论文标题
对未知纯反馈系统具有纯状态约束的自适应控制
Adaptive Control of Unknown Pure Feedback Systems with Pure State Constraints
论文作者
论文摘要
本文介绍了一类未知的纯反馈系统的跟踪控制问题，对状态变量和未知的时变界扰动具有纯粹的状态约束。第一次为此类系统提供了自适应控制器。控制器是使用反向替代方法设计的。在设计时，使用屏障Lyapunov函数，因此状态变量不会违反其约束。为了应对系统的未知动力，在线近似器是使用具有新颖自适应定律的神经网络设计的，以进行重量更新。在对系统的稳定性分析中，Lyapunov函数的时间导数涉及已知的虚拟控制系数，其方向未知方向，并处理Nussbaum增益的这种问题用于设计控制定律。此外，为了使控制器健壮和计算便宜，新型的干扰观察者旨在估计干扰以及神经网络近似误差以及虚拟控制输入的时间导数。通过对三阶非线性系统的仿真研究证明了所提出的方法的有效性。
This paper deals with the tracking control problem for a class of unknown pure feedback system with pure state constraints on the state variables and unknown time-varying bounded disturbances. An adaptive controller is presented for such systems for the very first time. The controller is designed using the backstepping method. While designing it, Barrier Lyapunov Functions is used so that the state variables do not contravene its constraints. In order to cope with the unknown dynamics of the system, an online approximator is designed using a neural network with a novel adaptive law for its weight update. In the stability analysis of the system, the time derivative of Lyapunov function involves known virtual control coefficient with unknown direction and to deal with such problem Nussbaum gain is used to design the control law. Furthermore, to make the controller robust and computationally inexpensive, a novel disturbance observer is designed to estimate the disturbance along with neural network approximation error and the time derivative of virtual control input. The effectiveness of the proposed approach is demonstrated through a simulation study on the third-order nonlinear system.
