Unified Design Method for Suboptimal Control of Nonlinear System With Multiple Constraints
提出一种统一方法，将状态、输入和输出等不等式约束转化为等式约束，为未知非线性系统设计次优控制器，并利用神经网络和积分强化学习处理未建模动态。
This article proposes a unified suboptimal controller design method for unknown general nonlinear systems subject to multiple constraints, including state, input, and output constraints. All inequality constraints are transformed into equality constraints using slack functions and Pade approximation. An unconstrained augmented system is then defined to describe the dynamics of original system and the equality constraints, where the optimal controller of the augmented system can be viewed as a suboptimal controller for the original system. Furthermore, considering the unmodeled dynamics of the original system, neural networks (NNs) are utilized and a data-based solution strategy of integral reinforcement learning (IRL) is presented for the augmented system. Ultimately, the simulation results are given to reflect the effectiveness of the unified design method.