This paper develops a novel adaptive integral sliding-mode control (SMC) technique to improve the tracking performance of a wheeled inverted pendulum (WIP) system, which belongs to a class of continuous time systems with input disturbance and/or unknown parameters. The proposed algorithm is established based on an integrating between the advantage of online adaptive reinforcement learning control and the high robustness of integral sliding-mode control (SMC) law. The main objective is to find a general structure of integral sliding mode control law that can guarantee the system state reaching a sliding surface in finite time. An adaptive/approximate optimal control based on the approximate/adaptive dynamic programming (ADP) is responsible for the asymptotic stability of the closed loop system. Furthermore, the convergence possibility of proposed output feedback optimal control was determined without the convergence of additional state observer. Finally, the theoretical analysis and simulation results validate the performance of the proposed control structure.

本文开发了一种新颖的自适应积分滑模控制（SMC）技术来提高轮式倒立摆（WIP）系统的跟踪性能，该系统属于一类具有输入扰动和/或未知参数的连续时间系统。该算法是基于在线自适应强化学习控制的优点与积分滑模控制法的高鲁棒性之间的综合而建立的。主要目的是找到整体滑模控制律的一般结构，该规律可以保证系统状态在有限时间内到达滑动表面。基于近似/自适应动态规划（ADP）的自适应/近似最佳控制负责闭环系统的渐近稳定性。此外，在没有其他状态观测器收敛的情况下，确定了建议的输出反馈最优控制的收敛可能性。最后，理论分析和仿真结果验证了所提出控制结构的性能。