Directly adaptive fuzzy control of discrete-time chaotic systems by least squares algorithm with dead-zone

As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine) neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine) neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control) will be used to improve the control performance. Simulation results are included to complement the theoretical results.

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“…For the detailed proof of Theorem 1, readers can refer to [14]; it can also be found in the appendix.…”

Section: Adaptive Control By Using Os-elm Neural Networkmentioning

confidence: 99%

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

Jia

Liu

et al. 2014

Abstract and Applied Analysis

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“…The first approach for achieving the control objective is to design a sampling time sequence and SDC protocol to control the discrete-time plant model obtained from the continuous-time system. This control theory has been widely used in References 18,[20][21][22] For example, in Reference 18, the authors applied this approach to propose a general framework, which was used to stabilize the approximate discrete-time models of the nonlinear sampled-data differential inclusions. In Reference 22, the authors designed a nonlinear discrete-time compensation term to enhance the tracking quality and stability of conventional ACT for the discrete-time EL systems.…”

Section: Introductionmentioning

confidence: 99%

Sampled‐data robust practical tracking of Euler‐Lagrange systems with an uncertain exosystem

Zeng,

Liu

2023

Intl J Robust & Nonlinear

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This article focuses on the problem of sampled‐data practical tracking for Euler‐Lagrange systems subject to uncertain parameters. We assume that the system matrix of the exosystem and the Euler‐Lagrange system both contain unknown parameters, which is clearly more practical. The existence of unknown system parameters invalidates existing control strategies and poses a major challenge to the solvability of the problem. With the help of the internal model principle and the adaptive control technology, we propose a novel sampled‐data dynamic compensator to overcome the challenge of unknown parameters in exosystems. In particular, a virtual sampling adaptive control input is proposed to deal with uncertainties in the exosystem matrix. Then, a sampled‐data control law is constructed to guarantee that, by any fast predefined rate, the tracking error exponentially approaches any small predefined ranges of the origin, thus ensure the tracking performance. Finally, we apply our result to a three‐link robot manipulator system.

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New results on synchronization of chaotic systems with time-varying delays via intermittent control

Cai

Hao

et al. 2011

Nonlinear Dyn

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This paper is concerned with the problem of exponential synchronization for chaotic systems with time-varying delays by using periodically intermittent control. Some new and useful synchronization criteria are obtained based on the differential inequality method and the analysis technique. It is noteworthy that the methods used in this paper are different from the techniques employed in the existing works, and the derived conditions are less conservative. Especially, a strong constraint on the control width that the control width should be large than the time delay imposed by the current references is released in this paper. Moreover, the new synchronization criteria do not impose any restriction on the size of time delay. Numerical examples are finally presented to illustrate the effectiveness of the theoretical results.

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Directly adaptive fuzzy control of discrete-time chaotic systems by least squares algorithm with dead-zone

Cited by 7 publications

References 22 publications

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

Sampled‐data robust practical tracking of Euler‐Lagrange systems with an uncertain exosystem

New results on synchronization of chaotic systems with time-varying delays via intermittent control

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