An Approximate Optimal Control Approach for Robust Stabilization of a Class of Discrete-Time Nonlinear Systems With Uncertainties
研究了一类离散时间非线性系统在不确定性下的鲁棒镇定问题，通过将鲁棒控制转化为最优控制问题，并用神经网络求解离散广义哈密顿-雅可比-贝尔曼方程，数值仿真验证了方法的有效性。
In this correspondence paper, the robust stabilization of a class of discrete-time nonlinear systems with uncertainties is investigated by using an approximate optimal control approach. The robust control problem is transformed into an optimal control problem under some proper restrictions on the bound of the uncertainties. For the purpose of dealing with the transformed optimal control, the discrete-time generalized Hamilton-Jacobi-Bellman equation is introduced and then solved using the successive approximation method with neural network implementation. In addition, a numerical simulation is included to illustrate the effectiveness of the robust control strategy.