Adaptive Iterative Learning Control Of Constrained Nonlinearly Parameterized Systems

In the dissertation, new iterative learning control schemes are studied to deal with constraint problems in nonlinear continuous-time systems. Particularly, adaptive iterative learning control is considered for a class of nonlinearly parameterized systems with input saturations and unknown time-varying delays. Meanwhile, iterative learning control is also studied for a class of nonlinear multi-input-multi-output systems with unknown nonparametric uncertainties and input saturations. Main works and contributions in this dissertation are summarized as follows.1. Adaptive iterative learning control problem is studied for a class of nonlinearly parameterized systems with input saturations and unknown time-varying delays which is widely encountered in many practical dynamic systems. First, for a class of nonlinearly parameterized systems with input saturations, an partially saturated adaptive iterative learning controller with fully saturated parameter updating law is presented; Second, for a class of nonlinearly parameterized systems with input saturations and unknown time-varying delays simultaneously, we studied the iterative learning control problem. Through the use of parameter separation technique, the unknown nonlinearities of local Lipschitz continuous function, the time-varying parameters and the unknown time-varying delays can be separated and combined into an unknown time-varying function. Based on this, an partially saturated adaptive iterative learning controller with fully saturated parameter updating law is designed. Finally, by constructing Lyapunov-Krasovskii-Like composite energy function, the boundedness of closed-loop signals and the asymptotic convergence of tracking error along the iteration domain can be guaranteed by the proposed controllers mentioned above. Both theory analysis and numerical simulations can verify that the nonlinearities caused by input saturations and unknown time-varying delays can be handled effectively by the proposed schemes.2. The consensus problem of a class of homogeneous multi-agent systems under a repeatable operation environment is studied. First, for the multi-agent systems with input saturations, where the agents’dynamics are modeled by nonlinearly parameterized equations with input saturations term, a distributed adaptive iterative learning controller is presented. Moreover, we demonstrate rigorously that the proposed control mechanism can guarantee the followers track the virtual leader asymptotically in the iteration domain. Second, a distributed adaptive iterative learning control scheme is developed for consensus problem of multi-agent systems whose dynamics can be modeled by nonlinearly parameterized equations with input saturations term and time-varying delays. By constructing a new designed time-weighted Lyapunov-Krasovskii-Like composite energy function, we can prove that the followers can track the virtual leader perfectly as the iteration number tends to infinity.3. An iterative learning control scheme is proposed for a class of nonlinear multi-input-multi-output systems under state alignment condition with unknown nonparametric uncertainties and input saturations. And we also prove that the proposed control scheme can handle the nonlinearities effectively which is caused by nonparametric uncertainties and input saturations. Meanwhile, the identical initial condition in iterative learning control can be relaxed.