Reinforcement learning control for a robotic manipulator with unknown deadzone

In this paper, an actor critic neural network control is developed for a robotic manipulator. Both system uncertainties and unknown deadzone are considered in the tracking control design. Stability of the closed-loop system is analyzed via the Lyapunov's direct method. The critic neural network is used to estimate the cost-to-go and the actor neural network is used to make the cost-to-go converge. Simulation studies are conducted to examine the effectiveness of the proposed actor critic neural network control.

[1]  Qinmin Yang,et al.  Reinforcement Learning Controller Design for Affine Nonlinear Discrete-Time Systems using Online Approximators , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Frank L. Lewis,et al.  Intelligent compensation of actuator nonlinearities , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[3]  Shuzhi Sam Ge,et al.  Adaptive neural control of uncertain MIMO nonlinear systems , 2004, IEEE Transactions on Neural Networks.

[4]  Frank L. Lewis,et al.  Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem , 2009, 2009 International Joint Conference on Neural Networks.

[5]  Shuzhi Sam Ge,et al.  Adaptive Neural Network Control of Robotic Manipulators , 1999, World Scientific Series in Robotics and Intelligent Systems.

[6]  Shuzhi Sam Ge,et al.  Adaptive neural control of MIMO nonlinear state time-varying delay systems with unknown dead-zones and gain signs , 2007, Autom..

[7]  Jennie Si,et al.  Online learning control by association and reinforcement , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[8]  W. Zhonghua,et al.  Robust adaptive deadzone compensation of DC servo system , 2006 .

[9]  Luigi Fortuna,et al.  Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control , 2009 .

[10]  Shuzhi Sam Ge,et al.  Adaptive neural network control for a Robotic Manipulator with unknown deadzone , 2013, Proceedings of the 32nd Chinese Control Conference.

[11]  Shuzhi Sam Ge,et al.  Adaptive NN control of uncertain nonlinear pure-feedback systems , 2002, Autom..

[12]  Frank L. Lewis,et al.  Deadzone compensation in motion control systems using neural networks , 2000, IEEE Trans. Autom. Control..

[13]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[14]  Kenji Doya,et al.  Reinforcement Learning in Continuous Time and Space , 2000, Neural Computation.

[15]  Frank L. Lewis,et al.  Adaptive optimal control for continuous-time linear systems based on policy iteration , 2009, Autom..

[16]  Frank L. Lewis,et al.  Adaptive optimal control algorithm for continuous-time nonlinear systems based on policy iteration , 2008, 2008 47th IEEE Conference on Decision and Control.

[17]  Shuzhi Sam Ge,et al.  Impedance Learning for Robots Interacting With Unknown Environments , 2014, IEEE Transactions on Control Systems Technology.