Optimal Output–Feedback Tracker Design of Linear Quadratic Tracking Problem Using High-Order Filter and Incremental Data Adaptive Dynamic Programming
针对线性二次型跟踪问题，提出一种输出反馈自适应动态规划框架，利用高阶滤波器使高阶导数信号可观测，并通过增量数据算法学习最优跟踪器，仿真验证了有效性。
In this study, a novel optimal tracker is developed for the linear quadratic tracking (LQT) problem using an output–feedback adaptive dynamic programming (ADP) framework. By leveraging the minimal polynomial of the exosystem matrix, we parameterize the steady-state input, state, and output, and incorporate them into the performance index of the LQT formulation. Unlike existing studies on LQT, in this study, we introduce a high-order filter to make the signals related to the high-order derivatives of the system output and input observable. Subsequently, we develop an incremental data ADP algorithm to learn the optimal dynamic output–feedback tracker, eliminate the impact of high-order filtering error and state reconstruction error on the iterative learning equation (ILE) within a specified time. Finally, the comprehensive simulation results show the effectiveness of the proposed output–feedback tracker.