论文标题
限制未知系统的最佳跟踪控制：多步线性编程方法
Constrained Optimal Tracking Control of Unknown Systems: A Multi-Step Linear Programming Approach
论文作者
论文摘要
我们研究了具有输入约束的未知离散确定性系统的最佳状态反馈跟踪控制的问题。为了处理输入约束，最先进的方法利用了某些非二次阶段成本函数，这有时会限制实际系统。此外，众所周知，在数据驱动控制中广泛使用的两种算法，提供了互补的优势和劣势，众所周知，政策迭代（PI）和价值迭代（VI）（VI）。在这项工作中，采用了两步转换，将约束的最佳跟踪问题转换为无约束的增强最佳调节问题，并允许考虑一般阶段成本功能。然后，得出了一种基于Q学习和线性编程的新型多步VI算法。所提出的算法提高了VI的收敛速度，避免了对PI初始稳定控制策略的要求，并计算一个受约束的最佳反馈控制器，而无需了解系统模型和舞台成本函数。仿真研究证明了拟议方法的可靠性和性能。
We study the problem of optimal state-feedback tracking control for unknown discrete-time deterministic systems with input constraints. To handle input constraints, state-of-art methods utilize a certain nonquadratic stage cost function, which is sometimes limiting real systems. Furthermore, it is well known that Policy Iteration (PI) and Value Iteration (VI), two widely used algorithms in data-driven control, offer complementary strengths and weaknesses. In this work, a two-step transformation is employed, which converts the constrained-input optimal tracking problem to an unconstrained augmented optimal regulation problem, and allows the consideration of general stage cost functions. Then, a novel multi-step VI algorithm based on Q-learning and linear programming is derived. The proposed algorithm improves the convergence speed of VI, avoids the requirement for an initial stabilizing control policy of PI, and computes a constrained optimal feedback controller without the knowledge of a system model and stage cost function. Simulation studies demonstrate the reliability and performance of the proposed approach.
