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.
