Data-Based Inverse Reinforcement Learning for Nonlinear Systems With Control Constraints
提出一种在线数据驱动的逆强化学习方法，利用系统演示的状态和输入数据恢复未知奖励函数，解决带控制约束的非线性系统最优控制问题。
This article proposes an online data-based inverse reinforcement learning (IRL) scheme to solve optimal control problem for nonlinear systems with control constraints, in which the unknown reward functions are recovered based on the systems’ demonstrated state and input data. To deal with control constraint, we introduce a saturation function to formulate the original constrained optimal control problem into a new unconstrained optimal control problem. Then a data-based identifier using neural network (NN) approximation technique is designed to estimate the system’s dynamics. Subsequently, we develop a data-based IRL approach to learn the unknown reward function and establish the weight tuning law of the value function and reward function using demonstrated state and input data. The proof of the uniform boundedness of the weight estimation error is presented. A simulation example is provided to verify the effectiveness of the proposed approach.