotation is the center of the disc Choose a fixed reference line. Point P is at a fixed distance r from the origin. A small
More informationLoad Prediction-based Energy-efficient Hydraulic Actuation. of a Robotic Arm. 1 Introduction
oad rediction-based Energy-efficient Hydraulic ctuation of a Robotic rm Miss Can Du, rof ndrew lummer and Dr Nigel Johnston fixed displacement pump. This can reduce the weight of plant compared with the
More informationDesign and Control of Variable Stiffness Actuation Systems
Design and Control of Variable Stiffness Actuation Systems Gianluca Palli, Claudio Melchiorri, Giovanni Berselli and Gabriele Vassura DEIS - DIEM - Università di Bologna LAR - Laboratory of Automation
More informationMEM04: Rotary Inverted Pendulum
MEM4: Rotary Inverted Pendulum Interdisciplinary Automatic Controls Laboratory - ME/ECE/CHE 389 April 8, 7 Contents Overview. Configure ELVIS and DC Motor................................ Goals..............................................3
More informationAdvanced Dynamics. - Lecture 4 Lagrange Equations. Paolo Tiso Spring Semester 2017 ETH Zürich
Advanced Dynamics - Lecture 4 Lagrange Equations Paolo Tiso Spring Semester 2017 ETH Zürich LECTURE OBJECTIVES 1. Derive the Lagrange equations of a system of particles; 2. Show that the equation of motion
More informationControl of a Handwriting Robot with DOF-Redundancy based on Feedback in Task-Coordinates
Control of a Handwriting Robot with DOF-Redundancy based on Feedback in Task-Coordinates Hiroe HASHIGUCHI, Suguru ARIMOTO, and Ryuta OZAWA Dept. of Robotics, Ritsumeikan Univ., Kusatsu, Shiga 525-8577,
More informationHARDWARE-IN-THE-LOOP SIMULATION EXPERIMENTS WITH A HYDRAULIC MANIPULATOR MODEL
HARDWARE-IN-THE-LOOP SIMULATION EXPERIMENTS WITH A HYDRAULIC MANIPULATOR MODEL Jorge A. Ferreira, André F. Quintã, Carlos M. Cabral Departament of Mechanical Engineering University of Aveiro, Portugal
More informationA Benchmark Problem for Robust Control of a Multivariable Nonlinear Flexible Manipulator
Proceedings of the 17th World Congress The International Federation of Automatic Control Seoul, Korea, July 6-11, 28 A Benchmark Problem for Robust Control of a Multivariable Nonlinear Flexible Manipulator
More informationNONLINEAR PATH CONTROL FOR A DIFFERENTIAL DRIVE MOBILE ROBOT
NONLINEAR PATH CONTROL FOR A DIFFERENTIAL DRIVE MOBILE ROBOT Plamen PETROV Lubomir DIMITROV Technical University of Sofia Bulgaria Abstract. A nonlinear feedback path controller for a differential drive
More informationA Sliding Mode Controller Using Neural Networks for Robot Manipulator
ESANN'4 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 8-3 April 4, d-side publi., ISBN -9337-4-8, pp. 93-98 A Sliding Mode Controller Using Neural Networks for Robot
More information