Model Reference Adaptive Control with Input and Output Constraints

本研究的目标是设计、分析和实现 一种模型参考自适应控制算法， 输入和输出信号的幅度和变化率。 的 所提出的算法的鲁棒性关于有界 将考虑扰动和时变参数。 在开发高度复杂的 自适应算法 然而，现有的结果在很大程度上忽略了 现实的输入和输出约束的存在。 迄今 稳定性的结果已经建立了自适应控制 算法与饱和输入和开环稳定的植物。 本文提出了一种模型参考自适应控制算法 具有时变参考模型、时变调节动态 长期预报原则。 使用这种方法，饱和度 的输入和输出信号，这可能会导致不稳定，是为了 在很大程度上避免了。 目的是获得良好的跟踪和 同时满足预定输入的调节特性 和输出限制。 开发的算法将由以下人员进行分析和广泛检查 稳定和不稳定开环工厂的模拟。 将 最终应用于人体血压的控制， 机械手的控制。

The goal of the proposed research is to design, analyze and implement a model reference adaptive control algorithm with constraints on the amplitude and the rate of change of input and output signals. The robustness of the proposed algorithm with respect to bounded disturbance and time-varying parameters will be considered. Much work has been done in the development of highly sophisticated adaptive algorithms. The existing results, however, largely ignore the presence of realistic input and output constraints. So far the stability results have been established for adaptive control algorithms with saturating inputs and open-loop stable plants. In this work a model reference adaptive control algorithm is proposed with time-varying reference model, time-varying regulation dynamics and long-range prediction principle. Using this method the saturation of the input and output signals, which can lead to instability, is to a large extent avoided. The aim is to obtain good tracking and regulation properties satisfying at the same time prespecified input and output constraints. The developed algorithm will be analyzed and extensively checked by simulations for both stable and unstable open-loop plants. It will be finally applied for control of blood pressure in human body and for control of robotic manipulators.