Adaptive Iterative Learning Prescribed Performance Control of Uncertain Strict-Feedback Systems With Improved Parameter Estimation

The existing adaptive iterative learning control approaches with variable constraints mainly consider the matched uncertainties and only ensure the boundedness of parameter estimation errors. In this paper, an adaptive iterative learning control (AILC) method with prescribed performance constraints and improved parameter estimations is developed for a class of uncertain nonlinear strict-feedback systems. The prescribed performance control is achieved through generating a preset error trajectory in each iteration within the performance envelope and making the error of the actual tracking error versus the preset one small enough all the time. The improvement of the parameter estimation performance is realized by reconstructing the parameter estimation errors and using them to modify the differential-difference adaptive laws. The control algorithm is designed based on the dynamic surface control method and thus free from the “differential explosion” problem. It is guaranteed via the Lyapunov theory that all signals of the closed-loop system are semi-global bounded, the system output could track the given reference trajectory with the prescribed performance in each iteration and the $\mathcal {L}_{2}$ norms of the estimation errors are uniformly ultimately bounded along the iteration-axis. Additionally, two simulation examples illustrate the effectiveness and advantages of the proposed adaptive iterative learning control method.