161 results on '"Yan, Jun"'
Search Results
2. Extended state observer-based non-singular practical fixed-time adaptive consensus control of nonlinear multi-agent systems
- Author
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Yang, Ming, Wang, Zheng, Yu, Dengxiu, Wang, Zhen, and Liu, Yan -Jun
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- 2023
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3. Adaptive Anti-saturation Tracking Control for Spacecraft Safe Approach Based on Fast Terminal Sliding Mode
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Wu, Guan-qun, He, Zong-Bo, Lu, Ya-dong, Xing, Yan-jun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Yu, Xiang, editor
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- 2022
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4. Adaptive H-infinity SMC-based Model Reference Tracker for Uncertain Nonlinear Systems with Input Nonlinearity
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Fang, Jiunn-Shiou, Tsai, Jason Sheng-Hong, Yan, Jun-Juh, and Guo, Shu-Mei
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- 2021
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5. Full event‐triggering adaptive control considering time‐varying constraints for nonholonomic tractor‐trailer system.
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Chen, Yang, Liu, Yan‐Jun, Cao, Ying‐Li, Wang, Ming‐Fei, and Liu, Lei
- Abstract
Summary: In this paper, for the nonholonomic tractor‐trailer system with slipping and skidding, a full state‐triggering implementation of a tracking controller is proposed. Trailers are an important part, and their knowledge is crucial for the safety and control of the vehicle. Without considering system performance constraints, trailers may deviate from their expected position, leading to collisions. Therefore, there is still great interest in addressing the constraint control issues that arise in modern technologies related to network communication. Based on the backstepping method, bounded measurement errors are introduced into the continuous sampling controller considering intra‐vehicle communications. Then two neural networks are used to model unknown dynamics of the system with event sampling inputs, thereby decoupling the design of the control inputs. Finally, we prove the input‐to‐state‐like stability of a continuously sampled controller, and the given event execution control law can ensure the boundedness of measurement errors. Simulation results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Approximation-Based Adaptive Neural Tracking Control of an Uncertain Robot with Output Constraint and Unknown Time-Varying Delays
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Li, Da-Peng, Liu, Yan-Jun, Li, Dong-Juan, Tong, Shaocheng, Meng, Duo, Wen, Guo-Xing, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Cong, Fengyu, editor, Leung, Andrew, editor, and Wei, Qinglai, editor
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- 2017
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7. Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems
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Liu, Lei, Liu, Yan-Jun, Chen, Aiqing, Tong, Shaocheng, and Chen, C. L. Philip
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- 2020
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8. Intelligent Motion Tracking Control of Vehicle Suspension Systems With Constraints via Neural Performance Analysis
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Lei Liu, Shaocheng Tong, Changqi Zhu, and Yan-Jun Liu
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Lyapunov function ,Adaptive control ,Adaptive algorithm ,Artificial neural network ,Computer science ,Mechanical Engineering ,Active suspension ,Computer Science Applications ,symbols.namesake ,Control theory ,Backstepping ,Automotive Engineering ,symbols ,Piecewise - Abstract
A novel adaptive control scheme is developed for active suspension systems (ASSs) based on neural networks (NNs) and backstepping control strategies. Since the springs and piecewise dampers are nonlinear, the unknown internal dynamics are approximated by radial basis function neural networks (RBFNNs). Then, to solve the time-varying constrains of both vertical displacement and corresponding speed in vehicle body, the Tangent Barrier Lyapunov Functions (TBLFs) are incorporated into the controller design. Furthermore, the adaptive controller and adaptive laws are designed to improve the riding comfortable, handling stability and driving safety. In the end, the simulation results show the effectiveness and feasibility of the proposed adaptive algorithm compared with unconstrained adaptive approach.
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- 2022
9. Relative Threshold-Based Event-Triggered Control for Nonlinear Constrained Systems With Application to Aircraft Wing Rock Motion
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Zhiwei Gao, Lei Liu, Yan-Jun Liu, and Shaocheng Tong
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Adaptive control ,Adaptive algorithm ,G500 ,Computer science ,G400 ,G600 ,Upper and lower bounds ,Computer Science Applications ,Reduction (complexity) ,Nonlinear system ,Control and Systems Engineering ,Control theory ,Trajectory ,Electrical and Electronic Engineering ,Information Systems ,Parametric statistics ,Motion system - Abstract
This paper concentrates upon the event-driven controller design problem for a class of nonlinear single input single output (SISO) parametric systems with full state constraints. A varying threshold for the triggering mechanism is exploited, which makes the communication more flexible. Moreover, from the viewpoint of energy conservation and consumption reduction, the system capability becomes better owing to the contribution of the proposed event triggered mechanism. In the meantime, the developed control strategy can avoid the Zeno behavior since the lower bound of the sample time is provided. The considered plant is in a lower-triangular form, in which the match condition is not satisfied. To ensure that all the states to retain in a predefined region, a barrier Lyapunov function (BLF) based adaptive control law is developed. Due to the existence of the parametric uncertainties, an adaptive algorithm is presented as an estimated tool. All the signals appearing in the closed-loop systems are then proven to be uniformly ultimately bounded (UUB). Meanwhile, the output of the system can track a given signal as far as possible. In the end, the effectiveness of the proposed approach is validated by an aircraft wing rock motion system.
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- 2022
10. Adaptive constraint control for flexible manipulator systems modeled by partial differential equations with dead‐zone input
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Li Tang, Xin-Yu Zhang, and Yan-Jun Liu
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Partial differential equation ,Adaptive control ,Computer science ,MathematicsofComputing_NUMERICALANALYSIS ,State (functional analysis) ,Dead zone ,Manipulator system ,Control and Systems Engineering ,Control theory ,ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION ,Signal Processing ,Astrophysics::Earth and Planetary Astrophysics ,Electrical and Electronic Engineering ,Manipulator ,Constraint control - Abstract
Summary In this article, based on partial differential equations (PDEs), the flexible manipulator system with both dead‐zone input and state constraints is studied. The dynamic model of the flexibl...
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- 2021
11. Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints
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Yan-Jun Liu, Wei Zhao, and Lei Liu
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0209 industrial biotechnology ,Adaptive control ,Observer (quantum physics) ,Computer science ,02 engineering and technology ,Constraint satisfaction ,Fuzzy logic ,Tracking error ,Nonlinear system ,020901 industrial engineering & automation ,Artificial Intelligence ,Control and Systems Engineering ,Control theory ,Backstepping ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Information Systems - Abstract
A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints. The fuzzy logic system is used to design the approximator, which deals with uncertain and continuous functions in the process of backstepping design. The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint, but also mixes the states and errors to directly constrain the state, reducing the conservativeness of the constraint satisfaction condition. Considering that the states in most nonlinear systems are immeasurable, a fuzzy adaptive states observer is constructed to estimate the unknown states. Combined with adaptive backstepping technique, an adaptive fuzzy output feedback control method is proposed. The proposed control method ensures that all signals in the closed-loop system are bounded, and that the tracking error converges to a bounded tight set without violating the full state constraint. The simulation results prove the effectiveness of the proposed control scheme.
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- 2021
12. Fuzzy Observer Constraint Based on Adaptive Control for Uncertain Nonlinear MIMO Systems With Time-Varying State Constraints
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Lei Liu, Shaocheng Tong, Yan-Jun Liu, Mingzhe Gong, and C. L. Philip Chen
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Lyapunov function ,0209 industrial biotechnology ,Adaptive control ,Computer science ,02 engineering and technology ,Interval (mathematics) ,Computer Science Applications ,Human-Computer Interaction ,Constraint (information theory) ,symbols.namesake ,Nonlinear system ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,Backstepping ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,State observer ,Electrical and Electronic Engineering ,Software ,Information Systems - Abstract
This article presents an adaptive output feedback approach of nonlinear multi-input-multi-output (MIMO) systems with time-varying state constraints and unmeasured states. An adaptive approximator is designed to approximate the unknown nonlinear functions existing in the state-constrained systems with immeasurable states. To deal with the tracking problem of such systems, a state observer with time-varying barrier Lyapunov functions (BLFs) is introduced in the controller design procedure. The backstepping design with time-varying BLFs is utilized to guarantee that all system states remain within the time-varying-constrained interval. The constant constraint is only the special case of the time-varying constraint which is more general in the real systems. The proposed control approach guarantees that all signals in the closed-loop systems are bounded and the tracking errors converge to a bounded compact set, and time-varying full-state constraints are never violated. A simulation example is given to confirm the feasibility of the presented control approach in this article.
- Published
- 2021
13. Adaptive NN control based on Butterworth low-pass filter for quarter active suspension systems with actuator failure
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Xing Zhang, Yan-Jun Liu, and Lei Liu
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Adaptive control ,Computer science ,lcsh:Mathematics ,General Mathematics ,Low-pass filter ,nn control ,Fault tolerance ,adaptive control ,lcsh:QA1-939 ,Active suspension ,Nonlinear system ,Control theory ,Filter (video) ,vehicle suspension systems ,Backstepping ,butterworth low-pass filter ,fault tolerance ,Actuator - Abstract
This paper focuses on the adaptive neural network (NN) control problem for nonlinear quarter active suspension systems with actuator failure. By using Butterworth low-pass filter (LPF), the second order active suspension system is converted to a fourth order system, which solves the problem of zero dynamics analysis in the second order system. Based on the adaptive backstepping technique, considering the actuator fault of vehicle, the corresponding fault tolerant controller is designed. At the same time, the unknown smooth functions are estimated by the NN. It is proved by stability analysis that all states in active suspension system are bounded. Finally, a simulation example is given to verify the effectiveness of the proposed method in a quarter active suspension system.
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- 2021
14. An Adaptive Neural Network Controller for Active Suspension Systems With Hydraulic Actuator
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Qiang Zeng, Yan-Jun Liu, Lei Liu, and Shaocheng Tong
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0209 industrial biotechnology ,Adaptive control ,Computer science ,02 engineering and technology ,Servomechanism ,Active suspension ,Computer Science Applications ,law.invention ,Human-Computer Interaction ,Vehicle dynamics ,Hydraulic cylinder ,Nonlinear system ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,law ,Backstepping ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Hydraulic machinery ,Software - Abstract
In this paper, an adaptive neural network (NN) controller is proposed for a class of nonlinear active suspension systems (ASSs) with hydraulic actuator. To eliminate the problem of “explosion of complexity” inherently in the traditional backstepping design for the hydraulic actuator, a dynamic surface control technique is developed to stabilize the attitude of the vehicle by introducing a first-order filter. Meanwhile, the presented scheme improves the ride comfort even when the uncertain parameter exists. Due to the existence of uncertain terms, the NNs are used to approximate unknown functions in the ASSs. Finally, a simulation for a servo system with hydraulic actuator is shown to verify the effectiveness and reliability of the proposed approach.
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- 2020
15. Reinforcement Learning Neural Network-Based Adaptive Control for State and Input Time-Delayed Wheeled Mobile Robots
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Haibo Gao, Zongquan Deng, Yan-Jun Liu, Shu Li, Li Nan, and Liang Ding
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Human-Computer Interaction ,Nonlinear system ,Adaptive control ,Artificial neural network ,Control and Systems Engineering ,Control theory ,Computer science ,Reinforcement learning ,Mobile robot ,Affine transformation ,Electrical and Electronic Engineering ,Software ,Computer Science Applications - Abstract
In this paper, a reinforcement learning-based adaptive control algorithm is proposed to solve the tracking problem of a discrete-time (DT) nonlinear state and input time delayed system of the wheeled mobile robot (WMR). With the typical model of the WMR transformed into an affine nonlinear DT system, a delay matrix function and appropriate Lyapunov–Krasovskii functionals are introduced to overcome the problems caused by the state and input time delays, respectively. Furthermore, with the approximation of the radial basis function neural networks (NNs), the adaptive controller, the critic NN, and action NN adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the WMR system, and the tracking errors convergence to a small compact set to zero. Two examples of simulation are given to illustrate the effectiveness of the proposed algorithm.
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- 2020
16. Disturbance observer-based adaptive neural network FTC for a class of nonlinear MASs with an estimated efficiency factor.
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Jia, Jiyang, Lan, Jie, Liu, Yan-Jun, and Liu, Lei
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FAULT-tolerant control systems ,CLOSED loop systems ,MULTIAGENT systems ,ADAPTIVE control systems ,LYAPUNOV functions - Abstract
An adaptive neural network fault-tolerant control(FTC) scheme is proposed for nonlinear and nonstrict-feedback multi-agent systems (MASs) with directed fixed topology. Firstly, a disturbance observer is designed to estimate the unknown external disturbances in the systems, and realise the dynamic estimation of the disturbances. Secondly, the efficiency factor is estimated online, and then the FTC scheme is designed successfully under the backstepping framework. It is proved that all signals in the closed-loop systems are semi-globally uniformly bounded and the tracking error is controlled in a small range. Finally, an example is given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
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- 2023
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17. Distributed adaptive NN control for nonlinear multi‐agent systems with function constraints on states.
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Ma, Yuzhen, Yuan, Fengyi, Liu, Yan‐Jun, Liu, Lei, and Xu, Tongyu
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MULTIAGENT systems ,NONLINEAR systems ,ADAPTIVE control systems ,LYAPUNOV functions - Abstract
An adaptive neural network (NN) distributed tracking control strategy is proposed for nonlinear strict‐feedback multi‐agent systems, which is affected by time‐varying full state constraints. The influence of asymmetric state constraints is also considered. The constraint bounds are related to both state vectors and time. Different from the usual constant boundary or function boundary, the constraint boundary adopted in this article not only considers the influence of state variables, but also takes time into consideration, which makes the constraint boundary more flexible and more difficult to solve. In addition, in order to solve the disturbance of the unknown function in the system, this article adopts the NN technology to realize the approximation effect of the unknown function, and designs a adaptive distributed controller based on the asymmetric obstacle Lyapunov function and backward step method, the closed‐loop signals are proved to be CSUUB. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]
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- 2023
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18. Actuator Failure Compensation-Based Adaptive Control of Active Suspension Systems With Prescribed Performance
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Yan-Jun Liu, Lei Liu, C. L. Philip Chen, Qiang Zeng, and Shaocheng Tong
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Adaptive control ,Artificial neural network ,Computer science ,020208 electrical & electronic engineering ,02 engineering and technology ,Active suspension ,Fault (power engineering) ,Suspension (motorcycle) ,Nonlinear system ,Control and Systems Engineering ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Overshoot (signal) ,Vertical displacement ,Electrical and Electronic Engineering ,Actuator ,Suspension (vehicle) - Abstract
In this article, we study the control problem of the vehicle active suspension systems (ASSs) subject to actuator failure. An adaptive control scheme is presented to stabilize the vertical displacement of the car-body. Meanwhile, the ride comfort, road holding, and suspension space limitation can be guaranteed. In order to overcome the uncertainty, the neural network is developed to approximate the continuous function with the unknown car-body mass. Furthermore, to improve the transient regulation performance of ASSs when the actuator failure occurs, we propose a novel control scheme with the prescribed performance function to characterize the tracking error convergence rate and maximum overshoot in ASSs. Then, the stability of the proposed control algorithm can be proven based on the Lyapunov theorem. Finally, the comparative simulation results of two actuator failure types (i.e., the float fault and the loss of effectiveness fault) are given to demonstrate the effectiveness of the proposed control schemes.
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- 2020
19. Fuzzy Approximation-Based Adaptive Control of Nonlinear Uncertain State Constrained Systems With Time-Varying Delays
- Author
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Lei Liu, Yan-Jun Liu, Dapeng Li, Shaocheng Tong, and C. L. Philip Chen
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Adaptive control ,Artificial neural network ,Computer science ,Applied Mathematics ,Control (management) ,LKFS ,02 engineering and technology ,Fuzzy logic ,Nonlinear system ,Computational Theory and Mathematics ,Artificial Intelligence ,Control and Systems Engineering ,Control theory ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,State (computer science) - Abstract
In this paper, a novel adaptive fuzzy tracking control strategy is developed for nonlinear time-varying delayed systems with full state constraints. State constraints and time delays are normally found in various real-life plants, which are two important factors for degrading system performance significantly. In the framework of adaptive control, the effects of state constraints and time-varying delays are removed simultaneously. The integral Barrier Lyapunov functionals (IBLFs) are applied to achieve full-state-constraint satisfactions and remove the need of the transformed error constraints in previous BLFs. The unknown time-varying delays are completely compensated by introducing the separation technique and Lyapunov–Krasovskii functionals (LKFs). The unknown functions existing in systems are approximated by employing fuzzy logic systems (FLSs). With the help of less-adjustable parameters, only one parameter is needed to be adjusted online in each step of control design. The novel strategy can guarantee that a satisfactory tracking performance is achieved and the signals existing in the closed-loop system are bounded. Finally, by presenting simulation results, the efficiency of the proposed approach is revealed.
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- 2020
20. Adaptive neural network control for nonlinear state constrained systems with unknown dead-zones input
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Wei Zhao, Yan-Jun Liu, and Lei Liu
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Lyapunov function ,Adaptive control ,Artificial neural network ,neural network ,Computer science ,lcsh:Mathematics ,General Mathematics ,Process (computing) ,Dead zone ,adaptive control ,lcsh:QA1-939 ,Constraint (information theory) ,Nonlinear system ,symbols.namesake ,barrier lyapunov functions ,Control theory ,dead-zone ,Backstepping ,symbols - Abstract
In this paper, an adaptive neural network tracking control problem for a class of strict feedback systems is disposed. The neural network adaptive control method is introduced in this paper to simplify the controller design. The difficulty in this article is the constraint problem and how to resolve dead-zones in the system. In order to overcome these difficulties, the Barrier Lyapunov functions (BLF) and backstepping process are introduced to ensure that the full state constraint is implemented, meanwhile, keep the system output as close as possible to trace the desired trajectory. Dead-zone compensation method is also plays an important role in controller design. Delay constraint is introduced to solve the problem of uncertain initial state. In the end, the stability of the closed-loop system is proved. Simulation results show that the developed method is effective.
- Published
- 2020
21. Time-varying asymmetrical BLFs based adaptive finite-time neural control of nonlinear systems with full state constraints
- Author
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Lei Liu, Shaocheng Tong, Tingting Gao, and Yan-Jun Liu
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Lyapunov stability ,Lyapunov function ,0209 industrial biotechnology ,Adaptive control ,Computer science ,Recursion (computer science) ,02 engineering and technology ,Nonlinear system ,symbols.namesake ,020901 industrial engineering & automation ,Artificial Intelligence ,Control and Systems Engineering ,Control theory ,Backstepping ,Stability theory ,Control system ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,Information Systems - Abstract
This paper concentrates on asymmetric barrier Lyapunov functions ( ABLFs ) based on finite-time adaptive neural network ( NN ) control methods for a class of nonlinear strict feedback systems with time-varying full state constraints. During the process of backstepping recursion, the approximation properties of NNs are exploited to address the problem of unknown internal dynamics. The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied. According to the Lyapunov stability and finite-time stability theory, it is proven that all the signals in the closed-loop systems are uniformly ultimately bounded ( UUB ) and the system output is driven to track the desired signal as quickly as possible near the origin. In the meantime, in the scope of finite-time, all states are guaranteed to stay in the pre-given range. Finally, a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.
- Published
- 2020
22. Fully adaptive-gain-based intelligent failure-tolerant control for spacecraft attitude stabilization under actuator saturation
- Author
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Xiaodong Cheng, Yuanqing Xia, Yan-Jun Liu, Ning Zhou, Dynamic Networks: Data-Driven Modeling and Control, and Control Systems
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0209 industrial biotechnology ,Neural Networks ,Computer science ,input saturation ,Terminal sliding mode ,02 engineering and technology ,Actuator saturation ,Computer ,020901 industrial engineering & automation ,Control theory ,Robustness (computer science) ,attitude stabilization ,0202 electrical engineering, electronic engineering, information engineering ,Computer Simulation ,Electrical and Electronic Engineering ,Spacecraft ,Artificial neural network ,business.industry ,020208 electrical & electronic engineering ,Adaptive control ,failure-tolerant control ,Computer Science Applications ,Human-Computer Interaction ,finite-time control ,Control and Systems Engineering ,Neural Networks, Computer ,Actuator ,business ,Software ,Algorithms ,Information Systems - Abstract
This article investigates the attitude stabilization problem of a rigid spacecraft with actuator saturation and failures. Two neural network-based control schemes are proposed using anti-saturation adaptive strategies. To satisfy the input constraint, we design two controllers in a saturation function structure. Taking into account the modeling uncertainties, external disturbances, and adverse effects from actuator faults and failures, the first anti-saturation adaptive controller is implemented based on radial basis function neural networks (RBFNNs) with a fixed-time terminal sliding mode (FTTSM) containing a tunable parameter. Then, we upgrade the proposed controller to a fully adaptive-gain anti-saturation version, in order to strengthen the robustness and adaptivity with respect to actuator faults and failures, unknown mass properties, and external disturbances. In the two schemes, all of the designed adaptive parameters are scalars, thus they only require light computational load and can avoid the redesign process of the controller during spacecraft operation. Finally, the feasibility of the proposed methods is illustrated via two numerical examples.
- Published
- 2022
23. Adaptive Neural Network Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints
- Author
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C. L. Philip Chen, Yan-Jun Liu, Qiang Zeng, Shaocheng Tong, and Lei Liu
- Subjects
Lyapunov function ,Adaptive control ,Artificial neural network ,Computer science ,020208 electrical & electronic engineering ,Stability (learning theory) ,02 engineering and technology ,Active suspension ,Task (project management) ,Vehicle dynamics ,symbols.namesake ,Control and Systems Engineering ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Vertical displacement ,Electrical and Electronic Engineering - Abstract
In this paper, an adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body. The NNs are used to approximate the unknown mass of car body. It is commonly known that the stability and security of the ASSs will be weakened when the constraints are violated. Thus, the control problem of the time-varying vertical displacement and speed constraints for the quarter-car ASSs is a very important task because of the demand of the handing safety. The time-varying barrier Lyapunov functions are used to guarantee the constraints of the vertical displacement not violated, and it can prove the stability of the closed-loop system. Finally, a simulation example for the ASSs is employed to show the feasibility and rationality of the proposed approach.
- Published
- 2019
24. Adaptive NN Control Without Feasibility Conditions for Nonlinear State Constrained Stochastic Systems With Unknown Time Delays
- Author
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Yan-Jun Liu, Lei Liu, Shaocheng Tong, Dapeng Li, and C. L. Philip Chen
- Subjects
Surface (mathematics) ,0209 industrial biotechnology ,Adaptive control ,Artificial neural network ,Computer science ,02 engineering and technology ,Exponential type ,Computer Science Applications ,Human-Computer Interaction ,Nonlinear system ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,State (computer science) ,Electrical and Electronic Engineering ,Software ,Information Systems - Abstract
In the novel, an adaptive neural network (NN) controller is developed for a category of nonlinear stochastic systems with full state constraints and unknown time delays. The control quality and system stability suffer from the problems of state time delays and constraints which frequently arises in most real plants. The considered systems are transformed into new constrained free systems based on nonlinear mappings, such that full state constraints are never violated and the feasibility conditions on virtual controllers (the values of virtual controllers and its derivative are assumed to be known) are removed. To compensate for unknown time delayed uncertainties, the exponential type Lyapunov-Krasovskii functionals (LKFs) are employed. NNs are utilized to approximate unknown nonlinear functions appearing in the design procedure. In addition, by employing dynamic surface control (DSC) technique and less adjustable parameters, the online computation burden is lightened. The control method presented can achieve the semiglobal uniform ultimate boundedness of all the closed-loop system signals and the satisfactions of full state constraints by rigorous proof. Finally, by presenting simulation examples, the efficiency of the presented approach is revealed.
- Published
- 2019
25. Adaptive Neural Network Control for Uncertain Time-Varying State Constrained Robotics Systems
- Author
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Dapeng Li, Yan-Jun Liu, and Shu-Min Lu
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Mathematical optimization ,Adaptive control ,Computer science ,02 engineering and technology ,symbols.namesake ,020901 industrial engineering & automation ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Parametric statistics ,Artificial neural network ,business.industry ,Robotics ,Mobile robot ,Computer Science Applications ,Human-Computer Interaction ,Nonlinear system ,Control and Systems Engineering ,Bounded function ,symbols ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Software - Abstract
In this paper, we design an adaptive neural network (NN) controller of uncertain ${n}$ -joint robotic systems with time-varying state constraints. By proposing a nonlinear mapping, the robotic systems are transformed into the multiple-input, multiple-output systems. Compared with constant constraints, the time-varying state constraints are more general in the real systems. To overcome the design challenge, the time-varying barrier Lyapunov function is introduced to ensure that the states of the robotic systems are bounded within the predetermined time-varying range. The NN approximations are employed to approximate the uncertain parametric and unknown functions in the robotic systems. Based on the Lyapunov analysis, it can be proved that all signals of robotic systems are bounded; the tracking errors of system output converge on a small neighborhood of zero and the time-varying state constraints are never violated. Finally, a simulation example is performed to demonstrate the feasibility of the proposed approach.
- Published
- 2019
26. Adaptive near optimal neural control for a class of discrete-time chaotic system
- Author
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Tang, Li, Gao, Ying, and Liu, Yan-Jun
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- 2014
- Full Text
- View/download PDF
27. Adaptive Fuzzy Output Feedback Control of Switched Uncertain Nonlinear Systems With Constraint Conditions Related to Historical States.
- Author
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Liu, Lei, Li, Zheng, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,UNCERTAIN systems ,STABILITY theory ,LYAPUNOV stability ,LYAPUNOV functions - Abstract
In this article, a fuzzy adaptive output feedback control strategy is designed for a class of uncertain nonlinear switched system with full state constraints under arbitrary switching signal. The states of the system studied in this article are unmeasurable, so a fuzzy observer is designed to estimate the unmeasurable states. At the same time, in order to ensure that the states of the system do not violate the constraints related to the desired output and states, the log-type barrier Lyapunov function method is selected to solve this constraint problem. Finally, through Lyapunov stability theory analysis, it is found that the designed control strategy can ensure that all signals in the closed-loop system are bounded, and the states of the system do not violate their corresponding constraints. In addition, a numerical simulation verifies the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
28. Adaptive control design for MIMO switched nonlinear systems with full state constraints
- Author
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Lei Liu, Aiqing Chen, and Yan-Jun Liu
- Subjects
0209 industrial biotechnology ,Full state ,Adaptive control ,Computer science ,MIMO ,Uncertain systems ,02 engineering and technology ,Fuzzy control system ,Nonlinear system ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering - Abstract
Summary This paper presents an adaptive fuzzy control approach of multiple‐input–multiple‐output (MIMO) switched uncertain systems, which involve time‐varying full state constraints (TFSCs) and unk...
- Published
- 2019
29. Fuzzy-Based Multierror Constraint Control for Switched Nonlinear Systems and Its Applications
- Author
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Shaocheng Tong, Yan-Jun Liu, and Lei Liu
- Subjects
Adaptive control ,Computer science ,Applied Mathematics ,Continuous stirred-tank reactor ,Contrast (statistics) ,02 engineering and technology ,Fuzzy logic ,Dwell time ,Nonlinear system ,Computational Theory and Mathematics ,Artificial Intelligence ,Control and Systems Engineering ,Control theory ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Constraint control - Abstract
In this paper, a framework of adaptive control for a switched nonlinear system with multiple prescribed performance bounds is established using an improved dwell time technique. Since the prescribed performance bounds for subsystems are different from each other, the different coordinate transformations have to be tackled when the system is transformed, which have not been encountered in some switched systems. We deal with the different coordinate transformations by finding a specific relationship between any two different coordinate transformations. To obtain a much less conservative result, in contrast to the common adaptive law, different adaptive laws are established for both active and inactive time-interval of each subsystem. The proposed controllers and switching signals guarantee that all signals appearing in the closed-loop system are bounded. Furthermore, both transient-state and steady-state performances of the switched system are obtained. Finally, the effectiveness of the developed method is verified by the application to a continuous stirred tank reactor system.
- Published
- 2019
30. Adaptive control for switched uncertain nonlinear systems with time‐varying output constraint and input saturation
- Author
-
Yan-Jun Liu, Ying Gao, Li Tang, and Aiqing Chen
- Subjects
Constraint (information theory) ,Nonlinear system ,Adaptive control ,Control and Systems Engineering ,Computer science ,Control theory ,Signal Processing ,Electrical and Electronic Engineering ,Saturation (chemistry) - Published
- 2019
31. Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay
- Author
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Shaocheng Tong, Dapeng Li, Yan-Jun Liu, Dong-Juan Li, and C. L. Philip Chen
- Subjects
0209 industrial biotechnology ,Adaptive control ,Artificial neural network ,Computer science ,02 engineering and technology ,Interval (mathematics) ,Computer Science Applications ,Human-Computer Interaction ,Tracking error ,Nonlinear system ,020901 industrial engineering & automation ,Compact space ,Control and Systems Engineering ,Control theory ,Bounded function ,Backstepping ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Software ,Information Systems - Abstract
This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.
- Published
- 2019
32. Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input
- Author
-
Shaocheng Tong, Yan-Jun Liu, C. L. Philip Chen, and Shu Li
- Subjects
Lyapunov stability ,Adaptive control ,Implicit function ,Artificial neural network ,Computer Networks and Communications ,Computer science ,Stability (learning theory) ,02 engineering and technology ,Optimal control ,Computer Science Applications ,Nonlinear system ,Discrete time and continuous time ,Artificial Intelligence ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Reinforcement learning ,020201 artificial intelligence & image processing ,Software - Abstract
In this paper, an optimal control algorithm is designed for uncertain nonlinear systems in discrete-time, which are in nonaffine form and with unknown dead-zone. The main contributions of this paper are that an optimal control algorithm is for the first time framed in this paper for nonlinear systems with nonaffine dead-zone, and the adaptive parameter law for dead-zone is calculated by using the gradient rules. The mean value theory is employed to deal with the nonaffine dead-zone input and the implicit function theory based on reinforcement learning is appropriately introduced to find an unknown ideal controller which is approximated by using the action network. Other neural networks are taken as the critic networks to approximate the strategic utility functions. Based on the Lyapunov stability analysis theory, we can prove the stability of systems, i.e., the optimal control laws can guarantee that all the signals in the closed-loop system are bounded and the tracking errors are converged to a small compact set. Finally, two simulation examples demonstrate the effectiveness of the design algorithm.
- Published
- 2019
33. Adaptive Vehicle Stability Control of Half-Car Active Suspension Systems With Partial Performance Constraints
- Author
-
Lei Liu, Qiang Zeng, and Yan-Jun Liu
- Subjects
Lyapunov function ,Lyapunov stability ,0209 industrial biotechnology ,Adaptive control ,Continuous function ,Computer science ,02 engineering and technology ,Active suspension ,Computer Science Applications ,Human-Computer Interaction ,Vehicle dynamics ,symbols.namesake ,Nonlinear system ,020901 industrial engineering & automation ,Electronic stability control ,Control and Systems Engineering ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Software - Abstract
A novel adaptive controller for the half-car active suspension systems (ASSs), which can improve the riding comfortability and handling stability of the driver, is proposed in this paper. By using nonlinear mapping, it is demonstrated that the nonlinear ASSs with partial performance constraints are transformed into the novel pure-feedback systems without constraints. By introducing a modified dynamic surface control (DSC) into the Lyapunov function, the adaptive neural network (NN) controller is discussed. The unknown continuous functions are estimated by the NNs, and the boundedness of all signals in the closed-loop systems is guaranteed by the Lyapunov stability theory. Meanwhile, the performance constraints are not violated. Finally, the simulations are performed to clarify and verify the effectiveness of the proposed scheme.
- Published
- 2019
34. Adaptive Fuzzy Output-Feedback Control for Switched Uncertain Nonlinear Systems With Full-State Constraints.
- Author
-
Liu, Lei, Chen, Aiqing, and Liu, Yan-Jun
- Abstract
This article investigates an adaptive fuzzy tracking control approach via output feedback for a class of switched uncertain nonlinear systems with full-state constraints under arbitrary switchings. The adaptive observer and controller are designed based on fuzzy approximation. The main characteristic of discussed systems is that the state variables are not available for measurement and need to be kept within the constraint set. In order to estimate the unmeasured states, the adaptive fuzzy state observer is constructed. To guarantee that all the states do not violate the time-varying bounds, the tangent barrier Lyapunov functions (BLF-Tans) are selected in the design procedure. Based on the common Lyapunov function method, the stability of considered systems is analyzed. It is demonstrated that all the signals in the resulting system are bounded, and all the states are limited in their constrained sets. Furthermore, the simulation example is used to validate the effectiveness of the presented control strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
35. IBLF-Based Adaptive Neural Control of State-Constrained Uncertain Stochastic Nonlinear Systems.
- Author
-
Gao, Tingting, Li, Tieshan, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
ADAPTIVE control systems ,NONLINEAR systems ,STOCHASTIC systems ,RADIAL basis functions ,LYAPUNOV stability ,CLOSED loop systems - Abstract
In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to symmetric and asymmetric constraints are studied, respectively. Then, corresponding adaptive neural controllers are developed by virtue of backstepping design procedure and the learning ability of radial basis function neural network (RBFNN). It is worth mentioning that the integral Barrier Lyapunov function (IBLF), as an effective tool, is first applied to solve the above constraint problems. As a result, the state constraints are avoided from being transformed into error constraints via the proposed schemes. In addition, based on Lyapunov stability analysis, it is demonstrated that the errors can converge to a small neighborhood of zero, the full states do not exceed the given constraint bounds, and all signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in probability. Finally, the numerical simulation results are provided to exhibit the effectiveness of the proposed control approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
36. Adaptive Finite-Time Neural Network Control of Nonlinear Systems With Multiple Objective Constraints and Application to Electromechanical System
- Author
-
Yue-Ying Wang, Lei Liu, Wei Zhao, Shaocheng Tong, and Yan-Jun Liu
- Subjects
Constraint (information theory) ,Nonlinear system ,Adaptive control ,Artificial neural network ,Artificial Intelligence ,Computer Networks and Communications ,Computer science ,Control theory ,Backstepping ,Stability (learning theory) ,Filter (signal processing) ,Software ,Computer Science Applications - Abstract
This article investigates an adaptive finite-time neural control for a class of strict feedback nonlinear systems with multiple objective constraints. In order to solve the main challenges brought by the state constraints and the emergence of finite-time stability, a new barrier Lyapunov function is proposed for the first time, not only can it solve multiobjective constraints effectively but also ensure that all states are always within the constraint intervals. Second, by combining the command filter method and backstepping control, the adaptive controller is designed. What is more, the proposed controller has the ability to avoid the ``singularity'' problem. The compensation mechanism is introduced to neutralize the error appearing in the filtering process. Furthermore, the neural network is used to approximate the unknown function in the design process. It is shown that the proposed finite-time neural adaptive control scheme achieves a good tracking effect. And each objective function does not violate the constraint bound. Finally, a simulation example of electromechanical dynamic system is given to prove the effectiveness of the proposed finite-time control strategy.
- Published
- 2020
37. Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems
- Author
-
Lei Liu, C. L. Philip Chen, Aiqing Chen, Shaocheng Tong, and Yan-Jun Liu
- Subjects
Lyapunov function ,Lyapunov stability ,State variable ,Adaptive control ,General Computer Science ,Computer science ,020207 software engineering ,02 engineering and technology ,Constraint (information theory) ,symbols.namesake ,Nonlinear system ,Control theory ,Backstepping ,0202 electrical engineering, electronic engineering, information engineering ,symbols - Abstract
This paper presents an adaptive control method for a class of uncertain strict-feedback switched nonlinear systems. First, we consider the constraint characteristics in the switched nonlinear systems to ensure that all states in switched systems do not violate the constraint ranges. Second, we design the controller based on the backstepping technique, while integral Barrier Lyapunov functions (iBLFs) are adopted to solve the full state constraint problems in each step in order to realize the direct constraints on state variables. Furthermore, we introduce the Lyapunov stability theory to demonstrate that the adaptive controller achieves the desired control goals. Finally, we perform a numerical simulation, which further verifies the significance and feasibility of the presented control scheme.
- Published
- 2020
38. Adaptive neural network-based control for a class of nonlinear pure-feedback systems with time-varying full state constraints
- Author
-
Yan-Jun Liu, Lei Liu, Tingting Gao, and Dapeng Li
- Subjects
Lyapunov stability ,Lyapunov function ,0209 industrial biotechnology ,Adaptive control ,Artificial neural network ,Computer science ,02 engineering and technology ,Nonlinear system ,symbols.namesake ,020901 industrial engineering & automation ,Artificial Intelligence ,Control and Systems Engineering ,Control theory ,Control system ,Backstepping ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,Information Systems - Abstract
In this paper, an adaptive neural network ( NN ) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions ( BLFs ) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.
- Published
- 2018
39. Adaptive Fuzzy Tracking Control Based Barrier Functions of Uncertain Nonlinear MIMO Systems With Full-State Constraints and Applications to Chemical Process
- Author
-
Dong-Juan Li, Yan-Jun Liu, Dapeng Li, and Shu-Min Lu
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Mathematical optimization ,Adaptive control ,Applied Mathematics ,Stability (learning theory) ,02 engineering and technology ,Fuzzy control system ,Fuzzy logic ,Nonlinear system ,symbols.namesake ,020901 industrial engineering & automation ,Computational Theory and Mathematics ,Artificial Intelligence ,Control and Systems Engineering ,Control theory ,Backstepping ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,Mathematics - Abstract
An adaptive control approach based on the fuzzy systems for a class of uncertain nonlinear multi-input multi-output (MIMO) systems is presented in this paper. This class of systems is in the nested multiple coupling structure and their states are constrained in the corresponding compact sets. The properties of the system structure are inevitable to bring about a complicated design and a difficult task. The fuzzy logic systems are employed to approximate the unknown functions of systems, and the decoupling backstepping way is proposed to design the stability controller and adaptation laws. Barrier Lyapunov functions (BLFs) are constructed in the backstepping design to guarantee that the constraint bounds are not violated. Based on Lyapunov analysis in barrier form, we can prove the stability of the closed-loop system. Two simulation examples are viewed to verify the feasibility of the approach.
- Published
- 2018
40. Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints
- Author
-
C. L. Philip Chen, Yan-Jun Liu, Shaocheng Tong, Xinkai Chen, Shu-Min Lu, and Dong Juan Li
- Subjects
Lyapunov function ,Scheme (programming language) ,0209 industrial biotechnology ,Mathematical optimization ,Adaptive control ,Constraint (computer-aided design) ,02 engineering and technology ,Stability (probability) ,Nonlinear system ,symbols.namesake ,020901 industrial engineering & automation ,Compact space ,Control and Systems Engineering ,Control theory ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,computer ,computer.programming_language ,Mathematics - Abstract
An adaptive control scheme is developed in the paper for nonlinear stochastic systems with unknown parameters. All the states in the systems are required to be constrained in a bounded compact set, i.e., the full state constraints are considered in the systems. It is for the first time to control nonlinear stochastic systems with the full state constraints. In contrast to deterministic systems, the stochastic systems with the full state constraints are more difficult to be stabilized and the design procedures are more complicated. By constructing Barrier Lyapunov Functions (BLF) in symmetric and asymmetric forms, it can be ensured that all the states of the stochastic systems are not to transgress their constraint bounds. Thus, the proposed scheme not only solves the stability problem of stochastic systems, but also overcomes the effect of the full state constraints on the control performance. Finally, it is proved that all the signals in the closed-loop system are semi-global uniformly ultimately bounded (SGUUB) in probability, the system output is driven to follow the reference signals, and all the states are ensured to remain in the predefined compact sets. The validity of the proposed scheme is verified by a simulation example. (C) 2017 Elsevier Ltd. All rights reserved.
- Published
- 2018
41. Adaptive NN Control Using Integral Barrier Lyapunov Functionals for Uncertain Nonlinear Block-Triangular Constraint Systems
- Author
-
Yan-Jun Liu, Dong-Juan Li, Shaocheng Tong, and C. L. Philip Chen
- Subjects
0209 industrial biotechnology ,Mathematical optimization ,Adaptive control ,Artificial neural network ,Constraint (computer-aided design) ,MIMO ,02 engineering and technology ,Computer Science Applications ,Human-Computer Interaction ,Nonlinear system ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,Backstepping ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Software ,Information Systems ,Mathematics ,Block (data storage) - Abstract
A neural network (NN) adaptive control design problem is addressed for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. The considered systems contain uncertainty dynamics and their states are enforced to subject to bounded constraints as well as the couplings among various inputs and outputs are inserted in each subsystem. To stabilize this class of systems, a novel adaptive control strategy is constructively framed by using the backstepping design technique and NNs. The novel integral barrier Lyapunov functionals (BLFs) are employed to overcome the violation of the full state constraints. The proposed strategy can not only guarantee the boundedness of the closed-loop system and the outputs are driven to follow the reference signals, but also can ensure all the states to remain in the predefined compact sets. Moreover, the transformed constraints on the errors are used in the previous BLF, and accordingly it is required to determine clearly the bounds of the virtual controllers. Thus, it can relax the conservative limitations in the traditional BLF-based controls for the full state constraints. This conservatism can be solved in this paper and it is for the first time to control this class of MIMO systems with the full state constraints. The performance of the proposed control strategy can be verified through a simulation example.
- Published
- 2017
42. Distributed adaptive fuzzy control for multi-agent systems with full state constraints and unmeasured states.
- Author
-
Ma, Yuzhen, Liu, Yan-Jun, Zhao, Wei, Lan, Jie, Xu, Tongyu, and Liu, Lei
- Subjects
- *
ADAPTIVE fuzzy control , *FUZZY control systems , *MULTIAGENT systems , *LYAPUNOV stability , *FUZZY logic , *PSYCHOLOGICAL feedback , *MULTIPLE criteria decision making - Abstract
This article proposes a fuzzy adaptive output feedback control method for multi-agent systems with uncertainties and nonlinearities, considering both full state constraints and unmeasurable states. Aiming at the problem that the state of followers in nonlinear multi-agent systems is unmeasured, an observer is constructed by applying the characteristics of fuzzy logic systems to achieve the output feedback control scheme. The state constraint of multi-agent systems is also a great challenge. Based on a backstepping technique and integral barrier Lyapunov functions (iBLFs), all states are constrained, and an adaptive controller is designed. Compared with imposing constraints on the error and then constraining the state, the method in this article compensates for defects in the constraint conditions. Furthermore, under the Lyapunov stability theorem, we can prove that all signals of closed-loop systems are cooperative semi-global uniformly ultimately bounded (CSUUB). A simulation validates the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Fault‐tolerant attitude tracking control with practical finite time convergence for unmanned aerial vehicles under actuation faults.
- Author
-
Yu, Wei, Yan, Jun, Pan, Xiaohu, Tan, Shilei, Cao, Hongwei, and Song, Yongduan
- Subjects
- *
TRACKING control systems , *DRONE aircraft , *LYAPUNOV stability , *FINITE, The , *CLOSED loop systems , *TRACKING algorithms - Abstract
This paper presents an adaptive attitude finite time tracking control algorithm for quadrotor unmanned aerial vehicle (UAV) in presence of actuator faults, input saturation and external disturbance. The dynamic model of the quadrotor UAV is characterized with quaternion representation. Subsequently, using nonsingular terminal sliding mode surface, an adaptive fault‐tolerant finite‐time attitude control scheme is proposed. Using Lyapunov stability analysis, it is shown that the proposed method is able to achieve practical finite time attitude tracking convergence in that the attitude tracking error converges to a small residual set within finite time and all signals of the closed‐loop system remain bounded. The effectiveness of the proposed method is confirmed via numerical simulation. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. Relative Threshold-Based Event-Triggered Control for Nonlinear Constrained Systems With Application to Aircraft Wing Rock Motion.
- Author
-
Liu, Lei, Liu, Yan-Jun, Tong, Shaocheng, and Gao, Zhiwei
- Abstract
This article concentrates on the event-driven controller design problem for a class of nonlinear single input single output parametric systems with full state constraints. A varying threshold for the triggering mechanism is exploited, which makes the communication more flexible. Moreover, from the viewpoint of energy conservation and consumption reduction, the system capability becomes better owing to the contribution of the proposed event-triggered mechanism. In the meantime, the developed control strategy can avoid the Zeno behavior since the lower bound of the sample time is provided. The considered plant is in a lower triangular form, in which the match condition is not satisfied. To ensure that all the states retain in a predefined region, a barrier Lyapunov function (BLF) based adaptive control law is developed. Due to the existence of the parametric uncertainties, an adaptive algorithm is presented as an estimated tool. All the signals appearing in the closed-loop systems are then proven to be bounded. Meanwhile, the output of the system can track a given signal as far as possible. In the end, the effectiveness of the proposed approach is validated by an aircraft wing rock motion system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
45. Time-varying IBLFs-based adaptive control of uncertain nonlinear systems with full state constraints
- Author
-
Lei Ma, Yan-Jun Liu, Shaocheng Tong, Tingting Gao, Lei Liu, and C. L. Philip Chen
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Adaptive control ,Computer science ,020208 electrical & electronic engineering ,Stability (learning theory) ,02 engineering and technology ,Constraint (information theory) ,symbols.namesake ,Nonlinear system ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,Backstepping ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Electrical and Electronic Engineering ,Adaptation (computer science) ,Mean value theorem - Abstract
This paper presents an adaptive control design for nonlinear systems with time-varying full state constraints. It is the first time to introduce the novel time-varying Integral Barrier Lyapunov functions (TVIBLFs) into the adaptive control design, which not only overcomes the limitation of conservatism existing in the traditional BLFs, but also guarantees that the full state time-varying constraint bounds are not violated. The TVIBLFs are combined with the backstepping design procedure to construct the controllers and adaptation laws, and the integral mean value theorem is used to differentiate TVIBLFs. It can be proven that all the states are forced in the time-varying regions and the stability of the closed-loop system is achieved. The effectiveness of the proposed adaptive control strategy can be illustrated through a simulation example.
- Published
- 2021
46. Adaptive Fuzzy Asymptotic Control of MIMO Systems With Unknown Input Coefficients Via a Robust Nussbaum Gain-Based Approach
- Author
-
Yan-Jun Liu, Kan Xie, Yun Zhang, Zhi Liu, Ci Chen, and C. L. Philip Chen
- Subjects
0209 industrial biotechnology ,Mathematical optimization ,Adaptive control ,Applied Mathematics ,MIMO ,02 engineering and technology ,Fuzzy control system ,Fuzzy logic ,Nonlinear system ,020901 industrial engineering & automation ,Computational Theory and Mathematics ,Artificial Intelligence ,Control and Systems Engineering ,Robustness (computer science) ,Control theory ,Control system ,Adaptive system ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Mathematics - Abstract
This paper proposes an adaptive fuzzy asymptotic control method for multiple input multiple output (MIMO) nonlinear systems with unknown input coefficients, with a focus on handling unknown input nonlinearities and control directions. For all the existing Nussbaum gain-based approaches, it is difficult to investigate unknown input coefficients problem since multiple time-varying coefficients and disturbances coexist and should be simultaneously tackled in the stability analysis. To overcome the above difficulty, we propose a robust Nussbaum gain-based approach for the adaptive fuzzy asymptotic control of MIMO nonlinear systems. Benefiting from the proposed Nussbaum gain-based approach, bounded disturbances including unmodeled system dynamics and universal approximation errors are handled. Furthermore, the proposed approach helps extend the bounded fuzzy control result to the asymptotic convergence. Hence, both the control robustness and control accuracy are prompted within the frame of the developed Nussbaum gain approach. Finally, a simulation example is carried out to illustrate the effectiveness of the proposed control method.
- Published
- 2017
47. Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints
- Author
-
Shaocheng Tong, C. L. Philip Chen, Dong-Juan Li, Dapeng Li, and Yan-Jun Liu
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Time Factors ,Adaptive control ,Computer science ,MIMO ,02 engineering and technology ,Interval (mathematics) ,symbols.namesake ,020901 industrial engineering & automation ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Computer Simulation ,Electrical and Electronic Engineering ,Artificial neural network ,Signal Processing, Computer-Assisted ,Computer Science Applications ,Human-Computer Interaction ,Nonlinear system ,Nonlinear Dynamics ,Control and Systems Engineering ,Bounded function ,symbols ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Software ,Information Systems - Abstract
This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.
- Published
- 2017
48. Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint
- Author
-
Shu-Min Lu, Yan-Jun Liu, and Shaocheng Tong
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Robot kinematics ,Adaptive control ,Artificial neural network ,Computer science ,Stability (learning theory) ,02 engineering and technology ,Computer Science Applications ,Human-Computer Interaction ,Constraint (information theory) ,symbols.namesake ,Nonlinear system ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,Adaptive system ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Robot ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Software - Abstract
An adaptive control-based neural network for a ${n}$ -link robot is studied and the considered robot can be transformed as a class of multi-input–multioutput systems. The position of the robot or the output of the transformed systems is constrained in a time-varying compact set. It is commonly known that the constant constraint belongs to a special case of the time-varying constraint, and thus, it can be more general for handling practical problem as compared with the existing methods for robot. The neural approximation is used to estimate the unknown functions of systems and the time-varying barrier Lyapunov function is used to overcome the violation of constraints. It can prove the stability of the closed-loop systems by using Lyapunov analysis. The feasibility of the approach is demonstrated by performing a simulation example.
- Published
- 2017
49. Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems
- Author
-
Yan-Jun Liu, Shu-Min Lu, Shaocheng Tong, and Dong-Juan Li
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Class (computer programming) ,Mathematical optimization ,Adaptive control ,Computer science ,02 engineering and technology ,Nonlinear control ,Computer Science Applications ,Human-Computer Interaction ,Nonlinear system ,symbols.namesake ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,Backstepping ,Control system ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Software - Abstract
In this paper, we address an adaptive control problem for a class of nonlinear strict-feedback systems with uncertain parameter. The full states of the systems are constrained in the bounded sets and the boundaries of sets are compelled in the asymmetric time-varying regions, i.e., the full state time-varying constraints are considered here. This is for the first time to control such a class of systems. To prevent that the constraints are overstepped, the time-varying asymmetric barrier Lyapunov functions (TABLFs) are employed in each step of the backsstepping design and we also establish a novel control TABLF scheme to ensure the asymptotic output tracking performance. The performances of the adaptive TABLF-based control are verified by a simulation example.
- Published
- 2017
50. Fuzzy tracking adaptive control of discrete-time switched nonlinear systems
- Author
-
Hao Wang, Yan-Jun Liu, Zhifeng Wang, and Shaocheng Tong
- Subjects
0209 industrial biotechnology ,Adaptive neuro fuzzy inference system ,Adaptive control ,Logic ,SIGNAL (programming language) ,02 engineering and technology ,Fuzzy logic ,Nonlinear system ,020901 industrial engineering & automation ,Discrete time and continuous time ,Artificial Intelligence ,Control theory ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Mathematics - Abstract
This paper is concerned with the problem of fuzzy tracking adaptive control for a class of discrete-time switched uncertain nonlinear systems with arbitrary switching. Based on the common Lyapunov function method and by utilizing the fuzzy logic systems to approximate the unknown nonlinear functions, an adaptive fuzzy controller is firstly constructed for a class of uncertain nonlinear discrete-time switched systems. Meanwhile, the proposed adaptive control algorithm reduces the amount of online adjustable parameters. Based on the above techniques, the constructed fuzzy controllers and the adaptive law can guarantee that all the signals are bounded and the system output can converge to a small neighborhood of the reference signal in the closed-loop system. An illustrative example is provided to demonstrate the effectiveness of the proposed approaches.
- Published
- 2017
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