Combined NN/RISE-based Asymptotic Tracking Control of a 3 DOF Robot Manipulator

Robotic control is an interesting subject due to its extensive industrial applications. This paper proposes a new continuous control design for tracking problem of a three degree-of-freedom (DOF) robot manipulator in presence of uncertainties and bounded external disturbances. The proposed method is based on the combination of the recently developed robust integral of the sign of the error (RISE) feedback and neural network (NN) feed forward terms. In this control strategy, a feed forward NN is utilized to compensate uncertainties of the nonlinear system. Furthermore, the RISE feedback control term is used to eliminate the NN approximation error and bounded external disturbances. Typical NN-based controllers generally provide the convergence only with uniformly ultimately bounded (UUB) error due to the NN reconstruction error. However, the proposed method guarantees asymptotic tracking by eliminating this error. Using Lyapunov stability analysis, a semi-global asymptotic tracking of the robot joints is achieved. Besides, a comparative study on the system performance is conducted between the proposed control strategy and an NN-based controller. Simulation results demonstrate that the proposed controller is robust to deal with uncertainties, and verify the effectiveness of the proposed control scheme.

[1]  D. Dawson,et al.  A continuous asymptotic tracking control strategy for uncertain multi-input nonlinear systems , 2003, Proceedings of the 2003 IEEE International Symposium on Intelligent Control.

[2]  Warren E. Dixon,et al.  Nonlinear Control of Engineering Systems , 2002 .

[3]  Fuchun Sun,et al.  Neural network control of flexible-link manipulators using sliding mode , 2006, Neurocomputing.

[4]  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.

[5]  C C Cheah,et al.  ADAPTIVE TRACKING CONTROL FOR ROBOTS WITH UNCERTAINTIES IN KINEMATIC, DYNAMIC AND ACTUATOR MODELS , 2006 .

[6]  Andrew A. Goldenberg,et al.  Robust control of robot manipulators based on dynamics decomposition , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[7]  Warren E. Dixon,et al.  Asymptotic Tracking for Systems With Structured and Unstructured Uncertainties , 2006, IEEE Transactions on Control Systems Technology.

[8]  Guoqiang Hu,et al.  Lyapunov-Based Tracking Control in the Presence of Uncertain Nonlinear Parameterizable Friction , 2007, IEEE Transactions on Automatic Control.

[9]  M. Kemal Ciliz,et al.  Adaptive control of robot manipulators with neural network based compensation of frictional uncertainties , 2005, Robotica.

[10]  Masayoshi Tomizuka,et al.  Robust MIMO model following with application to trajectory motion control of robot arms , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[11]  Antônio de Pádua Braga,et al.  Control of a Robotic Manipulator Using Artificial Neural Networks with On-line Adaptation , 2000, Neural Processing Letters.

[12]  Frank L. Lewis,et al.  Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities , 1987 .

[13]  Ricardo O. Carelli,et al.  Neural networks for advanced control of robot manipulators , 2002, IEEE Trans. Neural Networks.

[14]  Okyay Kaynak,et al.  The fusion of computationally intelligent methodologies and sliding-mode control-a survey , 2001, IEEE Trans. Ind. Electron..

[15]  Toshio Fukuda,et al.  Robust sliding-mode tip position control for flexible arms , 2001, IEEE Trans. Ind. Electron..

[16]  Warren E. Dixon,et al.  Asymptotic Tracking for Aircraft via Robust and Adaptive Dynamic Inversion Methods , 2010, IEEE Transactions on Control Systems Technology.

[17]  Warren E. Dixon,et al.  Asymptotic Tracking for Uncertain Dynamic Systems Via a Multilayer Neural Network Feedforward and RISE Feedback Control Structure , 2008, IEEE Transactions on Automatic Control.

[18]  B. Wittenmark,et al.  A survey of adaptive control applications , 1997, IEEE Industry Applications Society Dynamic Modeling Control Applications for Industry Workshop.

[19]  Euntai Kim,et al.  Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic , 2004, IEEE Trans. Fuzzy Syst..

[20]  Frank L. Lewis,et al.  Multilayer neural-net robot controller with guaranteed tracking performance , 1996, IEEE Trans. Neural Networks.

[21]  Fuchun Sun,et al.  Neural Network Control of Spacecraft Formation Using RISE Feedback , 2010 .

[22]  Minho Lee.  A sliding mode controller with neural network and fuzzy logic , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

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

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

[25]  Guangjun Liu,et al.  Uncertainty decomposition-based robust control of robot manipulators , 1996, IEEE Trans. Control. Syst. Technol..

[26]  Zhijun Cai,et al.  Robust adaptive asymptotic tracking of nonlinear systems with additive disturbance , 2006, IEEE Transactions on Automatic Control.

[27]  W.E. Dixon,et al.  Asymptotic Tracking for Uncertain Dynamic Systems via a Multilayer NN Feedforward and RISE Feedback Control Structure , 2007, 2007 American Control Conference.

[28]  Xinghuo Yu,et al.  Sliding-Mode Control With Soft Computing: A Survey , 2009, IEEE Transactions on Industrial Electronics.

[29]  Eduardo Bayro Corrochano,et al.  INTEGRAL NESTED SLIDING MODE CONTROL FOR ROBOTIC MANIPULATORS , 2008 .