On Model Free Learning Adaptive Control And Applications

This dissertation focuses on some issues on the model-flee adaptive control theory and its applications for nonlinear discrete-time systems.The main work and key innovations are summarized as the following:1.The stability and convergence of the model flee adaptive control based on partial form linearization(PFL-MFAC) are proved,and corresponding results are applied to permanent magnet linear motor(PMLM) and three-tank water.2.The improved MFAC based on tight form linearization(TFL-iMFAC) and improved MFAC based on partial form linearization(PFL-iMFAC) are developed for a class of large lag nonlinear discrete-time system.Theoretical analysis and simulation results show that the TFL-iMFAC and PFL-iMFAC are efficient.3.Based on the partial form linearization method along iteration axis and the full form linearization method along iteration axis,the model flee adaptive optimal iterative learning control based on PFL(PFL-MFAOILC) and the model free adaptive optimal iterative learning control based on FFL(FFL-MFAOILC) are developed for a class of SISO nonlinear discrete-time systems.The stability and convergence of the PFL-MFAOILC and FFL-MFAOILC are proved,and the corresponding results are applied to freeway traffic system.4.An iterative learning identification method is developed to estimate the parameters of the more general nonlinear discrete-time system.The stability and convergence of the identification algorithm are proved,and the corresponding results are applied to freeway traffic system.5.A novel parametric discrete-time adaptive iterative learning control(P-DAILC) by cooperating projection algorithm is applied to freeway traffic system and PMLM control system,and the simulation results show that the P-DAILC can deal with random initial conditions and iteration-varying reference trajectories,in the sequel achieving an almost perfect tracking performance over a finite interval.