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neural network control of fractional-order chaotic systems with unknown control direction

HELIYON 2024年第5期10卷 e26870页

作者： Wang, Suxia Chen, Yong Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Peoples R China Huainan Normal Univ Coll Appl Math Huainan 232038 Peoples R China Sichuan Technol & Business Univ Sch Comp Chengdu 611745 Peoples R China

In this paper, neural network control of fractional order chaotic systems (FOCSs) with input saturation and unknown sign of the controller gain is addressed by employing the Nussbaum function, where neural networks are utilized to model system uncertainties. To get rid of the limitation that reaching phase should be active before sliding motion in the traditional sliding mode control, a stable sliding surface is constructed. Then, by using the integer order Nussbaum gain control method, a novel controller with neural network sliding mode variable structure is designed. Finally, the practicality of the designed method is confirmed by a simulation experiment.

关键词： Fractional-order chaotic system neural network control Chaos synchronization control direction

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Full-state constraints and input backlash-based neural network control of a 2-DOF helicopter system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2025年第2期47卷 209-219页

作者： Bi, Hui Zou, Tao Wu, Lihua Guangzhou Univ Sch Mech & Elect Engn Guangzhou Peoples R China Greatoo Guangzhou Robots & Intelligent Mfg Co Ltd Guangzhou Peoples R China Guangdong Hong Kong Macao Key Lab Multiscale Infor Hong Kong Peoples R China Guangdong Open Univ Robot Inst Guangzhou Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

This paper introduces an adaptive neural network compensatory control approach designed for a 2-degree-of-freedom (2-DOF) helicopter system facing challenges such as input backlash and state constraints. The proposed methodology leverages a radial basis function (RBF) neural network to effectively approximate system uncertainties, mitigating the impact of nonlinear dynamics on control performance. To address the presence of nonlinear input backlash, a compensation technique is introduced to enhance the smoothness of input signals. In addition, for enhanced system safety, a barrier Lyapunov function is integrated to impose restrictions on position and velocity states, resulting in constrained control. Through a rigorous analysis using the Lyapunov direct method, this paper demonstrates the effectiveness of the proposed approach in achieving bounded stability of the system. The validation of the approach is further established through the presentation of simulation and experimental results, showcasing its effectiveness and feasibility in real-world applications.

关键词： 2-DOF helicopter system neural network control RBFNN input backlash state constraints

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Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 215卷 82-97页

作者： Liu, Xiaohua Li, Mengling Zeng, Pengyu Foshan Univ Sch Math & Big Data Foshan 528000 Peoples R China

This paper focuses on the adaptive finite-time neural network control problem for nonlinear stochastic systems with full state constraints. Adaptive controller and adaptive law are designed by backstepping design with log-type barrier Lyapunov function. Radial basis function neural networks are employed to approximate unknown system parameters. It is proved that the tracking error can achieve finite-time convergence to a small region of the origin in probability and the state constraints are confirmed in probability. Different from deterministic nonlinear systems, here the stochastic system is affected by two random terms including continuous Brownian motion and discontinuous Poisson jump process. Therefore, it will bring difficulties to the controller design and the estimations of unknown parameters. A simulation example is given to illustrate the effectiveness of the designed control method.(c) 2023 Published by Elsevier B.V. on behalf of International Association for Mathematics and Computers in Simulation (IMACS).

关键词： Stochastic systems Finite-time boundedness Adaptive control neural network control

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Adaptive neural network control of multiple-sectioned flexible riser with time-varying output constraint and input nonlinearity

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ASIAN JOURNAL OF control 2024年第2期26卷 917-930页

作者： Liu, Fengjiao Yao, Xiangqian Liu, Yu South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China

In this paper, an adaptive neural network controller is proposed for vibration suppression of a multisectional riser system with unknown boundary disturbance, time-varying asymmetric output constraint, and input nonlinearity. The considered riser system is composed of a continuous connection of several different pipes, and its dynamic models are represented by a set of multiple continuously connected partial differential equations (PDEs) and an ordinary differential equation (ODE) at the top boundary. Considering input nonlinearity, external disturbance, and system uncertainty, radial basis function (RBF) neural networks are adopted to eliminate the effect of these uncertain terms. Besides, a barrier Lyapunov function is employed to guarantee the restrictions. With the proposed boundary control, the stability of the closed-loop system is proved and simulations are given to illustrate the well performance of the proposed control strategy.

关键词： adaptive boundary control input nonlinearity multisectional riser neural network control time-varying output constraint

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Event-Triggered neural network control for LTI Systems

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IEEE control SYSTEMS LETTERS 2023年 7卷 1381-1386页

作者： de Souza, C. Tarbouriech, S. Girard, A. Univ Toulouse LAAS CNRS F-31077 Toulouse France Univ Paris Saclay CNRS CentraleSupelec Lab Signaux & Syst F-91190 Gif Sur Yvette France

This letter addresses the problem of event-triggered control (ETC) of discrete-time linear time-invariant (LTI) systems stabilized by neural network controllers. The event-triggering mechanism (ETM) is proposed to update only a portion of the layers required to maintain stability and satisfactory performance of the feedback system, thus reducing the computational cost associated with the evaluation of the neural network. Sufficient convex conditions in the form of linear matrix inequalities (LMIs) are provided to compute the triggering parameters and characterize an estimate of the domain of attraction for the feedback system. The formulation is based on the use of Lyapunov theory and a set of generalized sector constraints that deal with the nonlinear activation functions, represented in this case by the saturation function. Optimization procedures are also formulated to effectively reduce the amount of computation in the neural network. An example borrowed from the literature is used to illustrate the effectiveness of the proposal.

关键词： Event-triggered control neural network control quadratic constraints linear matrix inequalities Lyapunov theory

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Adaptive neural network control of an uncertain 2-DOF helicopter system with input backlash and output constraints

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neural COMPUTING & APPLICATIONS 2022年第20期34卷 18143-18154页

作者： Zhao, Zhijia He, Weitian Yang, Jingfeng Li, ZhiFu Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Guangzhou Inst Ind Intelligence Guangzhou 511458 Peoples R China Chinese Acad Sci Shenyang 110016 Peoples R China

This study considers an adaptive neural control for a two degrees of freedom helicopter nonlinear system preceded by system uncertainties, input backlash, and output constraints. First, a neural network is adopted to handle the hybrid effects of input backlash nonlinearities and system uncertainties. Subsequently, a barrier Lyapunov function is introduced to limit the output signals for further ensuring the safe operation of the system. The bounded stability of the closed-loop system is analyzed employing the direct Lyapunov approach. In the end, the simulation and experiment results are provided to demonstrate the validity and efficacy of the derived control.

关键词： 2-DOF helicopter system neural network control Input backlash Output constraint

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Adaptive neural network control of an Uncertain Nuclear Refueling Machine With Input Backlash and Asymmetric Output Constraint

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IEEE ACCESS 2022年 10卷 124387-124395页

作者： Fu, Yun Gan, Zuoxuan Chen, Bin Huang, Yushui Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Nanchang Univ Sch Future Technol Nanchang 330031 Peoples R China

This paper studies the control problem of an uncertain nuclear refueling machine (RM) system in the presence of uncertainty, external disturbance, input backlash, and asymmetric output constraint. Taking into account the effects of external disturbance and input backlash, the RM system with fuel rod is modeled as a coupling partial differential equation-ordinary differential equation (PDE-ODE). Using the backstepping method, an adaptive neural network control scheme is designed to drive the RM and bridge to the desired positions and simultaneously reduce the vibration of the fuel rod. A novel asymmetric tangent-type barrier Lyapunov function (Tan-BLF) is constructed to restrict the position tracking error into the given range. The formulation of backlash nonlinearity is transformed into the expected input and nonlinear error. Then the combination of input backlash error and boundary disturbance is defined as a disturbance-like item. Applying the robust control strategy and adaptive technique, two auxiliary input signals are proposed to offset the impact of the disturbance-like item. The adaptive neural network (NN) is employed to compensate for the system uncertainty. The practical stability of the RM system with the proposed control is demonstrated via the theoretical Lyapunov analysis. Simulation verifies the performance of the designed control scheme.

关键词： Vibrations Couplings Bridges Adaptation models Adaptive systems Uncertainty Tracking Adaptive control nuclear refueling machine neural network control input backlash output constraint

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A Cooperative neural network control Structure and Its Application for Systems Having Dead-Zone Nonlinearities

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2022年第1期46卷 187-203页

作者： Dincmen, Erkin Isik Univ Mech Engn Dept Istanbul Turkey

An adaptive control structure utilizing two feed-forward neural networks (NN) is proposed to deal with systems having unknown nonlinearities. One of the networks is trained to mimic the nonlinear system dynamics. Its training will be repeated with periods in order to keep it an updated valid model of the system all the times since the parameters and/or nonlinearities of the system may change during time. The other network, which is the controller NN, adapts itself continuously by collaborating with the Model NN. The stability-convergence analysis of both networks is performed via Lyapunov method. An example system is chosen to show the applicability of the control algorithm. This example system is created by combining a linear dynamics model with a dead-zone function to represent a nonlinear system to be controlled. It should be noted that the proposed control structure can be used in any nonlinear system without knowing the system dynamics. The only information required by Model NN is the training set consisting input-output data pairs of the system. The Model NN is trained offline with this training set, and afterward the controller NN adapts its weights online continuously during the control task with the help of Model NN. The performances of PD and PID controllers are also given for comparison purposes.

关键词： neural network control Machine learning Dead zone Adaptive control

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Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

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AUTOMATIKA 2022年第3期63卷 463-473页

作者： Xu, Fengxia Zhang, Xinyu Lu, Zhongda Wang, Shanshan Qiqihar Univ Coll Mech & Elect Engn Qiqihar Peoples R China Collaborat Innovat Ctr Intelligent Mfg Equipment Qiqihar Peoples R China Qiqihar Univ Coll Comp & Control Engn Qiqihar Peoples R China

This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification.

关键词： U-model particle swarm identification extended Kalman filtering neural network control

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AN UPDATE RULE OF PARAMETERS OF ONLINE-OFFLINE INTEGRATED LEARNING METHOD OF neural network control

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND control 2022年第3期18卷 667-685页

作者： Morita, Masakazu Huang, Qingjiu Morishita, Mimpei Kogakuin Univ Elect Engn & Elect Program Grad Sch Engn 1-24-2 Nishi shinjukuShinjuku ku Tokyo 1638677 Japan

In order to deal with load fluctuations and disturbances, and to realize real-time control, we have already proposed an online-offline integrated learning method of neural network control for the position control of servo system in previous study. However, the relationship between the learning rate, the threshold for the number of iterations within the sampling time, and the responsiveness and the robustness of the servo system has not yet been clarified. In this study, the relationship was clarified and formulated from simulation and analysis data, and an update rule for the learning rate and the threshold f or the number of iterations to enable self-tuning was proposed. Specifically, the relationship between the learning rate, the threshold for the number of iterations, and the responsiveness and the robustness of the servo system was clarified from simulation and analysis data. In addition, the relationship between the learning rate, the threshold for the number of iterations, a feedback error, and a settling time was formulated using the least squares method, and the update rule of the learning rate and the threshold for the number of iterations to enable self-tuning was proposed. Moreover, the index values of the feedback error and the settling time were set, and the learning rate and the threshold for the number of iterations were calculated based on the proposed update rule, and the effectiveness of this study was shown from simulation and analysis result before and after changing the load inertia.

关键词： neural network control Online-offline integrated learning Update rule of learning rate and threshold for number of iterations Least squares method Self-tuning

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