Enhancing precision performance of trajectory tracking controller for robot manipulators using RBFNN and adaptive bound

In this paper the design issues of trajectory tracking controller for robot manipulators are considered. The performance of classical model based controllers is reduced due to the presence of inherently existing uncertainties in the dynamic model of the robot manipulator. An intermediate approach between model based controllers and neural network based controllers is adopted to enhance the precision of trajectory tracking. The performance of the model based controller is enhanced by adding an RBF neural network and an adaptive bound part. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive bound part is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable. Finally, the numerical simulation results are produced with various controllers and the effectiveness of the proposed controller is shown in a comparative study for the case of a Microbot type robot Manipulator.

[1]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[2]  Manfred Morari,et al.  Local Training for Radial Basis Function Networks: Towards Solving the Hidden Unit Problem , 1991, 1991 American Control Conference.

[3]  Pedro Albertos,et al.  Sliding mode speed auto-regulation technique for robotic tracking , 2011, Robotics Auton. Syst..

[4]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[5]  Warren E. Dixon,et al.  Composite adaptation for neural network-based controllers , 2010, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[6]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[7]  Zhao-Hui Jiang,et al.  A Neural Network Controller for Trajectory Control of Industrial Robot Manipulators , 2008, J. Comput..

[8]  Vikas Panwar,et al.  Asymptotic trajectory tracking for a robot manipulator using RBF neural network and adaptive bound on disturbances , 2010, 2010 International Conference on Mechanical and Electrical Technology.

[9]  Roberto Horowitz,et al.  Repetitive and adaptive control of robot manipulators with velocity estimation , 1997, IEEE Trans. Robotics Autom..

[10]  Recep Burkan,et al.  Upper bounding estimation for robustness to the parameter uncertainty in trajectory control of robot arm , 2003, Robotics Auton. Syst..

[11]  Chien Chern Cheah,et al.  Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models , 2006, IEEE Transactions on Automatic Control.

[12]  Tore Hägglund,et al.  Automatic tuning of simple regulators with specifications on phase and amplitude margins , 1984, Autom..

[13]  Hyeung-Sik Choi,et al.  Robust control of robot manipulators with torque saturation using fuzzy logic , 2001, Robotica.

[14]  Zoe Doulgeri,et al.  Model-free robot joint position regulation and tracking with prescribed performance guarantees , 2012, Robotics Auton. Syst..

[15]  Abdelhak Bennia,et al.  Neural Networks-Based Adaptive State Feedback Control of Robot Manipulators , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

[16]  Dong Sun,et al.  Design of an enhanced nonlinear PID controller , 2005 .

[17]  George N. Saridis,et al.  L-Q design of PID controllers for robot arms , 1985, IEEE J. Robotics Autom..

[18]  Gang Feng,et al.  Robot tracking in task space using neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[19]  William A. Wolovich,et al.  Robotics - basic analysis and design , 1987, HRW Series in electrical and computer engineering.

[20]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[21]  Dimitri Mahayana,et al.  Robust adaptive control for robotic manipulator based on chattering free variable structure system , 2009, 2009 International Conference on Electrical Engineering and Informatics.

[22]  Mark W. Spong,et al.  Robust linear compensator design for nonlinear robotic control , 1985, IEEE J. Robotics Autom..

[23]  Jean-Jacques E. Slotine,et al.  The Robust Control of Robot Manipulators , 1985 .

[24]  Frank L. Lewis,et al.  Neural network feedforward control for mechanical systems with external disturbances , 2007, 2007 46th IEEE Conference on Decision and Control.