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Self-triggered adaptive neural control for USVs with sensor measurement sensitivity under deception attacks

JOURNAL OF FIELD ROBOTICS 2025年第1期42卷 153-168页

作者： Wu, Chen Zhu, Guibing Liu, Yongchao Li, Feng Zhejiang Ocean Univ Sch Naval Architecture & Maritime Zhoushan 316022 Zhejiang Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Shandong Peoples R China Donghai Nav Safety Adm Shanghai AIDS Nav Dept Shanghai Peoples R China

This article investigates the control problem of unmanned surface vessels with sensor measurement sensitivity under deception attacks, and proposes a novel self-triggered adaptive neural control scheme under the backstepping design framework. To solve the control design problem of unknown time-varying gains caused by deception attacks and measurement sensitivity in kinematic and kinetic channels, the parameter adaptive and neural network technology are involved. In addition, to decrease actuator wear caused by the high-frequency wave and sensor measurement sensitivity and reduce the computational burden caused by continuous monitoring of the triggered condition, a self-triggered mechanism is constructed in the controller-actuator channel. Finally, a self-triggered adaptive neural control solution is proposed, which can guarantee that all signals in the whole closed-loop system are bounded by theoretical analysis. The effectiveness and superiority are verified by numerical simulations.

关键词： adaptive neural control deception attacks measurement sensitivity self-triggered control unmanned surface vessels

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Direct adaptive neural control for precision piezoelectric actuator without hysteresis compensation

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第5期239卷 1702-1712页

作者： Son, Nguyen Ngoc Van Kien, Cao Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam

This paper introduces a novel Direct adaptive neural control (DANC) approach to enhance the precision of piezoelectric actuator motion control significantly. By effectively addressing the complexities of hysteresis, DANC simplifies system design while delivering superior performance. A Radial Basis Function neural Network (RBFNN) is employed to handle system uncertainties robustly, and a switching control mechanism guarantees stability, rigorously verified through Lyapunov analysis. Experimental results using a Thorlabs PZS001 actuator convincingly demonstrate substantial performance improvements over conventional PID control, the feedforward-based neural network and feedback control (FF-FB control).

关键词： adaptive neural control Lyapunov stability theory piezoelectric actuator

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Nonlinear adaptive neural control of Power Converter-Driven DC Motor System: Design and Experimental Validation

ENGINEERING REPORTS

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ENGINEERING REPORTS 2025年第1期7卷

作者： Nizami, Tousif Khan Gangula, Sasank Das Udumula, Ramanjaneya Reddy Chakravarty, Arghya Ahmad, Fareed Hosseinpour, Alireza SRM Univ AP Sch Engn & Sci Dept Elect & Elect Engn Amaravati India SND Coll Engn & Res Ctr Dept Elect Engn Nasik India Univ Zabol Dept Elect Engn Zabol Iran

This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter-driven DC motor system. The proposed technique integrates an adaptive polynomial-neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network-based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady-state performance. The stability of the entire closed-loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start-up, step-reference tracking, and external step-load torque disturbances. The real-time experimentation is conducted on a laboratory prototype of power converter-driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to 50%$$ 50\% $$ and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

关键词： adaptive neural control DC motor system disturbance rejection experimental validation trajectory tracking

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Dual-triggered scheme for adaptive neural control of MIMO nonlinear switched systems against sensor and actuator attacks

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2025年 147卷

作者： Zhao, Zixiang Zhang, Liang Yang, Shuyi Zhao, Ning Liu, Yongchao Bohai Univ Coll Control Sci & Engn Jinzhou 121013 Liaoning Peoples R China Bohai Univ Coll Innovat & Entrepreneurship Jinzhou 121013 Liaoning Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

A dual-triggered adaptive neural control scheme for a class of multiple-input multiple-output (MIMO) switched nonlinear cyber-physical systems under dual-channel deception attack is proposed in this paper. The dual-triggered mechanism for both sensor-to-controller and controller-to-actuator channels within the traditional backstepping framework is designed to effectively reduce the communication and computational burdens, while ensuring the Zeno behaviour is avoided. In addition, deception attacks on sensors and actuators are addressed by the designed event-triggered virtual controller and adaptive neural event-triggered controller. The results indicate that the signals of the overall closed-loop system can be bounded by using the developed dual-triggered adaptive neural control scheme. Finally, numerical and practical engineering examples are provided to validate the effectiveness of the scheme.

关键词： Dual-triggered Deception attacks Backstepping adaptive neural control Multiple-input-multiple-output Cyber-physical systems

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adaptive neural control of nonlinear periodic time-varying parameterized mixed-order multi-agent systems with unknown control coefficients

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Science China(Technological Sciences) 2022年第8期65卷 1675-1684页

作者： CHEN JiaXi CHEN WeiSheng LI JunMin ZHANG Shuai School of Mathematics and Statistics Xidian UniversityXi’an 710071China School of Aerospace Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Antennas and Microwave Laboratory Xidian UniversityXi’an 710071China

In this paper, we first consider the adaptive leader-following consensus problem for a class of nonlinear parameterized mixedorder multi-agent systems with unknown control coefficients and time-varying disturbance parameters of the same period. neural networks and Fourier series expansions are used to describe the unknown nonlinear periodic time-varying parameterized function.A distributed control protocol is designed based on adaptive control, matrix theory, and Nussbaum function. The robustness of the distributed control protocol is analyzed by combining the stability analysis theory of closed-loop systems. On this basis, this paper discusses the case of time-varying disturbance parameters with non-identical periods, expanding the application scope of this control protocol. Finally, the effectiveness of the algorithm is verified by a simulation example.

关键词： adaptive neural control unknown control coefficients mixed-order multi-agent systems periodic time-varying disturbances nonlinearly parameterized dynamics

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adaptive neural control of Nonaffine Systems With Unknown control Coefficient and Nonsmooth Actuator Nonlinearities

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IEEE TRANSACTIONS ON neural NETWORKS AND LEARNING SYSTEMS 2015年第8期26卷 1822-1827页

作者： Yang, Zaiyue Yang, Qinmin Sun, Youxian Zhejiang Univ State Key Lab Ind Control Technol Dept Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the original nonaffine nonlinear system into an equivalent affine one. Then, to deal with the unknown dynamics and unknown control coefficient contained in the affine system, online approximator and Nussbaum gain techniques are utilized in the controller design. It is proven rigorously that asymptotic convergence of the tracking error and ultimate uniform boundedness of all the other signals can be guaranteed by the proposed control method. The control feasibility is further verified by numerical simulations.

关键词： adaptive neural control nonaffine systems nonsmooth nonlinearity unknown control coefficient

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adaptive neural control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2011年第6期41卷 1681-1692页

作者： Wang, Min Liu, Xiaoping Shi, Peng S China Univ Technol Coll Automat Guangzhou 510641 Guangdong Peoples R China Lakehead Univ Fac Engn Thunder Bay ON P7B 5E1 Canada Univ Glamorgan Dept Comp & Math Sci Pontypridd CF37 1DL M Glam Wales Victoria Univ Sch Engn & Sci Melbourne Vic 8011 Australia

This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid "the explosion of complexity" in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov-Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.

关键词： adaptive neural control dynamic surface control (DSC) nonlinear time-delay systems pure-feedback systems

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adaptive neural control for a class of stochastic nonlinear systems with unknown parameters, unknown nonlinear functions and stochastic disturbances

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NEUROCOMPUTING 2017年 226卷 101-108页

作者： Chen, Chao-Yang Gui, Wei-Hua Guan, Zhi-Hong Wang, Ru-Liang Zhou, Shao-Wu Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410012 Hunan Peoples R China Huazhong Univ Sci & Technol Coll Automat Wuhan 430074 Peoples R China Guangxi Teachers Educ Univ Comp & Informat Engn Coll Nanning 530001 Peoples R China

In this paper, adaptive neural control (ANC) is investigated for a class of strict-feedback nonlinear stochastic systems with unknown parameters, unknown nonlinear functions and stochastic disturbances. The new controller of adaptive neural network with state feedback is presented by using a universal approximation of radial basis function neural network and backstepping. An adaptive neural network state-feedback controller is designed by constructing a suitable Lyapunov function. adaptive bounding design technique is used to deal with the unknown nonlinear functions and unknown parameters. It is shown that the global asymptotically stable in probability can be achieved for the closed-loop system. The simulation results are presented to demonstrate the effectiveness of the proposed control strategy in the presence of unknown parameters, unknown nonlinear functions and stochastic disturbances.

关键词： Unknown parameters Stochastic disturbances Unknown nonlinear functions Stochastic nonlinear adaptive neural control

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adaptive neural control for non-strict feedback stochastic nonlinear systems with input delay

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2024年第1期46卷 104-115页

作者： Zheng, Xiao-Yi Southeast Univ Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing Peoples R China Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China

In this paper, the problem of adaptive control is investigated for a class of non-strict feedback stochastic nonlinear systems with input delay. First, the effect of the input delay is eliminated by constructing an appropriate auxiliary system with the same order as the considered system. Then, with the help of the backstepping technique and the structural characteristics of the radial basis function (RBF) neural network (NN), an adaptive neural control scheme is extended to non-strict feedback stochastic nonlinear systems with input delay, in which uncertain nonlinear functions are approximated by RBF NN. Furthermore, the proposed adaptive controller ensures that all the closed-loop signals remain bounded in probability. Finally, two examples are provided to confirm the effectiveness of the designed strategy.

关键词： adaptive neural control non-strict feedback stochastic nonlinear system input delay auxiliary system

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adaptive neural control for a general class of pure-feedback stochastic nonlinear systems

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NEUROCOMPUTING 2014年 135卷 348-356页

作者： Wang, Huanqing Liu, Xiaoping Liu, Kefu Chen, Bing Lin, Chong Bohai Univ Sch Math & Phys Jinzhou 121000 Liaoning Peoples R China Lakehead Univ Fac Engn Thunder Bay ON P7B 5E1 Canada Qingdao Univ Inst Complex Sci Qingdao 266071 Shandong Peoples R China Liaoning Univ Sci & Technol Fac Elect & Informat Engn Anshan 114051 Liaoning Peoples R China

In this paper, the problem of the adaptive neural control is considered for a class of pure-feedback stochastic nonlinear systems. Based on the radial basis function (RBF) neural networks' universal approximation property, an adaptive neural controller is developed via backstepping technique. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are bounded in the sense of mean quartic value. Compared with the existing results on adaptive control of stochastic pure-feedback nonlinear systems, the main novelty of this note is that a systematic design procedure is presented for a class of pure-feedback stochastic nonlinear systems with a more general form of the diffusion term. Simulation results are presented to demonstrate the effectiveness of the proposed scheme. (C) 2014 Elsevier B.V. All rights

关键词： adaptive neural control Stochastic pure-feedback nonlinear systems Backstepping

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