Adaptive sliding Mode Control for Nonholonomic Mobile Robots based on Neural Networks

To improve the tracking precision of mobile robots with unknown disturbances, an adaptive sliding mode control method with stronger robustness is proposed based on the neural networks. A new control law is designed, in which the equivalent term is replaced by a double power term to improve the convergence rate of sliding mode control. Moreover, the RBF neural network is employed to estimate the upper bound of the uncertainties and to compensate for the unknown parameters and nonparametric disturbances. The precision of trajectory tracking control is improved. This control algorithm is simple and more applicable to mobile robots. The stability is proved, and the simulations demonstrate the superiority and feasibility of the proposed algorithm.

[1]  Fares Boudjema,et al.  Integral Sliding Mode Control for Trajectory Tracking of Wheeled Mobile Robot in Presence of Uncertainties , 2016 .

[2]  Phuc Thinh Doan,et al.  Trajectory tracking control of omnidirectional mobile robot using sliding mode controller , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[3]  Juntao Fei,et al.  Adaptive sliding mode control of dynamic system using RBF neural network , 2012 .

[4]  Huang Jin-feng.  A new double power reaching law for sliding mode control , 2013 .

[5]  Frank L. Lewis,et al.  Control of a nonholonomic mobile robot using neural networks , 1998, IEEE Trans. Neural Networks.

[6]  S. S. Vishnu Prasad,et al.  Development of backstepping sliding mode tracking control for Wheeled Mobile Robot , 2014, 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies.

[7]  Maarouf Saad,et al.  Dynamic tracking control of mobile robot using exponential sliding mode , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[8]  Baoguo Li,et al.  Adaptive fuzzy control for mobile robot obstacle avoidance based on virtual line path tracking , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[9]  Bin Li,et al.  Research on Improve the Generalization Ability of Neural Network Ensemble System , 2013 .

[10]  Mohammed Chadli,et al.  Robust adaptive neural network-based trajectory tracking control approach for nonholonomic electrically driven mobile robots , 2017, Robotics Auton. Syst..

[11]  Zongyi Wang.  Dual Adaptive Neural Sliding Mode Control of Nonholonomic Mobile Robot , 2010 .

[12]  Tarak Damak,et al.  Adaptive sliding-mode control of nonholonomic wheeled mobile robot , 2014, 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[13]  Fumio Miyazaki,et al.  A stable tracking control method for an autonomous mobile robot , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[14]  Klaus-Dieter Kuhnert,et al.  Robust adaptive control of nonholonomic mobile robot with parameter and nonparameter uncertainties , 2005, IEEE Transactions on Robotics.

[15]  F. Hamerlain,et al.  Adaptive sliding mode for the control of a wheeled mobile robot , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

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

[17]  Wang Zhangqi,et al.  Mobile Robot Path Planning based on Parameter Optimization Ant Colony Algorithm , 2011 .