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adaptive neural control for a Class of Nonlinear Time-Varying Delay Systems With Unknown Hysteresis

IEEE TRANSACTIONS ON neural NETWORKS AND LEARNING SYSTEMS 2014年第12期25卷 2129-2140页

作者： Liu, Zhi Lai, Guanyu Zhang, Yun Chen, Xin Chen, Chun Lung Philip Guangdong Univ Technol Fac Automat Guangzhou 510006 Guangdong Peoples R China Guangdong Univ Technol Coll Mech & Elect Engn Guangzhou 510006 Guangdong Peoples R China Univ Macau Fac Sci & Technol Macau 99999 Peoples R China

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

关键词： adaptive neural control Bouc-Wen hysteresis nonlinear control nonstrict-feedback system unknown direction hysteresis

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adaptive neural control with fast approximation for uncertain nonlinear systems: A novel composite learning approach

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ASIAN JOURNAL OF control 2023年第6期25卷 4481-4498页

作者： Xie, Jianbin Wang, Shaocong Dai, Hao Jia, Jinping Zhang, Hua Xidian Univ Sch Aerosp Sci & Technol Xian Peoples R China Tianshui Normal Univ Sch Math & Stat Tianshui Peoples R China Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Peoples R China

In existing adaptive neural control approaches, only when the regressor satisfies the persistent excitation (PE) or interval excitation (IE) conditions, the constant optimal weights of neural network (NN) can be identified, which can be used to establish uncertainties in nonlinear systems. This paper proposes a novel composite learning approach based on adaptive neural control. The focus of this approach is to make the NN approximate uncertainties in nonlinear systems quickly and accurately without identifying the constant optimal weights of the NN. Hence, the regressor does not need to satisfy the PE or IE conditions. In this paper, regressor filtering scheme is adopted to generate prediction error, and then the prediction error and tracking error simultaneously drive the update of NN weights. Under the framework of Lyapulov theory, the proposed composite learning approach can ensure that approximation error of the uncertainty and tracking error of the system states converge to an arbitrarily small neighborhood of zero exponentially. The simulation results verify the effectiveness and advantages of the proposed approach in terms of fast approximation.

关键词： adaptive neural control composite learning exponential convergence neural network uncertain nonlinear system

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adaptive neural control of uncertain MIMO nonlinear systems

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IEEE TRANSACTIONS ON neural NETWORKS 2004年第3期15卷 674-692页

作者： Ge, SS Wang, C Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore S China Univ Technol Coll Automat Guangzhou 510641 Peoples R China

In this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO systems consist of interconnected subsystems, with couplings in the forms of unknown nonlinearities and/or parametric uncertainties in the input matrices, as well as in the system interconnections without any bounding restrictions. Using the block-triangular structure properties, the stability analyses of the closed-loop MIMO systems are shown in a nested iterative manner for all the states. By exploiting the special properties of the affine terms of the two classes of MIMO systems, the developed neural control schemes avoid the controller singularity problem completely without using projection algorithms. Semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop of MIMO nonlinear systems is achieved. The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. The proposed schemes offer systematic design procedures for the control of the two classes of uncertain MIMO nonlinear systems. Simulation results are presented to show the effectiveness of the approach.

关键词： adaptive neural control backstepping block-triangular form multi-input/multi-output (MIMO) nonlinear systems neural networks (NN)

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adaptive neural control for MIMO stochastic nonlinear pure-feedback systems with input saturation and full-state constraints

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NEUROCOMPUTING 2018年 275卷 298-307页

作者： Si, Wenjie Dong, Xunde South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Sch Math Guangzhou 510640 Guangdong Peoples R China

This paper investigates an adaptive neural tracking control problem for a class of multi-input multi-output (MIMO) stochastic nonlinear pure-feedback systems with input saturation and full-state constraints. First, the implicit function and mean value theorems are used to overcome the difficulty in the control of pure-feedback systems. Second, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, the neural network is employed to approximate the unknown nonlinearity, and a barrier Lyapunov function is designed to ensure that the full-state variables are restricted. At last, based on Lyapunov stability theory, a robust adaptive neural control method is obtained, which decreases the number of learning parameters and thus reduces the computational burden. It is shown that the designed controller can guarantee that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. The simulation example is given to further illustrate the effectiveness of the proposed approach. (c) 2017 Elsevier B.V. All rights reserved.

关键词： adaptive neural control MIMO stochastic nonlinear Pure-feedback systems Full-state constraints Asymmetric saturation nonlinearity Barrier Lyapunov function

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adaptive neural control of Nonlinear Cyber-Physical Systems Against Randomly Occurring False Data Injection Attacks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第4期53卷 2444-2455页

作者： Zhang, Qiang He, Dakuo Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

In this article, the main contribution is to introduce the adaptive backstepping technique into nonlinear cyber-physical systems against randomly occurring false data injection (ROFDI) attacks. And a new defense strategy based on nonlinear disturbance observer (NDO) is developed, which can not only effectively estimate the external compound disturbance in the presence of the attack, but also improve the robustness of the controlled system. Different from FDI attacks, it is a special case of ROFDI attacks, and the proposed method can deal with ROFDI attacks injected by attackers. Meanwhile, multiple Nussbaum functions are introduced, which overcomes the design difficulty of unknown control directions caused by ROFDI attacks. Furthermore, the approximation of the unknown nonlinear function and the exponential growth problem in the traditional backstepping calculation process are handled by radial basis function neural network and dynamic surface control, respectively. Finally, a new adaptive neural control method based on NDO is proposed to make all signals bounded. Meanwhile, tracking errors and disturbance estimation errors converge on the neighborhood of zero. Numerical and practical examples further illustrate the rationality of the method.

关键词： adaptive neural control cyber-physical systems (CPSs) multiple Nussbaum functions nonlinear disturbance observer (NDO) randomly occurring false data injection (ROFDI) attacks

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adaptive neural control for pure-feedback nonlinear time-delay systems with unknown dead-zone: a Lyapunov-Razumikhin method

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控制理论与应用（英文版） 2013年第1期11卷 18-26页

作者： Zhaoxu YU Jianxu LUO Ji LIU Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology

This paper addresses the problem of adaptive neural control for a class of uncertain pure-feedback nonlinear systems with multiple unknown state time-varying delays and unknown dead-zone. Based on a novel combination of the Razumikhin functional method, the backstepping technique and the neural network parameterization, an adaptive neural control scheme is developed for such systems. All closed-loop signals are shown to be semiglobally uniformly ultimately bounded, and the tracking error remains in a small neighborhood of the origin. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control schemes.

关键词： Pure-feedback nonlinear systems adaptive neural control Razumikhin functional Time-delay Deadzone

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adaptive neural control for Strict-Feedback Nonlinear Systems Without Backstepping

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IEEE TRANSACTIONS ON neural NETWORKS 2009年第7期20卷 1204-1209页

作者： Park, Jang-Hyun Kim, Seong-Hwan Moon, Chae-Joo Mokpo Natl Univ Dept Control Syst Engn Chungnam 534729 South Korea Mokpo Natl Univ Dept Elect Engn Chungnam 534729 South Korea

In this brief, a new adaptive neurocontrol algorithm for a single-input-single-output (SISO) strict-feedback nonlinear system Is proposed. Most of the previous adaptive neural control algorithms for strict-feedback nonlinear systems were based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semiglobal sense.

关键词： adaptive neural control strict-feedback nonlinear system

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adaptive neural control of MIMO Nonlinear Systems With a Block-Triangular Pure-Feedback control Structure

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IEEE TRANSACTIONS ON neural NETWORKS AND LEARNING SYSTEMS 2014年第11期25卷 2017-2029页

作者： Chen, Zhenfeng Ge, Shuzhi Sam Zhang, Yun Li, Yanan Guangdong Polytech Normal Univ Coll Automat Guangzhou 510635 Guangdong Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore Natl Univ Singapore Interact Digital Media Inst Social Robot Lab Singapore 117576 Singapore Univ Elect Sci & Technol China Sch Comp Sci & Engn Inst Robot Chengdu 610054 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore Univ Elect Sci & Technol China Inst Intelligent Syst & Informat Technol Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Inst Robot Chengdu 610054 Peoples R China

This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

关键词： adaptive neural control backstepping coupling multiinput-multioutput (MIMO) nonlinear systems neural networks (NNs)

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adaptive neural control of MIMO stochastic systems with unknown high-frequency gains

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INFORMATION SCIENCES 2017年 418卷 513-530页

作者： Chen, Ci Liu, Zhi Xie, Kan Zhang, Yun Chen, C. L. Philip Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Guangdong Key Lab IoT Informat Technol Guangzhou 510006 Guangdong Peoples R China Univ Macau Fac Sci & Technol Macau Peoples R China

This paper addresses the control problem of MIMO stochastic nonlinear systems with unknown high-frequency gains. In the existing works, the prior knowledge of the high frequency gain or its control direction is assumed known for the control design. This paper removes such assumptions, and proposes an adaptive neural network algorithm that allows the high-frequency gains to be time-varying and their control directions to be unknown. Nussbaum gain based approach and adaptive neural network mechanism are brought together such that all the signals in the closed-loop system are ensured bounded. A simulation study is carried out to confirm the validity of the proposed algorithm. (C) 2017 Elsevier Inc. All rights reserved.

关键词： High-frequency gains Unknown control direction MIMO stochastic system adaptive neural control

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adaptive neural control of non-linear fractional order multi-agent systems in the presence of error constraints and input saturation

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IET control THEORY AND APPLICATIONS 2022年第13期16卷 1283-1298页

作者： Mohammadzamani, Fatemeh Hashemi, Mahnaz Shahgholian, Ghazanfar Islamic Azad Univ Dept Elect Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Digital Proc & Machine Vis Res Ctr Najafabad Branch Najafabad Iran

This paper presents a tracking control problem for fractional order multi-agent systems in the presence of uncertainties. The study takes into account the problems of error constraints, saturated inputs and transient response suitability. Because of the existing challenges, a neural adaptive control approach is used in this study. Fractional order multi-agent systems are investigated with uncertainty in the presence of error constraints. A controller is designed on the basis of adaptive control and dynamic surface control. As a result, the control objective of pursuing the desired output is achieved in the presence of required constraints. The effective performance of the proposed controller is demonstrated by simulation using MATLAB software.

关键词： tracking control controller design adaptive neural control neurocontrollers control system synthesis multi-agent systems adaptive control dynamic surface control nonlinear fractional order multiagent systems uncertain systems input saturation saturated inputs error constraints transient response nonlinear control systems

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