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Design of indirect mras based adaptive control systems

Direct MRAS offers a potential solution to reduce the tracking errors in the presence of uncertainties and variation in plant behavior. However, this control algorithm may fail to be robust to measurement noise. In order to solve this trouble, the indirect MRAS is introduced that permanently adjust the parameters of observers. The adaptive adjusting law is derived by applying Lyapunov theory. The adaptive algorithm that is shown in this paper is quite simple, robust and converges quickly. Performances of the controlled systems are studied through simulation in Matlab/Simulink environment. The effectiveness of the methods is demonstrated by numerical simulations. | Trần Thiện Dũng và Đtg Tạp chí KHOA HỌC & CÔNG NGHỆ 139(09): 245 - 251 DESIGN OF INDIRECT MRAS-BASED ADAPTIVE CONTROL SYSTEMS Tran Thien Dung, Dang Van Huyen, Dam Bao Loc, Nguyen Duy Cuong* College of Technology – TNU SUMMARY Direct MRAS offers a potential solution to reduce the tracking errors in the presence of uncertainties and variation in plant behavior. However, this control algorithm may fail to be robust to measurement noise. In order to solve this trouble, the indirect MRAS is introduced that permanently adjust the parameters of observers. The adaptive adjusting law is derived by applying Lyapunov theory. The adaptive algorithm that is shown in this paper is quite simple, robust and converges quickly. Performances of the controlled systems are studied through simulation in Matlab/Simulink environment. The effectiveness of the methods is demonstrated by numerical simulations. Keywords: Direct MRAS; Indirect MRAS;Lyapunov theory INTRODUCTION* The PID controller is an effective solution for most industrial control applications [1], [2]. The major problem with the fixed-gain PID controller is that the tracking error depends on plant parameter variations [4], [8], [9]. Because the selection of PID gains depends on the physical characteristics of the system to be controlled, there is no set of constant values that can be suited to every implementation when the dynamic characteristics are changing. Another problem with this controller is that the PID controlled system is sensitive to measurement noise. When the error is corrupted by noise, the noise content will be amplified by PID gains. These problems can be solved, for example, by using direct or indirect adaptive control systems that are designed based on MRAS. The basic philosophy behind Model Reference Adaptive Systems is to create a closed loop controller with parameters that can be adjusted based on the error between the output of the system and the desired response from the reference model [1] - .