Lyapunov Approach To Design Of Iterative Learning Controllers

Adaptive control is able to handle the parameter uncertainties effectively,since it has the ability to adapt to the change of dynamic characteristics of objects.By synthesizing adaptive control and robust control,adaptive robust control can ensure the boundedness of system parameters,and make the tracking error converge to a neighborhood of the origin.In order to deal with the influence of periodic disturbances in repetitive systems,we combine adaptive robust control with iterative learning control.For uncertain systems,adaptive robust iterative learning controllers are designed,and the effectiveness of the controllers are verified on the platform of PMSM systems.The main work and achievements of this paper are as follows:1.The integral adaptive laws are given to estimate unknown parameters for the systems with uncertain constant parameters,and the finite-time adaptive robust controllers are designed by applying initial rectified attractors.The controllers can make the tracking error converge to a neighborhood of the origin within the specified time.The convergence analysises are elaborated in this paper.The simulation results verify the effectiveness of the algorithms.2.The incremental adaptive robust controllers are designed for the systems with uncertain constant parameters and the incremental adaptive laws are given to estimate unknown parameters.The incremental finite-time adaptive robust controllers are given by applying initial rectified attractors,and the controllers can make the actual trajectory track the desired trajectory within the specified time.Furhermore,we have illustrated the convergence analysises.Numerical simulation results verify the effectiveness of the algorithms.3.For the repetitive operating uncertain systems,adaptive robust learning controllers are presented.The controllers can make the actual trajectory track the desired trajectory,and effectively eliminate the influence of periodic interference.Their boundedness and convergence analysises are given.The simulation results verify the effectiveness of the algorithms.4.For time-varying uncertain systems,iterative/repetitive learning controllers are designed.The controllers can eliminate the influence of periodic interference,and make the tracking error converge to zero.Their boundedness and convergence analysises are given.Numerical simulation results verify the validity of the algorithms.5.The platform of PMSM systems is built,and the hardware and software components of servo systems are given.The working principle of servo systems is elaborated.By using the least square method,the model of servo systems is built.The proposed algorithms in this paper are applied to the platform of servo systems,aiming to realize the accurate position control of the motor.Experimental datas verify the effectiveness and superiority of those algorithms.