IEEE Trans Neural Netw Learn Syst. 2018 Jun;29(6):2112-2126. doi: 10.1109/TNNLS.2018.2791419.
Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. When the system moves on the sliding surface, the optimal guaranteed cost control problem of sliding mode dynamics is transformed into the optimal control problem of a reformulated auxiliary system with a modified cost function. The ADP algorithm based on single critic neural network (NN) is applied to obtain the approximate optimal control law for the auxiliary system. Lyapunov techniques are used to demonstrate the convergence of the NN weight errors. In addition, the derived approximate optimal control is verified to guarantee the sliding mode dynamics system to be stable in the sense of uniform ultimate boundedness. Some simulation results are presented to verify the feasibility of the proposed control scheme.
基于积分滑模和近似动态规划（ADP）理论，针对具有匹配和不匹配干扰的约束输入非线性系统，设计了一种新颖的最优保性能滑模控制。当系统在滑动面上运动时，滑模动力学的最优保性能控制问题被转化为具有修正代价函数的重构辅助系统的最优控制问题。基于单评价神经网络（NN）的 ADP 算法被应用于获得辅助系统的近似最优控制律。李雅普诺夫技术用于证明 NN 权误差的收敛性。此外，所得到的近似最优控制被验证保证滑动模态动力学系统在一致最终有界意义下是稳定的。一些仿真结果被提出以验证所提出的控制方案的可行性。
