Iterative Learning Control For Some Classes Of Linear Systems

In this paper,the problems of iterative learning control(ILC)for linear discretetime systems,linear singular systems,distributed parameter systems and singular distributed parameter systems are studied,and the simulation examples verify the theoretical conclusions.The full text are divided into seven chapters:In chapter 1,the background and research status of ILC are introduced and the remaining problems are proposed.In chapter 2,the problem of ILC for linear discrete-time systems is studied.ILC laws are proposed based on SOR iterative method.It is shown that the scheme can guarantee the output tracking errors converge to zero.In chapter 3,the ILC is applied to linear singular systems with fixed initial shift.According to systems properties,a new PD-P-type ILC algorithm is proposed and the corresponding state limiting trajectory is presented,further the initial rectifying strategy is introduced.It is shown that the algorithm can guarantee the state of the system converges uniformly to the corresponding state limiting trajectory.In chapter 4,the problem of ILC for first order hyperbolic distributed parameter systems with initial-boundary value problem is considered.And the convergence condition for system under the action of P-type learning algorithm is given.In chapter 5,the problem of ILC for distributed parameter systems with bounded disturbance is studied.Under the effect of P-type learning algorithm,it is shown that the output tracking errors uniformly bounded on L~2 space,and make it further converge to zero in the absence of the disturbance.In chapter 6,the problem of ILC for singular distributed parameter systems is put forward.Using the contraction mapping method,it is shown that the output tracking errors on L~2 space converge to zero under the action of P-type learning algorithm.In chapter 7,the conclusion of this paper is given and the problems need to be further studied are presented.