A new adaptive iterative learning control of finite-time hybrid function projective synchronization for unknown time-varying chaotic systems

Abstract

A new adaptive iterative learning control (AILC) scheme is proposed to solve the finite-time hybrid function projective synchronization (HFPS) problem of chaotic systems with unknown periodic time-varying parameters. Fourier series expansion (FSE) is introduced to deal with the problem of uncertain time-varying parameters. The bound of the expanded remaining items is unknown. A typical convergent series is used to deal with the unknown bound in the design process of the controller. The adaptive iterative learning synchronization controller and parameter update laws are designed. Two different chaotic systems are synchronized asymptotically according to different proportional functions on a finite time interval by Lyapunov stability analysis. The simulation example proves the feasibility and effectiveness of the proposed method.

Non-uniform Trajectory Tracking AILC for Nonlinear Time-varying Parameter System with Multiple Unknown Control Directions

“…, n are unknown, i.e., the control directions of the system are completely unknown. Since a time-varying parameter can be regarded as periodic in a finite time interval,the processing method of unknown periodic time-varying parameters θ(t) is the same as in article [18]. By Fourier series, periodic function θ(t) can be expanded as…”

Multi-scroll Hopfield neural network under electromagnetic radiation and its brain-like coupling synchronization

“…Brain regions are synchronized through the connection of some neurons to form inter-regional neural networks, and thus complete the information transmission between different brain regions. Designing a suitable controller is one of the most fundamental methods for controlling complex systems to achieve synchronization, and many scholars have proposed different control strategies [45][46][47][48][49][50][51][52][53][54][55]. In this paper, we use two bounded sub-MHNNs to represent different brain regions.…”