Event-triggered adaptive iterative learning prescribed performance control of high-order strict feedback nonlinear...

Most adaptive iterative learning control techniques involving variable constraints are primarily based on first-order system structures and neglect the impact of limited network communication resources. To address these limitations, this paper investigates the event-triggered adaptive iterative learning prescribed performance control (PPC) approach for a class of uncertain high-order strict feedback nonlinear systems. The considered systems contain linearly parameterized uncertainties in both the drift terms and the control coefficients. During each iteration, a preset error trajectory is constructed within the performance envelope to eliminate the limitation of initial conditions and the error of the actual tracking error relative to the preset one is kept small enough to realize the PPC. The detailed event-triggered adaptive iterative learning PPC algorithm is designed by treating every high-order subsystem as a whole and comprehensively utilizing the event-triggering mechanism, the nonlinear mapping technology and the dynamic surface control method. Moreover, an improved parameter estimation error reconstruction mechanism is given to modify the differential-difference adaptive laws and thus strengthen the parameter estimation performance, meanwhile the projection operator is employed to avoid the singularity problem of the control law. It is proved that the tracking error and L₂ norms of the parameter estimation errors are uniformly ultimately bounded along the iteration-axis under the interval excitation condition and the tracking error satisfies the prescribed transient performance in each iteration. The obtained results are validated via simulations on a single-link flexible-joint robot.