Model reference adaptive control for nonlinear time‐varying hybrid dynamical systems
Model reference adaptive control for nonlinear time‐varying hybrid dynamical systems
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DOI：
10.1002/acs.3631
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发表时间：
2023-05
影响因子：
3.1
通讯作者：
Andrea L’Afflitto
中科院分区：
文献类型：
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作者：
Andrea L’Afflitto
针对受匹配不确定性和参数不确定性影响的非线性、时变、混杂动态对象，其复位事件是时间和对象状态的未知函数，提出了第一个模型参考自适应控制系统。除了类似于连续时间动态系统的经典模型参考自适应控制框架的控制律和自适应律外，该框架还允许在参考模型的轨迹中施加瞬时变化以快速将轨迹跟踪误差引导到零，同时保持闭环系统跟踪用户定义信号的能力。本文还首次将经典的Lasalle-Yoshizawa定理推广到时变混合动力系统，从而得到了这些结果。数值仿真表明了所提出的自适应控制系统的主要特点，并强调了其与应用于相同问题的经典模型参考自适应控制系统相比，在减少控制量和轨迹跟踪误差方面的能力。
This paper presents the first model reference adaptive control system for nonlinear, time‐varying, hybrid dynamical plants affected by matched and parametric uncertainties, whose resetting events are unknown functions of time and the plant's state. In addition to a control law and an adaptive law, which resemble those of the classical model reference adaptive control framework for continuous‐time dynamical systems, the proposed framework allows imposing instantaneous variations in the reference model's trajectory to rapidly steer the trajectory tracking error to zero, while retaining the closed‐loop system's ability to follow a user‐defined signal. These results are enabled by the first extension of the classical LaSalle–Yoshizawa theorem to time‐varying hybrid dynamical systems, which is presented in this paper as well. A numerical simulation shows the key features of the proposed adaptive control system and highlights its ability to reduce both the control effort and the trajectory tracking error over a classical model reference adaptive control system applied to the same problem.