DYNAMIC MODEL FOR AN ARTICULATED MANIPULATOR. Luis Arturo Soriano, Jose de Jesus Rubio, Salvador Rodriguez and Cesar Torres

Similar documents
Fuzzy Based Robust Controller Design for Robotic Two-Link Manipulator

Introduction to Robotics

Exponential Controller for Robot Manipulators

Case Study: The Pelican Prototype Robot

Dynamics. Basilio Bona. Semester 1, DAUIN Politecnico di Torino. B. Bona (DAUIN) Dynamics Semester 1, / 18

OBSERVER DESIGN BASED IN THE MATHEMATICAL MODEL OF A WIND TURBINE

Lecture Note 12: Dynamics of Open Chains: Lagrangian Formulation

Artificial Intelligence & Neuro Cognitive Systems Fakultät für Informatik. Robot Dynamics. Dr.-Ing. John Nassour J.

In this section of notes, we look at the calculation of forces and torques for a manipulator in two settings:

(W: 12:05-1:50, 50-N202)

Lecture Schedule Week Date Lecture (M: 2:05p-3:50, 50-N202)

Robot Dynamics II: Trajectories & Motion

Trigonometric Saturated Controller for Robot Manipulators

Robust Control of Robot Manipulator by Model Based Disturbance Attenuation

112 Dynamics. Example 5-3

DYNAMICS OF SERIAL ROBOTIC MANIPULATORS

Dynamics. describe the relationship between the joint actuator torques and the motion of the structure important role for

The Dynamics of Fixed Base and Free-Floating Robotic Manipulator

q 1 F m d p q 2 Figure 1: An automated crane with the relevant kinematic and dynamic definitions.

Chapter 5. . Dynamics. 5.1 Introduction

Lecture Note 12: Dynamics of Open Chains: Lagrangian Formulation

Robotics. Dynamics. Marc Toussaint U Stuttgart

Dynamics of Open Chains

Adaptive Robust Tracking Control of Robot Manipulators in the Task-space under Uncertainties

Control of the Inertia Wheel Pendulum by Bounded Torques

Design Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain

Multibody simulation

Trajectory-tracking control of a planar 3-RRR parallel manipulator

Observer Based Output Feedback Tracking Control of Robot Manipulators

Gain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control

A Sliding Mode Controller Using Neural Networks for Robot Manipulator

DYNAMICS OF PARALLEL MANIPULATOR

Robotics. Dynamics. University of Stuttgart Winter 2018/19

Rigid Manipulator Control

Advanced Robotic Manipulation

Video 1.1 Vijay Kumar and Ani Hsieh

Robotics & Automation. Lecture 25. Dynamics of Constrained Systems, Dynamic Control. John T. Wen. April 26, 2007

Multibody simulation

Dynamics. 1 Copyright c 2015 Roderic Grupen

New Tuning Conditions for a Class of Nonlinear PID Global Regulators of Robot Manipulators.

We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors

Emulation of an Animal Limb with Two Degrees of Freedom using HIL

Dynamic Model of Space Robot Manipulator

In most robotic applications the goal is to find a multi-body dynamics description formulated

Tensor Analysis in Euclidean Space

Lecture «Robot Dynamics»: Dynamics and Control

STOCHASTIC MODELING AND TRACKING CONTROL FOR A TWO-LINK PLANAR RIGID ROBOT MANIPULATOR. Received February 2012; revised June 2012

Rotational & Rigid-Body Mechanics. Lectures 3+4

Iterative Feedback Tuning for the joint controllers of a 7-DOF Whole Arm Manipulator

PARTIAL DERIVATIVE OF MATRIX FUNCTIONS WITH RESPECT TO A VECTOR VARIABLE

Differential Kinematics

CONTROL OF ROBOT CAMERA SYSTEM WITH ACTUATOR S DYNAMICS TO TRACK MOVING OBJECT

Advanced Robotic Manipulation

Robot Control Basics CS 685

1/30. Rigid Body Rotations. Dave Frank

Modelling and Simulation of a Wheeled Mobile Robot in Configuration Classical Tricycle

ROBOTICS Laboratory Problem 02

Trajectory Planning from Multibody System Dynamics

Dynamics modeling of an electro-hydraulically actuated system

Nonholonomic Constraints Examples

Computational and mathematical modeling of an industrialautomobile robot: a multi-purpose case of study

Matrix Differentiation

Generalized Forces. Hamilton Principle. Lagrange s Equations

3- DOF Scara type Robot Manipulator using Mamdani Based Fuzzy Controller

M. De La Sen, A. Almansa and J. C. Soto Instituto de Investigación y Desarrollo de Procesos, Leioa ( Bizkaia). Aptdo. 644 de Bilbao, Spain

Lecture «Robot Dynamics»: Dynamics 2

Passivity-based Control for 2DOF Robot Manipulators with Antagonistic Bi-articular Muscles

Virtual Passive Controller for Robot Systems Using Joint Torque Sensors

Tracking Control of Robot Manipulators with Bounded Torque Inputs* W.E. Dixon, M.S. de Queiroz, F. Zhang and D.M. Dawson

Control of a biped robot by total rate of angular momentum using the task function approach

Experimental Evaluation of a Saturated Output Feedback Controller Using RBF Neural Networks for SCARA Robot IBM 7547

Linköping University Electronic Press

Open-loop Control for 2DOF Robot Manipulators with Antagonistic Bi-articular Muscles

DIFFERENTIAL KINEMATICS. Geometric Jacobian. Analytical Jacobian. Kinematic singularities. Kinematic redundancy. Inverse differential kinematics

ADAPTIVE NEURAL NETWORK CONTROL OF MECHATRONICS OBJECTS

ROBOTICS 01PEEQW. Basilio Bona DAUIN Politecnico di Torino

Nonlinear PD Controllers with Gravity Compensation for Robot Manipulators

Model Reference Adaptive Control of Underwater Robotic Vehicle in Plane Motion

An Adaptive Full-State Feedback Controller for Bilateral Telerobotic Systems

Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot

Research Article A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

Mechanical Engineering Department - University of São Paulo at São Carlos, São Carlos, SP, , Brazil

ENGG 5402 Course Project: Simulation of PUMA 560 Manipulator

GAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL

Manipulator Dynamics 2. Instructor: Jacob Rosen Advanced Robotic - MAE 263D - Department of Mechanical & Aerospace Engineering - UCLA

MSMS Matlab Problem 02

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582

Position and orientation of rigid bodies

EN Nonlinear Control and Planning in Robotics Lecture 2: System Models January 28, 2015

PID Controllers for Robots Equipped with Brushed DC-Motors Revisited

The Design of Sliding Mode Controller with Perturbation Estimator Using Observer-Based Fuzzy Adaptive Network

EML5311 Lyapunov Stability & Robust Control Design

Robust Control of Cooperative Underactuated Manipulators

Explicit Lagrangian Formulation of the Dynamic Regressors for Serial Manipulators

Adaptive set point control of robotic manipulators with amplitude limited control inputs* E. Zergeroglu, W. Dixon, A. Behal and D.

8 Velocity Kinematics

Lecture 9 Nonlinear Control Design

Robotics I. Classroom Test November 21, 2014

Circular Motion, Pt 2: Angular Dynamics. Mr. Velazquez AP/Honors Physics

MEM04: Rotary Inverted Pendulum

Transcription:

ICIC Express Letters Part B: Applications ICIC International c 011 ISSN 185-766 Volume, Number, April 011 pp 415 40 DYNAMIC MODEL FOR AN ARTICULATED MANIPULATOR Luis Arturo Soriano, Jose de Jesus Rubio, Salvador Rodriguez and Cesar Torres Seccion de Estudios de Posgrado e Investigacion ESIME Azcapotzalco Instituto Politecnico Nacional Av de las Granjas no 68, Col Santa Catarina, CP 050, Mexico DF, Mexico larturosoriano@gmailcom; jrubioa@ipnmx Received July 010; accepted October 010 Abstract The mathematical models of robotic arms describe the relationship between force or torque and motion The equations of motion are important to consider in the design of robotic arms, in simulation and animation of robotic arm motion and in the design of control algorithms avoiding the necessity to build a prototype of a real robotic arm The major contribution of this paper is to present an interesting method to obtain the dynamics of an articulated robotic arm Keywords: Dynamic model, Articulated manipulator 1 Introduction Now, the presition in the homeworks is required in robotics [] which is applied in the manufacture and the education [1] and in other repetitive homeworks made by humans in the past [] These homeworks are frequently with a defined trajectory [5], that is way it is an open research in robotics [4, 5, 10, 13] which present interesting results The homeworks in the education [1] and in the medicine [] are improved using a dynamic model [4, 5, 10, 13] The mathematical model of robotic arms describes the relationship between force or torque and motion There are some books that present dynamic models as are [3, 8, 9, 11, 14], the method of [6] is interesting and is different to the others, however, in [6] they do not apply their method to an articulated robotic arm, in this paper, this method is applied to an articulated robotic arm In this paper, the method of [6] is applied to obtain the dynamics of an articulated robotic arm Dynamic Model The method is based on the Lagrangian L = K P (1 where K is the total kinetic energy of the system and P is the total potential energy of the system θ i is considered for the rotatory joints and d i for the prismatic joints and τ i for the applied force moment of the joint i The equation of Lagrange-Euler is the following: ( τ i = d L dt θ i L θ i ( To get to the form (, we needed to make use of the homogeneous transformation matrix 1 Velocity of the joints arm The formulation of Lagrange-Euler knowledge needs the kinetic energy of the physical system, which in turn requires a knowledge of the velocity of every joint 415

416 L A SORIANO, J DE JESUS RUBIO, S RODRIGUEZ AND C TORRES i r i is one fixed and point at rest in the element i and expressed in homogeneous coordinates regarding the element s coordinate system i i r i = [ x i y i z i 1 ] T This coordinates of point i with respect to the system {i} The aforementioned point (any point in the link i is motionless from the system {i}, but not as i With reference to {0} the point is: 0 r i = 0 A i i r i (4 To obtain the derivative 0 r i with the time, the velocity of each link is obtained with respect base coordinates system: 0 v i = v i = d dt ( 0 r i = d dt (0 A i i r i = ( j=1 0 A i qj (3 i r i (5 The form compact of the Equation (5 is obtained because iṙ i = 0 The partial derivative of 0 A i with can be obtained easily with the help of a matrix D i, for the case of the revolution articulation is defined as: D i = 0 1 0 1 1 0 0 1 (6 0 1 0 1 0 Therefore, i 1 A i = D i i 1 A i = We will have then for i = 1,,, n and j i: 0 1 0 1 1 0 0 1 0 1 0 1 0 i 1 A i (7 0 A i = 0 A 1 1 A j A j 1 D j j 1 A j i 1 A i (8 And for j i, the derivative will be equal to zero This equation reflects the effect of the motion of the joint j in the link i If a link j > i (eg, farther from the base than the link i in the chain of links the effect in i will be zero, because i it will not be moved due to the motion of j For simplify the notation let s define B ij = 0 A i of mode that for j i: B ij = 0 A j 1 D j 1 j A i (9 And for j > i, B ij = 0 Using the Equation (5 ( v i = B i ij qj r i (10 B ij q k = B ijk = j=1 For calculating the kinetics energy of system, also it is needded the partial derivative of B ij with respect to k : 0 A j 1 D j 1 j A k 1 D k 1 k A i i k j 0 A k 1 D k 1 k A j 1 D j 1 j A i i j k 0 i < j or i < k And for i < j or i < k the partial derivative is equal to zero This relations are because of the movements of the links j and k over the link i From the Equation (10, the velocities of the 3 links of manipulator are obtained as: v 1 = (B 11 q1 1 r 1, v = (B 11 q1 + B 1 q + B 1 q1 + B q r (11 v 3 = (B 11 q1 + B 1 q + B 13 q3 + B 1 q1 + B q + B 3 q3 + B 31 q1 + B 3 q + B 33 q3 3 r 3 (1

ICIC EXPRESS LETTERS, PART B: APPLICATIONS, VOL, NO, 011 417 Kinetic energy From the second law of Newton, the kinetic energy of a particle in movement give for e = 1 mv In this case, if K i is the kinetic energy of link i and dk i the one particle with mass dm in this link, dk i = 1 (ẋ i + ẏi + żi 1 dm = tr ( v i vi T dm (13 where tra = n a ii in others words is the sum of the diagonal elements of the matrix A, on the other hand using (10 in (13: ( dk i = 1 T ( tr B i il ql r i B i ir qr r i dm = 1 tr B i il r i dm i ri T B T irq l qr (14 B ij represents the change reason of the points i r i (in the link i related with the reference coordinate because of the change of It means that this matrix is the same for all the points in the link i (that is, it is constant in all the points and it is independent to dm At the same way the velocity dq i is the same in all the points of the link i, then to dt obtain the total kinetic energy K i of the link i by solving the integral of dk i, we can let the factors in (14 out of the integral and solving it inside of the double add ( K i = dk i = 1 ( tr B i il r i i ri T dm B T irq l qr (15 For i r i i ri T dm of the Equation (15, it is have that i r i = [ x i y i z i 1 ] T of (3 then the product of i r i i ri T is equal to: x i i r i i ri T = y i [ z i xi y i z i 1 ] x i x i y i x i z i x i = x i y i yi y i z i y i x i z i y i z i zi z i (16 1 x i y i z i 1 Each one of the elements of (16 is multiplied by the scalar dm and using the rules of integral of matrices, each element of the matrix may be solved as an integral x i dm x i y i dm x i z i dm x i dm J i = xi y i dm yi dm y i z i dm y i dm xi z i dm y i z i dm zi dm z i dm xi dm y i dm z i dm (17 dm where J i is depended of i r i Therefore, it is independent of the motion of the links and it is necessary to calculated it once And pseudo-inertia of the link is called i With (17 pseudo-inertias matrix for each link is calculated For the link 1: [ ] 03 3 0 J 1 = 1 3 (18 0 3 1 m 1 where the matrix J 1 is considered only the element J 44 because is suppose than every mass this concentrated at the source {1}, and is calculated from now on: dm = m1 For the second link is: 1 3 J = L m 0 1 m L 0 1 0 0 1 (19 m L 0 1 m where, x dm = m L 0 L x dx = m L x 3 3 0 L= m L 3 L 3 = m L 3, dm = m

418 L A SORIANO, J DE JESUS RUBIO, S RODRIGUEZ AND C TORRES Similarly form for the link 3: 1 3 J 3 = L m 3 0 1 m 3L 0 1 0 0 1 (0 m 3L 0 1 m 3 Now, the total kinetic energy of arm K is: ( K i = K i = 1 tr Developing the Equation (1, we have: K = n Ki = 1 [( Bil J i Bir T ql ] qr (1 tr ( B il J i Bir T ql qr ( For n = 1,, 3, l = 1,, 3 and r = 1,, 3: tr ( B 11 J 1 B T 11 θ1 θ1 + tr ( B 1 J B T 1 θ1 θ1 + tr ( B 1 J B T θ1 θ K = 1 + tr ( B J B T 1 θ θ1 + tr ( B J B T θ θ + tr ( B 31 J 3 B T 31 θ1 θ1 + tr ( B 31 J 3 B3 T θ1 θ + tr ( B 31 J 3 B33 T θ1 θ3 + tr ( B 3 J 3 B31 T θ θ1 + tr ( B 3 J 3 B3 T θ θ + tr ( B 3 J 3 B33 T θ θ3 + tr ( B 33 J 3 B31 T θ3 θ1 + tr ( B 33 J 3 B T 3 θ3 θ + tr ( B 3 J 3 B T 33 θ3 θ3 3 Potential energy The potential energy of joint is P i and the total manipulator P we have: P i = m i g 0 r i = m i g ( 0 A i i r i (4 In this equation, g is the vector of gravity, g = [ g x g y g z 0 ] is the system of the base The specific value of the vector g depends of how is the contact of the robot If the base is over the floor and the axe Z 0 is normal to it, g = [ 0 0 981 0 ] The total potential energy is the sum of all the energy of each link: P = P i = m i g ( 0 A i i r i (5 The potential energy of arm for n = 3: P = m 1 g ( 0 A 1 1 r 1 m g ( 0 A r m3 g ( 0 A 3 3 r 3 4 The lagrangian and the dynamics model Now, they have the necessary data to obtain the Lagrangian, L, and the equation of Euler-Lagrange Replacing ( and (5 in (1 gives: L = 1 tr ( B il J i Bir T ql qr + m i g ( 0 A i i r i Remember that l > i, B il = 0, of mode that the Lagrangian is simplified considerably Now, using (7 in ( and remembering the definitions of the B The derivation of L L whit respect to q x is: tr ( B ix J i Bik T qx If B il = 0 for l > i, and the q x = 1 l=1k=1 derivative with respect to t, we have d dt ( L q x = n l=ik=1 l tr ( B ik J l Bli T (3 (6 (7 q k The factor 1

ICIC EXPRESS LETTERS, PART B: APPLICATIONS, VOL, NO, 011 419 disappear since it has to derive partially with respect to i and to k By means of similar considerations for L q x gives the expression: { l τ i = tr ( B ip J l Bli T l l q p + tr ( } B ipq J l Bli T qp q q m l gb l li r l (8 l=i p=1 p=i q=1 For the arm of tree degree of freedom 3 equations are required for the moments, this equations can denoted in matrix form and in form more compact using the next few definitions: τ(t is the vector of 3 1 the moments applied in the joints q(t is the vector of 3 1 the variables of link, q(t, q(t, fist and second derivative of the q(t with respect to time M(q is the symmetric matrix of 3 3 that depends of the inertias and with elements gives: M ik = j=max(i,k tr ( B jk J j Bji T i, k = 1,, 3,, n (9 C(q, q is the 3 1 no lineal vector that depends of the Coriolis and Centrifuge force, we have: C i = c ikm q k q m i = 1,, 3,, n (30 where, c ikm = j=max(1,k,m k=1m=1 tr ( B jkm J j Bji T i, k, m = 1,, 3,, n (31 G(q is the 3 1 vector that is obtained by severely exited force on the robot Each of the elements is given as: G i = m j g B j ji r j i = 1,, 3,, n (3 j=i using this definitions in (8 the equation τ i gives τ i = n developing the Equation (9 gives M ik k=1 q k + c i ( q k q m + G i (q i or: τ i = M(q q(t + C(q, q + G(q (33 M(θ = [M ij ] R 3 3, i = 1, 3, j = 1, 3 (34 substituting values gives M 11 = 1 6 L (m +4m 3 +3m 3 cos θ 3 +3m 3 cos (θ + θ 3 +m cos θ +3m 3 cos θ + m 3 cos (θ + θ 3, M 1 = M 13 = M 1 = M 31 = 0, M = 1 3 L (m + 4m 3 +3m 3 cos θ 3, M 3 = M 3 = 1 6 L m 3 (3 cos θ 3 +, M 33 = 1 3 L m 3 On the other hand develop the Equation (30 we have: C(θ, θ = [ C 1 C C 3 ] T (35 where C = 1 6 L (6m 3 sin (θ + θ 3 + m sin θ + 6m 3 sin θ + m 3 sin (θ + θ 3 θ 1 θ, C 1 = 0 and C 3 = 1 3 L m 3 (3 sin θ + sin (θ + θ 3 + 3 sin (θ + θ 3 θ 1 θ 1 48 L m 3 (16 sin (θ + θ 3 + 4 sin θ 3 + 4 sin (θ + θ 3 θ 1 θ3 Finally, developing the Equation (3 we have: G(θ = [ G 1 G G 3 ] T (36 where G 1 = 0, G = 1 Lg(m 3 cos (θ + θ 3 + m cos θ + m 3 cos θ and G 3 = 1 Lgm 3 cos (θ + θ 3

40 L A SORIANO, J DE JESUS RUBIO, S RODRIGUEZ AND C TORRES 3 Conclusion In this paper, we obtain the equations for the robot s dynamic Model articulated through the formulation of [6], the equations are very useful for understanding the functioning of the robot and can be compared with existing ones Acknowledgment The authors thank the Secretaria de Investigación y Posgrado and the Comisión de Operación y Fomento de Actividades Académicas del IPN and the Consejo Nacional de Ciencia y Tecnologia for their help in this research REFERENCES [1] G Capi, Application of recurrent neural controllers for robot complex task performance, International Journal of Innovative Computing, Information and Control, vol5, no5, pp1171-1178, 009 [] M-K Chang and T-H Yuan, Experimental implementations of adaptive self-organizing fuzzy sliding mode control to 3-DOF rehabilitation robot, International Journal of Innovative Computing, Information and Control, vol5, no10(b, pp3391-3404, 009 [3] J J Craig, Robótics, Prentice Hall [4] Y Dai, M Konishi and J Imai, Rnn-based cooperative motion control of -DOF robot arms, International Journal of Innovative Computing, Information and Control, vol3, no4, pp937-95, 007 [5] M M Fateh and M R Soltanpour, Robust task-space control of robot manipulators under imperfect transformation of control space, International Journal of Innovative Computing, Information and Control, vol5, no11(a, pp3949-3960, 009 [6] K S Fu, R C González and C S G Lee, Robotics: Control, Detection, Vision and Intelligence, McGraw-Hill [7] P Groover, Robótica Industrial, McGraw-Hill, Madrid, 1989 [8] R Kelly and V Santibañez, Movement Control Manipulator Robots, Prentice Hall, 003 [9] F L Lewis, D M Dawson and C T Abdallah, Robot Manipulator Control Theory and Practice, nd Edition, Marcel Dekker [10] K Najim, E Ikonen and E Gomez-Ramirez, Trejectory tracking control based on a general geological decision tree controller for robot manipulators, International Journal of Innovative Computing, Information and Control, vol4, no1, pp53-6, 008 [11] R M Murray, Z Li and S S Sastry, A Mathematic Robot Modeling and Control [1] R Radharamanan and H E Jenkins, Laboratory learning modules on CAD/CAM and robotics engineering education, International Journal of Innovative Computing, Information and Control, vol4, no, pp433-443, 008 [13] J J Rubio and L A Soriano, An asymptotic stable proportional derivative control with sliding mode compensation and with a high gain observer for robotic arms, International Journal of Innovative Computing, Information and Control, vol6, no10, pp4513-456, 010 [14] M W Spong and M Vidyasagar, Robot Dynamics and Control, John Wiley & Sons