Policy Iterative-Based Adaptive Optimal Control for Unknown Continuous-Time Nonlinear Systems
针对动态未知的连续时间非线性系统，提出一种基于策略迭代的自适应最优控制方法，利用多变量神经网络线性微分包含逼近非线性模型，仅需状态和输入数据即可求解最优控制，仿真验证了可行性。
This study addresses the optimal control problem for continuous-time nonlinear systems with unknown dynamics. A policy iterative-based optimization algorithm is proposed to solve this problem by leveraging a novel neural network representation termed multivariable neural network linear differential inclusion (MVNNLDI). MVNNLDI approximates the initial nonlinear model with a linear differential equation formulation that includes bounded disturbances. Based on this linearized representation, the relevant adaptive optimal control and disturbance compensation approach are derived to tackle the nonlinear optimization problem. Capitalizing on model-free control principles, the optimal solutions can be obtained using only measured state and input data, thus simplifying algorithmic complexity and accelerating convergence speed substantially. Finally, we use two simulation experiments to demonstrate the feasibility and effectiveness of the proposed method.