Prescribed performance synchronization for uncertain chaotic systems with input saturation based on neural networks

This paper investigates the impulsive synchronization scheme of fractional-order chaotic systems with actuator saturation and control gain error. Based on the theory of fractional order system and impulsive differential system, discontinuous Lyapunov stability and matrix inequality approach, some new sufficient conditions are derived to guarantee the impulsive synchronization of a general class of fractional order chaotic systems. It is worth mentioning that the actuator saturation and control gain error are discussed simultaneously, which is more rigorous and practical in real systems. Finally, some simulation results verify the correctness and effectiveness of the theoretical results. INDEX TERMS Fractional-order chaotic systems, impulsive synchronization, control gain error, actuator saturation.

Section: Introductionmentioning

confidence: 99%

Impulsive Synchronization of Fractional-Order Chaotic Systems With Actuator Saturation and Control Gain Error

Luo

Zhang

et al. 2020

“…Synchronization chaotic systems have attracted great interest because of their potential applications in various fields such as electronics, signal processing, secure communication, aerospace, and biological systems [70][71][72][73][74][75][76][77]. Synchronization of chaotic systems is the control of the slave system to mimic the behavior of the master system.…”

Section: Introductionmentioning

confidence: 99%

A Modified Grey Wolf Optimizer for Optimum Parameters of Multilayer Type-2 Asymmetric Fuzzy Controller

Lin

Hong

2020

This study presents a modified algorithm of the grey wolf optimizer to solve the problem of learning rate selection in the multilayer type-2 asymmetric fuzzy controller (MT2AFC). The improvements of our modified optimizer are: the best position of the swarm is memorized, thus making the alpha wolves only update when a better position appears in the next iteration; search performance is enhanced by giving more freedom to update the grey wolf position. The proposed optimizer algorithm is then applied to optimize the suitable learning rates for the proposed controller. The multilayer type-2 asymmetric membership function is used in the fuzzy control network to enhance the learning ability and flexibility of the designed network architecture. The gradient descent method is used to adjust the parameters of the proposed MT2AFC controller online. The stability of the system is guaranteed using the Lyapunov approach. Besides, the self-evolving algorithm is used to construct the network structure autonomously. Ultimately, the numerical simulations of the chaotic synchronization systems are carried out to verify the effectiveness of our proposed method. INDEX TERMS grey wolf optimizer, interval type-2 fuzzy neural network, asymmetric membership function, chaotic synchronization systems.

“…The synchronization of chaotic systems is a fundamental problem in nonlinear science that has attracted the attention of many researchers in various fields. Many approaches for synchronizing chaotic systems have been proposed in the past, such as the fuzzy controller, neural network controller, robust controller, and CMAC controller [39][40][41]. Synchronization can be defined as the process where two or more chaotic systems are coupled or when a chaotic system drives another chaotic system [42].…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of Function-Link Type-2 Fuzzy Asymmetric CMAC Based on Self-Organizing Algorithm and Modified Jaya Algorithm

Quynh

Long

et al. 2020

In this work, a novel self-organizing interval type-2 fuzzy cerebellar model articulation controller design for chaotic systems synchronization is presented. A function-link network and an asymmetric-membership function are employed to improve the learning ability and flexibility of the proposed network. The gradient-descent method is used to derive the adaptive laws for online updating the network parameters. Suitable learning rates for adaptive laws can be achieved by employing the modified Jaya algorithm. Furthermore, the proposed self-organizing algorithm is used to generate new fuzzy rules or delete less-active fuzzy rules. The control system's exponential stability is analyzed by applying the Lyapunov theory. Finally, numerical simulations on the synchronization of four-dimensional chaotic systems (considering external disturbances and system uncertainties) are conducted to validate the efficiency and performance of the proposed method. INDEX TERMS self-organizing algorithm, type-2 fuzzy CMAC, Jaya algorithm, function-link network, asymmetric membership function.