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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

Event-Triggered adaptive neural control for Multiagent Systems with Deferred State Constraints

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Journal of Systems Science & Complexity 2022年第3期35卷 973-992页

作者： YANG Bin CAO Liang XIAO Wenbin YAO Deyin LU Renquan School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative control Guangdong University of TechnologyGuangzhou 510006China

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints.A distributed eventtriggered adaptive neural control approach is *** virtue of a distributed sliding-mode estimator,the leader-following consensus control problem is converted into multiple simplified tracking control ***,a shifting function is utilized to transform the error variables such that the initial tracking condition can be totally unknown and the state constraints can be imposed at a specified time ***,the deferred asymmetric time-varying full state constraints are addressed by a class of asymmetric barrier Lyapunov *** order to reduce the burden of communication,a relative threshold event-triggered mechanism is incorporated into controller and Zeno behavior is *** on Lyapunov stability theorem,all closed-loop signals are proved to be semi-globally uniformly ultimately ***,a practical simulation example is given to verify the presented control scheme.

关键词： adaptive neural control deferred time-varying state constraints event-triggered mechanism multiagent systems sliding-mode estimator

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

adaptive neural control based on HGO for hypersonic flight vehicles

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Science China(Information Sciences) 2011年第3期54卷 511-520页

作者： XU Bin 1,2 , GAO DaoXiang 3 & WANG ShiXing 1 1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China 2 Autonomous System Lab, ETH Zurich Zurich 8092, Switzerland 3 School of Technology, Beijing Forestry University, Beijing 100083, China Department of Computer Science and Technology Tsinghua University Beijing China Autonomous System Lab ETH Zurich Zurich Switzerland School of Technology Beijing Forestry University Beijing China

This paper describes the design of adaptive neural controller for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV) which are decomposed into two functional systems, namely the altitude subsystem and the velocity subsystem. For each subsystem, one adaptive neural controller is investigated based on the normal output-feedback formulation. For the altitude subsystem, the high gain observer (HGO) is taken to estimate the unknown newly defined states. Only one neural network (NN) is employed to approximate the lumped uncertain system nonlinearity during the controller design which is considerably simpler than the ones based on back-stepping scheme with the strict-feedback form. The Lyapunov stability of the NN weights and filtered tracking error are guaranteed in the semiglobal sense. Numerical simulation study of step response demonstrates the effectiveness of the proposed strategy in spite of system uncertainty.

关键词： adaptive neural control hypersonic flight vehicle high gain observer output-feedback

Novel adaptive neural control of flexible air-breathing hypersonic vehicles based on sliding mode differentiator

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Chinese Journal of Aeronautics 2015年第4期28卷 1209-1216页

作者： Bu Xiangwei Wu Xiaoyan Ma Zhen Zhang Rui Air and Missile Defense College Air Force Engineering University

A novel adaptive neural control strategy is exploited for the longitudinal dynamics of a generic flexible air-breathing hypersonic vehicle（FAHV）.By utilizing functional decomposition method, the dynamics of FAHV is decomposed into the velocity subsystem and the altitude *** each subsystem, only one neural network is employed for the unknown function *** further reduce the computational burden, minimal-learning parameter（MLP）technology is used to estimate the norm of ideal weight vectors rather than their *** introducing sliding mode differentiator（SMD） to estimate the newly defined variables, there is no need for the strict-feedback form and virtual *** the developed control law is considerably simpler than the ones derived from back-stepping ***, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties and varying disturbances.

关键词： adaptive neural control Flexible air-breathing hyper-sonic vehicle (FAHV) Flexible effects Minimal-learning parameter(MLP) Sliding mode differentiator(SMD)

Typical adaptive neural control for hypersonic vehicle based on higher-order filters

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Journal of Systems Engineering and Electronics 2020年第5期31卷 1031-1040页

作者： ZHAO Hewei LI Rui Shore Guard Institute Naval Aviation UniversityYantai 264001China Wenjing College Yantai UniversityYantai 264005China

A typical adaptive neural control methodology is used for the rigid body model of the hypersonic vehicle. The rigid body model is divided into the altitude subsystem and the velocity subsystem. The proportional integral differential(PID) controller is introduced to control the velocity track. The backstepping design is applied for constructing the controllers for the altitude *** avoid the explosion of differentiation from backstepping, the higher-order filter dynamic is used for replacing the virtual controller in the backstepping design steps. In the design procedure,the radial basis function(RBF) neural network is investigated to approximate the unknown nonlinear functions in the system dynamic of the hypersonic vehicle. The simulations show the effectiveness of the design method.

关键词： hypersonic vehicle adaptive neural control higher-order filter differential explosion

adaptive neural control for Nonlinear Distributed Time-Delay Systems with Delay-Dependent control Gains

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Adaptive Neural Control for Nonlinear Distributed Time-Delay...

adaptive neural control of Permanent Magnet Synchronous Motor

第二十九届中国控制会议

作者： HAN Thanh-Trung GE Shuzhi Sam Institute of Intelligent Systems and Information Technology(ISIT) and School of Automation Engineering University of Electronic Science and Technology of China Institute of Intelligent Systems and Information Technology(ISIT) and Robotics Institute University of Electronic Science and Technology of China

This paper addresses the problem of output tracking control for a class of nonlinear distributed time-delay systems whose control gains are delay-dependent and whose uncertainty involves both delay times and system *** intro-duce the framework of using delay-independent control to deal with unknown distributed delay dependence and using switching-based adaptive neural control to deal with unknown delay-dependent control *** control is designed in consideration of a Razumikhin norm condition on retarded functional state entailing efficiency with distributed delay dependence.

关键词： adaptive neural control Distributed Time-delay Systems

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Adaptive Neural Control of Permanent Magnet Synchronous Moto...

Decentralized Steam-Valving adaptive neural control of Multi-machine Power Systems

第二十四届中国控制会议

作者： Tengfei Liu, Cong Wang(South China University of Technology, Guangzhou 510640)

＜正＞ In this paper, an adaptive neural control scheme is presented for speed control of a permanent magnet synchronous motor （PMSM） with unknown dynamics and parameters considered. Simulation results clearly show that with the proposed scheme, the motor speed and the d-axis current can track the desired reference signals successfully.

关键词： adaptive neural control permanent magnet synchronous motor speed controller multi-input/multi-output (MIMO) nonlinear system uncertainties

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Decentralized Steam-Valving Adaptive Neural Control of Multi...

Deterministic learning-based neural output-feedback control for a class of nonlinear sampled-data systems

第25届中国控制会议

作者： Zhong Xiurong, Wang Cong, Liu Yuelong control and Optimization Center &. College of Automation South China University of Technology, Guangzhou 510641

＜正＞In this paper, an adaptive neural control scheme is proposed for multi-machine steam-valving power systems. The multi-machine steam-valving power systems consist of interconnected subsystems, with coupling in the form of unknown nonlinearities and parametric uncertainties. Using the adaptive neural control method in, the coupling problem between subsystems can be solved without linearization treatment. Semi-global ultimate boundedness of all the signals in the closed-loop system are obtained. The outputs of the closed-loop system are proven to converge to a small neighborhood of the desired trajectories. Simulation results of two-machine power systems are presented as an example to show the effectiveness of the approach.

关键词： adaptive neural control Multimachine power systems Steam-valving control Backstepping

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Science China(Information Sciences) 2024年第9期67卷 164-183页

作者： Yu ZENG Fukai ZHANG Tianrui CHEN Cong WANG School of Automation Science and Engineering South China University of Technology School of control Science and Engineering Shandong University

This study investigates the deterministic learning(DL)-based output-feedback neural control for a class of nonlinear sampled-data systems with prescribed performance(PP). Specifically, first, a sampleddata observer is employed to estimate the unavailable system states for the Euler discretization model of the transformed system dynamics. Then, based on the observations and backstepping method, a discrete neural network(NN) controller is constructed to ensure system stability and achieve the desired tracking performance. The noncausal problem encountered during the controller deduction process is resolved using a command filter. Moreover, the regression characteristics of the NN input signals are demonstrated with the observed states. This ensures that the radial basis function NN, based on DL theory, meets the partial persistent excitation condition. Subsequently, a class of discrete linear time-varying systems is proven to be exponentially stable, achieving partial convergence of neural weights to their optimal/actual values. Consequently, accurate modeling of unknown closed-loop dynamics is achieved along the system trajectory from the output-feedback control. Finally, a knowledge-based controller is developed using the modeling *** controller not only enhances the control performance but also ensures the PP of the tracking error. The effectiveness of the scheme is illustrated through simulation results.

关键词： deterministic learning neural networks adaptive neural control predefined performance nonlinear sampled-data systems