Iterative Learning Control for Nonlinear Switched Systems With Constant Time Delay and Noise

Abstract

The note focuses on the issue of iterative learning control for a kind of non-affine nonlinear switched systems with state time delay and external noise. According to the structural properties of the systems, a proportional-type scheme is considered. Then, the effect of both constant time delay and external noise on the tracking performance is explored by employing contraction mapping principle and Bellman-Gronwall inequality. Results reveal that time delay has insignificant influence on learning performance if it is less than dwell time, and the output follows into a permissible neighbourhood of the desired trajectory on the whole operation time interval if noise fluctuates within finite boundary. The system switching will reduce the convergence speed of the ILC algorithm, but will not change the convergence in iterative domain. Finally, simulation is given to illustrate the feasibility of the theoretical analysis.

Iterative learning tracking control for second‐order nonlinear hyperbolic impulsive partial differential systems

“…ILC was widely used in many fields such as robotics [21]- [23], rehabilitation medicine [24], chemical batch processing technology [25], and tracking control of piezoelectric systems [26]. In addition to practical applications, ILC has also been widely used in switching systems [27,28], time-delay systems [29], and stochastic systems [30]. Indeed, many scholars have applied ILC methods to impulsive differential systems.…”

Robustness Analysis of Exponential Stability of Neutral-Type Nonlinear Systems With Multi-Interference

“…N ONLINEAR dynamic systems have received more and more attention due to the changeable dynamic properties, variety of model forms and arbitrary switching patterns. Up to now, there have been quite a few characteristic investigations of control methods and dynamical behaviors for nonlinear systems [1]- [20], such as fixed-time control [1], event-triggered adaptive control [2], distributed control [3], piecewise control [4], fuzzy control [5], horizon control [6], U-control [7], passivity cascade technique-based control [8], stabilization control [9], iterative learning control [10], sliding set design [11], robustness control [12] and so forth. In addition, various dynamical behaviors of nonlinear systems have been explored [13]- [20], such as asymptotic stability [13], Mittag-Leffler stability [14], globally exponential stabilization [15], [16], synchronization [13], [17], dissipativity [18], robustness analysis [19], [20], etc.…”