Adaptive Robust Repetitive/iterative Learning Control With Its Implementation For A Motor

Adaptive control lacks the robust ability with regarding to external disturbance, measurable noise, time-varying parameters, unmodeled error and so on. To overcome its defect, adaptive robust theory is proposed to achieve the satisfactory performance, which combines advantages of adaptive control and deterministic robust control. In view of the periodic tracking error, the adaptive robust repetitive/iterative learning control method is presented. Then, the finite-time convergent performance is realized by applying the terminal attracting technique. To compensate the spatial periodic disturbance and random disturbance, an adaptive robust repetitive learning controller is designed. The experiments are operated to validate these algorithms.The main work and achievements of this thesis are as follows:1. When the system contains constant parameter and random disturbance, adaptive robust controller is applied. Furthermore, in view of repeat operating system with time-varying parameter, an adaptive robust repetitive/iterative learning controller is proposed.2. In order to eliminate the periodic state disturbance, spatial adaptive repetitive algorithm is presented. Furthermore, if the spatial periodic disturbance and random disturbance both exist,a spatial adaptive robust repetitive learning algorithm is designed.3. The repetitive/iterative learning finite-time controller is designed based on adaptive robust control technique. The finite-time convergent performance is realized. If it has no disturbance, algorithm ensures that system tracks the desired trajectory completely and the output error converges to zero.4. By using the empirical transfer function estimation method, mathematical model of the system is built. Based on the permanent magnet synchronous motor, the proposed control algorithms are applied to servo system. Experimental data shows the effective of the algorithms.