This paper investigates the neural adaptive tracking control problem of a class of strict-feedback systems considering asymmetric full-state with input magnitude and rate constraint (MRC). By designing a dual-integral-type actual control law, the MRC on system input is transformed to be the magnitude limitations on the extended states of the original system, so the original system with both state and MRC considerations is converted to be a new system with only full-state constraint. Besides, compared with the traditional symmetric integral barrier Lyapunov function, new asymmetric integral barrier Lyapunov function is introduced to the dynamic surface-based controller design process in this paper for dealing with the asymmetric state constraint problem. It is analyzed that the original system is semi-globally uniformly ultimately bounded, and that the desired multiple constraints are never violated. The effectiveness of the control strategy is shown via numerical simulations.

本文研究了考虑具有输入大小和速率约束（MRC）的非对称全态的一类严格反馈系统的神经自适应跟踪控制问题。通过设计双积分型实际控制律，将系统输入上的MRC转换为对原始系统扩展状态的大小限制，因此将考虑了状态和MRC的原始系统转换为新系统仅具有全状态约束。此外，与传统的对称积分势垒Lyapunov函数相比，本文将新的非对称积分势垒Lyapunov函数引入基于动态曲面的控制器设计过程中，以解决非对称状态约束问题。分析认为，原始系统最终是半全局一致有界的，并且永不违反所需的多个约束。通过数值模拟显示了控制策略的有效性。