A Sliding Mode Controller Using Neural Networks for Robot Manipulator
|
|
- Marilynn Washington
- 6 years ago
- Views:
Transcription
1 ESANN'4 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 8-3 April 4, d-side publi., ISBN , pp A Sliding Mode Controller Using Neural Networks for Robot Manipulator Hajoon Lee, Dongkyung Nam, Cheol Hoon Park Division of Electrical Engineering Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology Daejeon, 35-7, Korea Abstract. This paper proposes a new sliding mode controller using neural networks. Multilayer neural networks with the error back-propagation learning algorithm are used to compensate for the system uncertainty in order to reduce tracking errors and control torques. The stability of the proposed control scheme is proved with the Lyapunov function method. Computer simulation shows that the proposed neuro-controller yields better control performance than the conventional sliding mode controller in the view of tracking errors and overall control torque. Introduction In a robot system there are many uncertainties such as dynamic parameters (eg., inertia and payload conditions), dynamic effects (eg., complex nonlinear frictions), and unmodeled dynamics. Traditional linear controllers have many difficulties in treating these uncertainties. To overcome this problem, sliding mode control has been widely used as one of the precise and robust algorithms,. The most distinguished property of the sliding mode control lies in its robustness. Loosely speaking, when a system is in a sliding mode, it is insensitive to dynamic uncertainties and external disturbances. One of the significant drawbacks of the sliding mode control is that it has a discontinuous switching function, which raises some theoretical as well as practical issues. A theoretical issue is the existence and the uniqueness of solutions and a practical one is the chattering phenomena due to imperfections in switching devices 3. The chattering is generally undesirable because it involves extremely high control activities and may excite high-frequency dynamics neglected in modeling 4. This work has been supported by EESRI(R-3-B-4), which is funded by MO- CIE(Ministry of commerce, industry and energy). newth@nnmi.kaist.ac.kr
2 ESANN'4 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 8-3 April 4, d-side publi., ISBN , pp By this reason, we use a continuous approximation of the discontinuous switching function and thus the so-called boundary layer approach. However, there is a trade-off between the steady-state error and boundary layer thickness 4. In this paper, a sliding mode controller using neural networks is proposed to solve the boundary layer problem. The multilayer neural networks with error back-propagation learning algorithm 5 are used to learn the uncertainties in order to reduce the tracking errors and control torques. It adaptively generates additional input torques to reduce the tracking errors and control torques. The following section describes the design method of the proposed sliding mode controller using neural networks. Simulation results for a two-link planar robot manipulator are shown in Section 3. Conclusion is given in Section 4. Design of a sliding mode controller using neural networks The dynamics of a rigid robot manipulator can be written as follows 6: H(q) q + C(q, q) q + g(q) =τ () where q R n is the joint angle vector, H(q) isthen n manipulator inertia matrix (which is symmetric positive definite), and C(q, q) q R n represents centripetal and Coriolis torques. g(q) R n, τ R n represent gravitational torques, applied joint torques, respectively. Let us define q = q q d as a tracking error vector where q d is a desired joint angle vector. In order to make the system track q(t) q d (t), we define a sliding surface s, s = q + Λ q = q q r () where Λ is a positive definite matrix, and the reference velocity vector q r = q d Λ q 4. Let us define the Lyapunov function as follows: By differentiating (3), we can show V (t) = st Ĥs. (3) V = st Ĥs + s T Ĥṡ (4) = st (Ḣ Ĥ)s + s T (τ C q r g H q r ) s T (H Ĥ)ṡ (5) where the skew symmetry of (Ḣ C) has been used to eliminate the term st Ḣs. The total control input τ as shown in Figure is τ = τ eq + τ + τ nn (6) where τ eq is the equivalent control input, τ nn is the output of the neural network, and the chattering control input τ is as follows: τ = K σ(s/φ) K s (7)
3 ESANN'4 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 8-3 April 4, d-side publi., ISBN , pp where K and K are positive diagonal matrices and σ(s/φ) is defined as follows: { σ(s/φ) = sgn(s) if s Φ, s/φ if s Φ (8) where sgn is the sign function, and Φ is the boundary layer thickness. τ eq is the control input vector which would make V equal if the dynamics were exactly known τ eq = Ĥ q r + Ĉ q r + ĝ. (9) We then have ( V = s T τ + τ nn + H q r + C q r + g + ) Hs + Hṡ () where the modeling errors H, C, and g are H = Ĥ H, C = Ĉ C, and g = ĝ g, respectively. Let us define the total uncertainty u as follows, u = H q r + C q r + g + Hs + Hṡ. () From (4), () and (), as shown in Figure, Ĥs + Ĥṡ = τ + τ nn + u. () We can obtain the cost function of the neural network as follows, E = τ Ĥs Ĥṡ = u τ nn. (3) If the neural network successfully learned the total uncertainty u, the error dynamic equation becomes u + τ nn = ɛ (4) where ɛ denotes a functional reconstruction error. Therefore, the derivative of the Lyapunov function () becomes V = s T (u + τ nn + τ )=s T (ɛ K σ(s/φ) K s) λ min (K ) s + ɛ s s T K σ(s/φ) (5) where λ min (K ) denotes the minimum eigenvalue of K.If s ɛ /λ min (K ), V s T K σ(s/φ). (6) It means that the surface s = ɛ /λ min (K ) will be reached in a finite time. Bounds on s can be directly translated into bounds on the tracking error vector
4 ESANN'4 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 8-3 April 4, d-side publi., ISBN , pp Neural Networks Sliding Mode - + Robot Controller - Figure : The proposed sliding mode controller using neural network. q. By equation (), the tracking error vector q is obtained from s through a first-order lowpass filter 4. From s ɛ /λ min (K )weget ( ɛ q λ min (K )λ min (Λ), q ɛ + λ ) max(λ). (7) λ min (K ) λ min (Λ) If the neural network successfully learned uncertainties so that ɛ, it means that s, and the tracking error vector q and q. Also, τ from (7). 3 Simulation In order to show the performance of the proposed controller, we performed the computer simulation using a two-link planar robot manipulator, whose dynamics can be written explicitly as 6 H H q H H q + C q C ( q + q ) C q q q + g g + g g f f = τ τ (8) where H =(m + m )l + m l +m l l cos(q ), H = H = m l + m l l cos(q ), H = m l, C = m l l sin(q ), g =(m + m )l cos(q )+ m l cos(q +q ), g = m l cos(q +q ), f = c sgn( q )+v q, f = c sgn( q )+ v q,andg is the acceleration of gravity. f and f are nonlinear frictions. The parameter values are selected as c = c =., v =3,v =,l =m, l =.8m, m =kg andm =.8.5kg where the payload m is changed at 5 seconds after simulation begins. The desired trajectories are q d (t) = π 6 ( cos(πt)) π 4 ( cos(πt)) T. The initial states are chosen as q() = T T, q() =. The control parameters used in the proposed method are Φ =., K = K = I, Λ =I where I is an identity matrix. We used the two-layer neural network with eight hidden neurons and q d,q d, q d, q d as the inputs of the neural network. The outputs of the neural
5 ESANN'4 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 8-3 April 4, d-side publi., ISBN , pp The first joint angle rad q q d The first joint angle rad q q d The second joint angle rad.5.5 q q d The second joint angle rad.5.5 q q d (a) (b) Control input τ 4 Control input τ Control input τ Control input τ (c) (d) Figure : Tracking trajectories and control torques. (a) The tracking trajectory of the conventional sliding mode controller. (b) The tracking trajectory of the proposed controller. (c) The control torque of the conventional sliding mode controller. (d) The control torque of the proposed controller. network generate the input torques to compensate for the uncertainties. Initial weights are chosen as small random values bounded by ±.5. The fourth-order Runge-Kutta method with ms sampling time is used to solve the differential equation (8). Figure shows the tracking performances of the conventional sliding mode controller using boundary layer method 4 and the proposed controller, where the dashdot lines show the desired target trajectories and the solid lines represent the controlled results. Table shows the comparative performances of the conventional sliding mode controller and the proposed controller. The tracking performance of the proposed controller is better than that of the conventional controller in tracking errors and overall control torque.
6 ESANN'4 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 8-3 April 4, d-side publi., ISBN , pp Table : Numerical comparisons between the conventional sliding mode controller and the proposed controller. The conventional The proposed sliding mode controller controller First link Second link First link Second link Tracking error* Control torque*,9,99 8,34,843,48 3,43 * This is a sum-squared value. 4 Conclusion In this paper, the sliding mode controller using neural networks is proposed for a robot manipulator. The proposed method used the neural network to compensate for the system uncertainties. According to the simulation results, the neural network, if trained successfully on-line, compensates for the uncertainties, and reduces tracking errors and overall control torque. As a further work, an experiment for more realistic robot manipulator using the proposed controller is under investigation. References J. Y. Hung, W. Gao and J. C. Hung, Variable structure control: a survey, IEEE Transactions on Industrial Electronics, vol. 4, no., pp. -, Feb Minho Lee and Hyeung-Sik Choi, A robust neural controller for underwater robot manipulators, IEEE Transactions on Neural Networks, vol., no. 6, pp , Nov.. 3 H. K. Khalil, Nonlinear systems, 3rd ed., Prentice-Hall,. 4 J. E. Slotine and W. Li, Applied nonlinear control, Pentice-Hall, D. E. Rumelhart and J. L. McClelland, Parallel distributed processing: explorations in the microstructure of cognition, vol., MIT Press, C. Y. Su and T. P. Leung, A sliding mode controller with bound estimation for robot manipulators, IEEE Transactions on Robotics and Automation, vol. 9, no., pp. 8-4, Apr. 993.
Nonlinear 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 informationAdaptive Neuro-Sliding Mode Control of PUMA 560 Robot Manipulator
Journal of Automation, Mobile Robotics & Intelligent Systems VOLUME 1, N 4 216 Adaptive Neuro-Sliding Mode Control of PUMA 56 Robot Manipulator Submitted: 28 th June 216; accepted: 7 th October 216 Ali
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 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 informationNeural Network Sliding-Mode-PID Controller Design for Electrically Driven Robot Manipulators
Neural Network Sliding-Mode-PID Controller Design for Electrically Driven Robot Manipulators S. E. Shafiei 1, M. R. Soltanpour 2 1. Department of Electrical and Robotic Engineering, Shahrood University
More informationThe Design of Sliding Mode Controller with Perturbation Estimator Using Observer-Based Fuzzy Adaptive Network
ransactions on Control, utomation and Systems Engineering Vol. 3, No. 2, June, 2001 117 he Design of Sliding Mode Controller with Perturbation Estimator Using Observer-Based Fuzzy daptive Network Min-Kyu
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 informationObserver Based Output Feedback Tracking Control of Robot Manipulators
1 IEEE International Conference on Control Applications Part of 1 IEEE Multi-Conference on Systems and Control Yokohama, Japan, September 8-1, 1 Observer Based Output Feedback Tracking Control of Robot
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 informationNMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582
NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING NMT EE 589 & UNM ME 482/582 Simplified drive train model of a robot joint Inertia seen by the motor Link k 1 I I D ( q) k mk 2 kk Gk Torque amplification G
More informationCombined NN/RISE-based Asymptotic Tracking Control of a 3 DOF Robot Manipulator
JIAS Journal of Intelligent Automation Systems Published online: 15 January 214 Combined NN/RISE-based Asymptotic Tracking Control of a 3 DOF Robot Manipulator Sara Yazdanzad a, Alireza Khosravi b,*, Abolfazl
More informationExponential Controller for Robot Manipulators
Exponential Controller for Robot Manipulators Fernando Reyes Benemérita Universidad Autónoma de Puebla Grupo de Robótica de la Facultad de Ciencias de la Electrónica Apartado Postal 542, Puebla 7200, México
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 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 informationA Sliding Mode Control based on Nonlinear Disturbance Observer for the Mobile Manipulator
International Core Journal of Engineering Vol.3 No.6 7 ISSN: 44-895 A Sliding Mode Control based on Nonlinear Disturbance Observer for the Mobile Manipulator Yanna Si Information Engineering College Henan
More informationNeural network based robust hybrid control for robotic system: an H approach
Nonlinear Dyn (211) 65:421 431 DOI 117/s1171-1-992-4 ORIGINAL PAPER Neural network based robust hybrid control for robotic system: an H approach Jinzhu Peng Jie Wang Yaonan Wang Received: 22 February 21
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 information458 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 16, NO. 3, MAY 2008
458 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL 16, NO 3, MAY 2008 Brief Papers Adaptive Control for Nonlinearly Parameterized Uncertainties in Robot Manipulators N V Q Hung, Member, IEEE, H D
More informationIntroduction to centralized control
Industrial Robots Control Part 2 Introduction to centralized control Independent joint decentralized control may prove inadequate when the user requires high task velocities structured disturbance torques
More informationDesign of Sliding Mode Control for Nonlinear Uncertain System
Design of Sliding Mode Control for Nonlinear Uncertain System 1 Yogita Pimpale, 2 Dr.B.J.Parvat ME student,instrumentation and Control Engineering,P.R.E.C. Loni,Ahmednagar, Maharashtra,India Associate
More informationA Novel Finite Time Sliding Mode Control for Robotic Manipulators
Preprints of the 19th World Congress The International Federation of Automatic Control Cape Town, South Africa. August 24-29, 214 A Novel Finite Time Sliding Mode Control for Robotic Manipulators Yao ZHAO
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 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 informationRBF Neural Network Adaptive Control for Space Robots without Speed Feedback Signal
Trans. Japan Soc. Aero. Space Sci. Vol. 56, No. 6, pp. 37 3, 3 RBF Neural Network Adaptive Control for Space Robots without Speed Feedback Signal By Wenhui ZHANG, Xiaoping YE and Xiaoming JI Institute
More informationIntroduction to centralized control
ROBOTICS 01PEEQW Basilio Bona DAUIN Politecnico di Torino Control Part 2 Introduction to centralized control Independent joint decentralized control may prove inadequate when the user requires high task
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 informationPERIODIC signals are commonly experienced in industrial
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 15, NO. 2, MARCH 2007 369 Repetitive Learning Control of Nonlinear Continuous-Time Systems Using Quasi-Sliding Mode Xiao-Dong Li, Tommy W. S. Chow,
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 informationDesign On-Line Tunable Gain Artificial Nonlinear Controller
Journal of Computer Engineering 1 (2009) 3-11 Design On-Line Tunable Gain Artificial Nonlinear Controller Farzin Piltan, Nasri Sulaiman, M. H. Marhaban and R. Ramli Department of Electrical and Electronic
More informationAn Adaptive Iterative Learning Control for Robot Manipulator in Task Space
INT J COMPUT COMMUN, ISSN 84-9836 Vol.7 (22), No. 3 (September), pp. 58-529 An Adaptive Iterative Learning Control for Robot Manipulator in Task Space T. Ngo, Y. Wang, T.L. Mai, J. Ge, M.H. Nguyen, S.N.
More informationVideo 8.1 Vijay Kumar. Property of University of Pennsylvania, Vijay Kumar
Video 8.1 Vijay Kumar 1 Definitions State State equations Equilibrium 2 Stability Stable Unstable Neutrally (Critically) Stable 3 Stability Translate the origin to x e x(t) =0 is stable (Lyapunov stable)
More informationADAPTIVE NEURAL NETWORK CONTROL OF MECHATRONICS OBJECTS
acta mechanica et automatica, vol.2 no.4 (28) ADAPIE NEURAL NEWORK CONROL OF MECHARONICS OBJECS Egor NEMSE *, Yuri ZHUKO * * Baltic State echnical University oenmeh, 985, St. Petersburg, Krasnoarmeyskaya,
More informationAn Adaptive LQG Combined With the MRAS Based LFFC for Motion Control Systems
Journal of Automation Control Engineering Vol 3 No 2 April 2015 An Adaptive LQG Combined With the MRAS Based LFFC for Motion Control Systems Nguyen Duy Cuong Nguyen Van Lanh Gia Thi Dinh Electronics Faculty
More informationDynamics. Basilio Bona. Semester 1, DAUIN Politecnico di Torino. B. Bona (DAUIN) Dynamics Semester 1, / 18
Dynamics Basilio Bona DAUIN Politecnico di Torino Semester 1, 2016-17 B. Bona (DAUIN) Dynamics Semester 1, 2016-17 1 / 18 Dynamics Dynamics studies the relations between the 3D space generalized forces
More informationOVER THE past 20 years, the control of mobile robots has
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 18, NO. 5, SEPTEMBER 2010 1199 A Simple Adaptive Control Approach for Trajectory Tracking of Electrically Driven Nonholonomic Mobile Robots Bong Seok
More informationDesign and Stability Analysis of Single-Input Fuzzy Logic Controller
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL. 30, NO. 2, APRIL 2000 303 Design and Stability Analysis of Single-Input Fuzzy Logic Controller Byung-Jae Choi, Seong-Woo Kwak,
More informationTwo-Link Flexible Manipulator Control Using Sliding Mode Control Based Linear Matrix Inequality
IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Two-Link Flexible Manipulator Control Using Sliding Mode Control Based Linear Matrix Inequality To cite this article: Zulfatman
More informationComputing Optimized Nonlinear Sliding Surfaces
Computing Optimized Nonlinear Sliding Surfaces Azad Ghaffari and Mohammad Javad Yazdanpanah Abstract In this paper, we have concentrated on real systems consisting of structural uncertainties and affected
More informationA Model-Free Control System Based on the Sliding Mode Control Method with Applications to Multi-Input-Multi-Output Systems
Proceedings of the 4 th International Conference of Control, Dynamic Systems, and Robotics (CDSR'17) Toronto, Canada August 21 23, 2017 Paper No. 119 DOI: 10.11159/cdsr17.119 A Model-Free Control System
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 informationq HYBRID CONTROL FOR BALANCE 0.5 Position: q (radian) q Time: t (seconds) q1 err (radian)
Hybrid Control for the Pendubot Mingjun Zhang and Tzyh-Jong Tarn Department of Systems Science and Mathematics Washington University in St. Louis, MO, USA mjz@zach.wustl.edu and tarn@wurobot.wustl.edu
More informationTHE robot is one of the choices for improving productivity
320 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 52, NO. 1, FEBRUARY 2005 Multiple Neuro-Adaptive Control of Robot Manipulators Using Visual Cues Choon-Young Lee and Ju-Jang Lee, Senior Member, IEEE
More informationHover Control for Helicopter Using Neural Network-Based Model Reference Adaptive Controller
Vol.13 No.1, 217 مجلد 13 العدد 217 1 Hover Control for Helicopter Using Neural Network-Based Model Reference Adaptive Controller Abdul-Basset A. Al-Hussein Electrical Engineering Department Basrah University
More informationThe Effect of the Static Striebeck Friction in the Robust VS/Sliding Mode Control of a Ball-Beam System
The Effect of the Static Striebeck Friction in the Robust VS/Sliding Mode Control of a -Beam System József K. Tar, János F. Bitó Institute of Intelligent Engineering Systems, Budapest Tech Bécsi út 96/B,
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 informationTracking Control of Robot Manipulators with Bounded Torque Inputs* W.E. Dixon, M.S. de Queiroz, F. Zhang and D.M. Dawson
Robotica (1999) volume 17, pp. 121 129. Printed in the United Kingdom 1999 Cambridge University Press Tracking Control of Robot Manipulators with Bounded Torque Inputs* W.E. Dixon, M.S. de Queiroz, F.
More informationControl of industrial robots. Centralized control
Control of industrial robots Centralized control Prof. Paolo Rocco (paolo.rocco@polimi.it) Politecnico di Milano ipartimento di Elettronica, Informazione e Bioingegneria Introduction Centralized control
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 informationFuzzy Based Robust Controller Design for Robotic Two-Link Manipulator
Abstract Fuzzy Based Robust Controller Design for Robotic Two-Link Manipulator N. Selvaganesan 1 Prabhu Jude Rajendran 2 S.Renganathan 3 1 Department of Instrumentation Engineering, Madras Institute of
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 informationADAPTIVE FORCE AND MOTION CONTROL OF ROBOT MANIPULATORS IN CONSTRAINED MOTION WITH DISTURBANCES
ADAPTIVE FORCE AND MOTION CONTROL OF ROBOT MANIPULATORS IN CONSTRAINED MOTION WITH DISTURBANCES By YUNG-SHENG CHANG A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
More informationA SIMPLE ITERATIVE SCHEME FOR LEARNING GRAVITY COMPENSATION IN ROBOT ARMS
A SIMPLE ITERATIVE SCHEME FOR LEARNING GRAVITY COMPENSATION IN ROBOT ARMS A. DE LUCA, S. PANZIERI Dipartimento di Informatica e Sistemistica Università degli Studi di Roma La Sapienza ABSTRACT The set-point
More informationRobust Control for Robot Manipulators By Using Only Joint Position Measurements
Proceedings of the 29 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 29 Robust Control for Robot Manipulators By Using Only Joint Position Measurements Shafiqul
More informationLecture 9 Nonlinear Control Design. Course Outline. Exact linearization: example [one-link robot] Exact Feedback Linearization
Lecture 9 Nonlinear Control Design Course Outline Eact-linearization Lyapunov-based design Lab Adaptive control Sliding modes control Literature: [Khalil, ch.s 13, 14.1,14.] and [Glad-Ljung,ch.17] Lecture
More informationNeural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems F.L. LEWIS Automation and Robotics Research Institute The University of Texas at Arlington S. JAG ANNATHAN Systems and Controls Research
More informationNonlinear Identification of Backlash in Robot Transmissions
Nonlinear Identification of Backlash in Robot Transmissions G. Hovland, S. Hanssen, S. Moberg, T. Brogårdh, S. Gunnarsson, M. Isaksson ABB Corporate Research, Control Systems Group, Switzerland ABB Automation
More informationResearch Article Periodic and Chaotic Motions of a Two-Bar Linkage with OPCL Controller
Hindawi Publishing Corporation Mathematical Problems in Engineering Volume, Article ID 98639, 5 pages doi:.55//98639 Research Article Periodic and Chaotic Motions of a Two-Bar Linkage with OPCL Controller
More informationMultibody simulation
Multibody simulation Dynamics of a multibody system (Euler-Lagrange formulation) Dimitar Dimitrov Örebro University June 16, 2012 Main points covered Euler-Lagrange formulation manipulator inertia matrix
More informationResearch Article Proportional-Derivative Observer-Based Backstepping Control for an Underwater Manipulator
Mathematical Problems in Engineering Volume 211, Article ID 39792, 18 pages doi:1.1155/211/39792 Research Article Proportional-Derivative Observer-Based Backstepping Control for an Underwater Manipulator
More informationApplication of Neuro Fuzzy Reduced Order Observer in Magnetic Bearing Systems
Application of Neuro Fuzzy Reduced Order Observer in Magnetic Bearing Systems M. A., Eltantawie, Member, IAENG Abstract Adaptive Neuro-Fuzzy Inference System (ANFIS) is used to design fuzzy reduced order
More informationRobust Adaptive Attitude Control of a Spacecraft
Robust Adaptive Attitude Control of a Spacecraft AER1503 Spacecraft Dynamics and Controls II April 24, 2015 Christopher Au Agenda Introduction Model Formulation Controller Designs Simulation Results 2
More informationCHATTERING REDUCTION OF SLIDING MODE CONTROL BY LOW-PASS FILTERING THE CONTROL SIGNAL
Asian Journal of Control, Vol. 12, No. 3, pp. 392 398, May 2010 Published online 25 February 2010 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/asjc.195 CHATTERING REDUCTION OF SLIDING
More informationSensorless Sliding Mode Control of Induction Motor Drives
Sensorless Sliding Mode Control of Induction Motor Drives Kanungo Barada Mohanty Electrical Engineering Department, National Institute of Technology, Rourkela-7698, India E-mail: kbmohanty@nitrkl.ac.in
More informationVirtual Passive Controller for Robot Systems Using Joint Torque Sensors
NASA Technical Memorandum 110316 Virtual Passive Controller for Robot Systems Using Joint Torque Sensors Hal A. Aldridge and Jer-Nan Juang Langley Research Center, Hampton, Virginia January 1997 National
More information3- DOF Scara type Robot Manipulator using Mamdani Based Fuzzy Controller
659 3- DOF Scara type Robot Manipulator using Mamdani Based Fuzzy Controller Nitesh Kumar Jaiswal *, Vijay Kumar ** *(Department of Electronics and Communication Engineering, Indian Institute of Technology,
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 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 informationDelay-Independent Stabilization for Teleoperation with Time Varying Delay
9 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June -, 9 FrC9.3 Delay-Independent Stabilization for Teleoperation with Time Varying Delay Hiroyuki Fujita and Toru Namerikawa
More informationTHE control of systems with uncertain nonlinear dynamics
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 16, NO. 2, MARCH 2008 373 Asymptotic Tracking for Systems With Structured and Unstructured Uncertainties P. M. Patre, Student Member, IEEE, W. MacKunis,
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 informationRobotics & Automation. Lecture 25. Dynamics of Constrained Systems, Dynamic Control. John T. Wen. April 26, 2007
Robotics & Automation Lecture 25 Dynamics of Constrained Systems, Dynamic Control John T. Wen April 26, 2007 Last Time Order N Forward Dynamics (3-sweep algorithm) Factorization perspective: causal-anticausal
More informationI. MAIN NOTATION LIST
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 5, MAY 2008 817 Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation Qing Song, Member, IEEE, James C. Spall,
More informationHeterogeneous mixture-of-experts for fusion of locally valid knowledge-based submodels
ESANN'29 proceedings, European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning. Bruges Belgium), 22-24 April 29, d-side publi., ISBN 2-9337-9-9. Heterogeneous
More informationStable Gaussian Process based Tracking Control of Lagrangian Systems
Stable Gaussian Process based Tracking Control of Lagrangian Systems Thomas Beckers 1, Jonas Umlauft 1, Dana Kulić 2 and Sandra Hirche 1 Abstract High performance tracking control can only be achieved
More informationAdaptive set point control of robotic manipulators with amplitude limited control inputs* E. Zergeroglu, W. Dixon, A. Behal and D.
Robotica (2) volume 18, pp. 171 181. Printed in the United Kingdom 2 Cambridge University Press Adaptive set point control of robotic manipulators with amplitude limited control inputs* E. Zergeroglu,
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 informationA DISCRETE-TIME SLIDING MODE CONTROLLER WITH MODIFIED FUNCTION FOR LINEAR TIME- VARYING SYSTEMS
http:// A DISCRETE-TIME SLIDING MODE CONTROLLER WITH MODIFIED FUNCTION FOR LINEAR TIME- VARYING SYSTEMS Deelendra Pratap Singh 1, Anil Sharma 2, Shalabh Agarwal 3 1,2 Department of Electronics & Communication
More informationMechanical Engineering Department - University of São Paulo at São Carlos, São Carlos, SP, , Brazil
MIXED MODEL BASED/FUZZY ADAPTIVE ROBUST CONTROLLER WITH H CRITERION APPLIED TO FREE-FLOATING SPACE MANIPULATORS Tatiana FPAT Pazelli, Roberto S Inoue, Adriano AG Siqueira, Marco H Terra Electrical Engineering
More informationRobust Tracking Under Nonlinear Friction Using Time-Delay Control With Internal Model
1406 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 17, NO. 6, NOVEMBER 2009 Robust Tracking Under Nonlinear Friction Using Time-Delay Control With Internal Model Gun Rae Cho, Student Member, IEEE,
More informationENGG 5402 Course Project: Simulation of PUMA 560 Manipulator
ENGG 542 Course Project: Simulation of PUMA 56 Manipulator ZHENG Fan, 115551778 mrzhengfan@gmail.com April 5, 215. Preface This project is to derive programs for simulation of inverse dynamics and control
More informationNeural Networks for Advanced Control of Robot Manipulators
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 13, NO. 2, MARCH 2002 343 Neural Networks for Advanced Control of Robot Manipulators H. Daniel Patiño, Member, IEEE, Ricardo Carelli, Senior Member, IEEE, and
More informationRigid Manipulator Control
Rigid Manipulator Control The control problem consists in the design of control algorithms for the robot motors, such that the TCP motion follows a specified task in the cartesian space Two types of task
More informationObserver Based Friction Cancellation in Mechanical Systems
2014 14th International Conference on Control, Automation and Systems (ICCAS 2014) Oct. 22 25, 2014 in KINTEX, Gyeonggi-do, Korea Observer Based Friction Cancellation in Mechanical Systems Caner Odabaş
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 informationOptimal Control of Uncertain Nonlinear Systems
Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, 28 Optimal Control of Uncertain Nonlinear Systems using RISE Feedback K.Dupree,P.M.Patre,Z.D.Wilcox,andW.E.Dixon
More informationFAULT-TOLERANT SYSTEM BASED ON OUTPUT FEEDBACK H MARKOVIAN CONTROL FOR MANIPULATOR ROBOTS. Adriano A. G. Siqueira, Cleber Buosi, Marco H.
FAULT-TOLERANT SYSTEM BASED ON OUTPUT FEEDBACK H MARKOVIAN CONTROL FOR MANIPULATOR ROBOTS Adriano A. G. Siqueira Cleber Buosi Marco H. Terra Electrical Engineering Department University of São Paulo at
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 informationPassivity-based Control for 2DOF Robot Manipulators with Antagonistic Bi-articular Muscles
Passivity-based Control for 2DOF Robot Manipulators with Antagonistic Bi-articular Muscles Hiroyuki Kawai, Toshiyuki Murao, Ryuichi Sato and Masayuki Fujita Abstract This paper investigates a passivity-based
More informationLinköping University Electronic Press
Linköping University Electronic Press Report Simulation Model of a 2 Degrees of Freedom Industrial Manipulator Patrik Axelsson Series: LiTH-ISY-R, ISSN 400-3902, No. 3020 ISRN: LiTH-ISY-R-3020 Available
More informationControl of constrained spatial three-link flexible manipulators
Control of constrained spatial three-link flexible manipulators Sinan Kilicaslan, M. Kemal Ozgoren and S. Kemal Ider Gazi University/Mechanical Engineering Department, Ankara, Turkey Middle East Technical
More informationPrecision position control of servo systems using adaptive back-stepping and recurrent fuzzy neural networks
ournal of Mechanical Science and Technology (9) 59~7 ournal of Mechanical Science and Technology www.springerlink.com/content/78-9x DOI.7/s6-9-97- Precision position control of servo systems using adaptive
More informationAn adaptive sliding mode control scheme for induction motor drives
An adaptive sliding mode control scheme for induction motor drives Oscar Barambones, Patxi Alkorta, Aitor J. Garrido, I. Garrido and F.J. Maseda ABSTRACT An adaptive sliding-mode control system, which
More informationResearch Article Extended and Unscented Kalman Filtering Applied to a Flexible-Joint Robot with Jerk Estimation
Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 21, Article ID 482972, 14 pages doi:1.1155/21/482972 Research Article Extended and Unscented Kalman Filtering Applied to a
More informationResearch Article RBF Neural Network of Sliding Mode Control for Time-Varying 2-DOF Parallel Manipulator System
Mathematical Problems in Engineering Volume, Article ID 7, pages http://dx.doi.org/.//7 Research Article RBF Neural Network of Sliding Mode Control for Time-Varying -DOF Parallel Manipulator System Haizhong
More informationA Logarithmic Neural Network Architecture for Unbounded Non-Linear Function Approximation
1 Introduction A Logarithmic Neural Network Architecture for Unbounded Non-Linear Function Approximation J Wesley Hines Nuclear Engineering Department The University of Tennessee Knoxville, Tennessee,
More informationM. De La Sen, A. Almansa and J. C. Soto Instituto de Investigación y Desarrollo de Procesos, Leioa ( Bizkaia). Aptdo. 644 de Bilbao, Spain
American Journal of Applied Sciences 4 (6): 346-353, 007 ISSN 546-939 007 Science Publications Adaptive Control of Robotic Manipulators with Improvement of the ransient Behavior hrough an Intelligent Supervision
More informationTracking Control of an Ultrasonic Linear Motor Actuated Stage Using a Sliding-mode Controller with Friction Compensation
Vol. 3, No., pp. 3-39() http://dx.doi.org/.693/smartsci.. Tracking Control of an Ultrasonic Linear Motor Actuated Stage Using a Sliding-mode Controller with Friction Compensation Chih-Jer Lin,*, Ming-Jia
More informationDynamic Tracking Control of Uncertain Nonholonomic Mobile Robots
Dynamic Tracking Control of Uncertain Nonholonomic Mobile Robots Wenjie Dong and Yi Guo Department of Electrical and Computer Engineering University of Central Florida Orlando FL 3816 USA Abstract We consider
More informationAn application of the temporal difference algorithm to the truck backer-upper problem
ESANN 214 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 23-25 April 214, i6doc.com publ., ISBN 978-28741995-7. Available
More informationA dap tive rob ust it e rative lea r ni ng cont rol f or u nce rt ai n robotic syst e ms
2 5 23 1 Control heory & Applications Vol. 2 No. 5 Oct. 23 Article ID : 1-8152 (23) 5-77 - 6 A dap tive rob ust it e rative lea r ni ng cont rol f or u nce rt ai n robotic syst e ms YANG Sheng- yue, LUO
More information