Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 6, Pages 3196-3205Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2018.2874182
Keywords
Adaptive control; mobile robot; trajectory-tracking control; tube-based model predictive control (MPC)
Categories
Funding
- National Natural Science Foundation of China [61573147, 61625303, 61751310]
- National key research and development program of China [2017YFB1302302]
- Guangdong Science and Technology Research Collaborative Innovation Projects [2014B090901056, 2015B020214003, 2016A020220003]
- Guangdong Science and Technology Plan Project (Application Technology Research Foundation) [2015B020233006]
- Anhui Science and Technology Major Program [17030901029, TII-18-1217]
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This paper addresses a trajectory-tracking control problem for mobile robots by combining tube-based model predictive control (MPC) in handling kinematic constraints and adaptive control in handling dynamic constraints. In order to handle kinematic constraints, the tube-based MPC scheme is introduced, which includes the state feedback controller to suppress the external disturbance in the velocity level. The tube-based MPC is transformed to a constrained quadratic programming (QP) problem, and then the QP problem can be efficiently solved by a primal-dual neural network over a finite receding horizon so as to obtain the optimal control velocity. Besides, an adaptive controller employing the neural network technology is proposed to acquire the approximation of the uncertain robotic dynamics. Moreover, an auxiliary control is developed in order to deal with actuator saturation, and a disturbance observer is designed to reject the external disturbance online in the dynamic level. Subsequently, through Lyapunov function synthesis, the stability of the closed-loop system have been guaranteed. Finally, in order to verify the effectiveness, the experimental studies are carried out using an actual mobile robot.
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