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Guidance, Navigation and Control 2023年第2期3卷 82-111页

作者： Maolong Lv Chuhan Zhou Air Traffic Control and Navigation College Air Force Engineering University Xi'an ShaanriP.R.China

This paper proposes a novel neural adaptive performance-constrained synchronization tracking control algorithm for multiple hypersonic flight vehicles(HFVs),which are subject to actuator faults and full-state constraints.The proposed method is based on advanced Lyapunov finite-time stability theory and a sophisticated backstepping design scheme.The longitudinal model of HFV is converted into velocity and altitude subsystems through functional decomposition.Our method presents three significant contributions over the existing state-of-the-art approaches:(a)ensuring finite-time convergence of HFVs systems by guaranteeing that the setting time is lower bounded by a positive constant that is related to the initial states;(b)utilizing a tan-type Barrier Lyapunov function(BLF)to ensure that the synchronization tracking errors of velocity,altitude,flight path angle,angle of attack,and pitch angle rate are maintained within certain performance bounds;and(c)designing a neural adaptive control algorithm and adaptive parameter laws by combining the backstepping design technique and radial basisfunction neural networks(RBFNNs)to handle unknown actuator faults and modeling uncer-tainties.Finally,comparative simulations are conducted to validate the efficacy of the proposed scheme.

关键词： Hypersonic flight vehicles(HFVs) finite-time convergence state constraints neural adaptive synchronization control actuator faults.

finite-time convergence Disturbance Rejection Control for a Flexible Timoshenko Manipulator

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 157-168页

作者： Zhijia Zhao Zhijie Liu the School of Mechanical and Electrical Engineering Guangzhou UniversityGuangzhou 510006China the School of Automation and Electrical Engineering University of Science and Technology Beijing(USTB)Beijing 100083 Shunde Graduate School of USTB Foshan 528300China

This paper focuses on a new finite-time convergence disturbance rejection control scheme design for a flexible Timoshenko manipulator subject to extraneous disturbances.To suppress the shear deformation and elastic oscillation,position the manipulator in a desired angle,and ensure the finitetime convergence of disturbances,we develop three disturbance observers(DOs)and boundary controllers.Under the derived DOs-based control schemes,the controlled system is guaranteed to be uniformly bounded stable and disturbance estimation errors converge to zero in a finite time.In the end,numerical simulations are established by finite difference methods to demonstrate the effectiveness of the devised scheme by selecting appropriate parameters.

关键词： Disturbance observer disturbance rejection control finite-time convergence Timoshenko manipulator vibration control

Target Tracking Algorithm Using finite-time convergence Smooth Second-order Sliding Mode Controller for Mobile Robots

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Chinese Journal of Mechanical Engineering 2011年第3期24卷 414-419页

作者： GE Lianzheng ZHAO Lijun GAO Tong State Key Laboratory of Robotics Technique and System Harbin Institute of Technology Harbin 150001 China

Target tracking control for wheeled mobile robot （WMR） need resolve the problems of kinematics model and tracking algorithm.High-order sliding mode control is a valid method used in the nonlinear tracking control system,which can eliminate the chattering of sliding mode control.Currently there lacks the research of robustness and uncertain factors for high-order sliding mode control.To address the fast convergence and robustness problems of tracking target,the tracking mathematical model of WMR and the target is derived.Based on the finite-time convergence theory and second order sliding mode method,a nonlinear tracking algorithm is designed which guarantees that WMR can catch the target in finite time.At the same time an observer is applied to substitute the uncertain acceleration of the target,then a smooth nonlinear tracking algorithm is proposed.Based on Lyapunov stability theory and finite-time convergence,a finite time convergent smooth second order sliding mode controller and a target tracking algorithm are designed by using second order sliding mode method.The simulation results verified that WMR can catch up the target quickly and reduce the control discontinuity of the velocity of WMR.

关键词： mobile robots second order sliding mode finite-time convergence observer

finite-time convergence of an nth nonlinear system with unmatched disturbance and its application in integrated guidance and control system

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第5期9卷 59-73页

作者： Cong Zhang Commanding Automation Technique R&D and Application Center the 4th Research Institute of China Aerospace Science and Industry Corporation BeijingP.R.China

The finite-time convergence problem of an nth nonlinear system with unmatched disturbance is primarily studied in this paper. During the recursive procedure, a new finite-timecontroller is designed and proven by adding a sign function and a power integrator. Meanwhile, a C1 positive definite and proper Lyapunov function, which satisfies the finite-timeLyapunov stability law, is designed. Finally, the designed finite-time controller is appliedto some examples and an application of integrated guidance and control system to testand verify its advantage and practicability.

关键词： finite-time convergence unmatched disturbance high-order nonlinear system recursive algorithm integrated guidance and control.

A variable structure passivity control method for elastic joint robots based on cascaded high-order state estimation

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Science China(Technological Sciences) 2024年第2期67卷 395-407页

作者： ZHANG JieXin NIE PingYun ZHANG Bo State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai200240China

Passivity-based controllers are widely used to facilitate physical interaction between humans and elastic joint robots,as they enhance the stability of the interaction system.However,the joint position tracking performance can be limited by the structures of these controllers when the system is faced with uncertainties and rough high-order system state measurements(such as joint accelerations and jerks).This study presents a variable structure passivity(VSP)control method for joint position tracking of elastic joint robots,which combines the advantages of passive control and variable structure control.This method ensures the tracking error converges in a finite time,even when the system faces uncertainties.The method also preserves the passivity of the system.Moreover,a cascaded observer,called CHOSSO,is also proposed to accurately estimate high-order system states,relying only on position and velocity signals.This observer allows independent implementation of disturbance compensation in the acceleration and jerk estimation channels.In particular,the observer has an enhanced ability to handle fast time-varying disturbances in physical human-robot interaction.The effectiveness of the proposed method is verified through simulations and experiments on a lower limb rehabilitation robot equipped with elastic joints.

关键词： motion control elastic joint robot finite-time convergence high-order system state estimation physical human-robot interaction

finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

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Global Energy Interconnection 2023年第3期6卷 355-362页

作者： Yubin Jia Tengjun Zuo Yaran Li Wenjun Bi Lei Xue Chaojie Li School of Automation Southeast UniversityNanjing 210096P.R.China Nanjing Institute of Technology Nanjing 211167P.R.China State Grid Jiangsu Electric Power Research Institute Nanjing 211103P.R.China School of Electrical Engineering and Telecommunications University of New South WalesSydneyNSW 2052Australia

This paper presents a finite-time economic model predictive control(MPC)algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems.Economic MPC can be used in a power system to ensure frequency stability,real-time economic optimization,control of the system and optimal load dispatch from it.A generalized terminal penalty term was used,and the finite-time convergence of the system was guaranteed.The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system,which had two areas connected by an AC tie line.The simulation results demonstrated the effectiveness of the algorithm.

关键词： Economic model predictive control finite-time convergence Optimal load dispatch Frequency stability

A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1778-1791页

作者： Ning Tan Peng Yu Zhiyan Zhong Fenglei Ni IEEE the School of Computer Science and Engineering&Key Laboratory of Machine Intelligence and Advanced Computing Ministry of EducationSun Yat-sen UniversityGuangzhou 510006China the School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China the State Key Laboratory of Robotics and Systems Harbin Institute of Technology(HIT)Harbin 150080China

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

关键词： Dual zeroing neural networks(ZNN) finite-time convergence model-free robot control robustness analysis

Modified rolling guidance law for single moving mass controlled reentry vehicle against maneuvering target with impact angle constraints

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Chinese Journal of Aeronautics 2022年第6期35卷 226-239页

作者： Guanlin LI Ming YANG Songyan WANG Tao CHAO Control and Simulation Center Harbin Institute of TechnologyHarbin 150001China

This paper deals with the problem of guidance law design for the single moving mass controlled reentry vehicle when impact angle constraints and maneuvering target are taken into consideration.More specifically,a modified rolling guidance law is proposed with the interactive virtual target and the landing point prediction strategy.First,considering the fact that the roll channel can be controlled directly,the relative motion between the single moving mass controlled reentry vehicle and the target is described by the error angle between the relative velocity and the line-ofsight.Second,a nonlinear error angle command is given to reduce the rotation rate.To satisfy impact angle constraints,an interactive virtual target is presented and the‘‘S”formed velocity of the virtual target is given to abate the error angle tracking difficulty at the final stage of the reentry phase.Then,the landing point prediction strategy is employed and the motion variation trend is also taken into consideration.As the maneuvering target is replaced with the predicted landing point,the error angle tracking difficulty caused by the target velocity decreases,which is helpful to meet impact angle constraints and improve guidance accuracy at the same time.Finally,the finite-time rolling guidance law is proposed and proved via Lyapunov stability theorem.Compared with the existing method,lower-speed rotation,smaller missing distance and less impact angle errors are obtained,which can be demonstrated by numerical simulations.

关键词： Reentry vehicles Guidance law Impact angle Maneuvering target finite-time convergence

Integral terminal sliding mode control for nonlinear systems

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Journal of Systems Engineering and Electronics 2018年第3期29卷 571-579页

作者： GUO Jianguo LIU Yuchao ZHOU Jun Institute of Precision Guidance and Control Northwestern Polytechnical University

This paper proposes a fast integral terminal sliding mode（ITSM） control method for a cascaded nonlinear dynamical system with mismatched uncertainties. Firstly, an integral terminal sliding mode surface is presented, which not only avoids the singularity in the traditional terminal sliding mode, but also addresses the mismatched problems in the nonlinear control system. Secondly, a new ITSM controller with finite convergence time based on the backstepping technique is derived for a cascaded nonlinear dynamical system with mismatched uncertainties. Thirdly, the convergence time of ITSM is analyzed, whose convergence speed is faster than those of two nonsingular terminal sliding modes.Finally, simulation results are presented in order to evaluate the effectiveness of ITSM control strategies for mismatched uncertainties.

关键词： terminal sliding mode（TSM） finite-time convergence mismatched disturbance Lyapunov stability nonlinear systems

A finite-time Recurrent Neural Network for Computing Quadratic Minimization with time-Varying Coefficients

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Chinese Journal of Electronics 2019年第2期28卷 253-258页

作者： XIAO Lin LU Rongbo College of Information Science and Engineering Jishou University

This paper proposes a finite-time Zhang neural network(FTZNN) to solve time-varying quadratic minimization problems. Different from the original Zhang neural network(ZNN) that is specially designed to solve time-varying problems and possesses an exponential convergence property, the proposed neural network exploits a sign-bi-power activation function so that it can achieve the finite-time convergence. In addition, the upper bound of the finite convergence time for the FTZNN model is analytically estimated in theory. For comparative purposes, the original ZNN model is also presented to solve time-varying quadratic minimization problems. Numerical experiments are performed to evaluate and compare the performance of the original ZNN model and the FTZNN model. The results demonstrate that the FTZNN model is a more effective solution model for solving time-varying quadratic minimization problems.

关键词： Recurrent neural network Quadratic minimization Activation function finite-time convergence Comparative verification