An algorithm of model reference iterative learning control (MR-ILC) was proposed for nonlinear systems with periodic and non-periodic uncertain disturbances. The objective is to make the state variables of a controlled system can be steered to follow the state variables of a reference model that does not necessarily have the same structure as the controlled system on condition that the controlled system is satisfied with assumptions and adopted with matching learning rate. The λ-norm was adopted as the topological measure in proof of the convergence of the system. The effectiveness of proposed scheme was illustrated by simulation results.

A simple modified Smith predictor controller design was proposed for integrating processes with long dead time. The overall control structure introduced two physically meaningful tuning parameters. One was used to set the speed of the closed-loop servo response and the other was used to set the performance of the load response. A simple controller tuning method was also developed to obtain the PI tuning parameters. With a given estimation of the unmatched model, the stability region of the tuning parameter was estimated. Two examples were used to demonstrate the effectiveness of the method.

An iterative learning control algorithm was proposed for linear time-invariant systems based on optimal theory. On the basis of the quadratic performance criterion, a gradient descent search method was adopted to fit the iterative learning control law. Simulation results demonstrate that the proposed algorithm has rapid convergence speed. Moreover, the actual output of the systems can track the given trajectory rapidly under less iterated times.