论文标题
状态和输入约束模型参考自适应控制
State and Input Constrained Model Reference Adaptive Control
论文作者
论文摘要
对状态和输入约束的满意度是控制工程应用程序中最关键的要求之一。在经典模型参考自适应控制（MRAC）公式中，尽管状态和输入保持界限，但界限既不是用户定义的也不是已知的A-Priori。在本文中，使用简单的饱和控制设计以及屏障Lyapunov函数（BLF），开发了具有用户定义状态和输入约束的多变量线性时间不变（LTI）工厂的MRAC。如果没有任何可能限制实际实施的限制性假设，则提议的控制器可以保证植物状态和控制输入均保持在用户定义的安全集内，同时确保工厂状态轨迹轨迹轨迹轨迹。控制器确保所有闭环信号保持界限，并且轨迹跟踪误差会渐近地收敛到零。与标准MRAC算法相比，模拟结果验证了所提出的受限MRAC在更好的跟踪性能和有限的控制工作方面的疗效。
Satisfaction of state and input constraints is one of the most critical requirements in control engineering applications. In classical model reference adaptive control (MRAC) formulation, although the states and the input remain bounded, the bound is neither user-defined nor known a-priori. In this paper, an MRAC is developed for multivariable linear time-invariant (LTI) plant with user-defined state and input constraints using a simple saturated control design coupled with a barrier Lyapunov function (BLF). Without any restrictive assumptions that may limit practical implementation, the proposed controller guarantees that both the plant state and the control input remain within a user-defined safe set for all time while simultaneously ensuring that the plant state trajectory tracks the reference model trajectory. The controller ensures that all the closed-loop signals remain bounded and the trajectory tracking error converges to zero asymptotically. Simulation results validate the efficacy of the proposed constrained MRAC in terms of better tracking performance and limited control effort compared to the standard MRAC algorithm.
