117 results on '"Yan, Jun"'
Search Results
2. Relative Threshold-Based Event-Triggered Control for Nonlinear Constrained Systems With Application to Aircraft Wing Rock Motion
- Author
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Zhiwei Gao, Lei Liu, Yan-Jun Liu, and Shaocheng Tong
- Subjects
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.
- Published
- 2022
3. Fuzzy Observer Constraint Based on Adaptive Control for Uncertain Nonlinear MIMO Systems With Time-Varying State Constraints
- Author
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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
4. Adaptive NN control based on Butterworth low-pass filter for quarter active suspension systems with actuator failure
- Author
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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.
- Published
- 2021
5. 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
- Subjects
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.
- Published
- 2020
6. Reinforcement Learning Neural Network-Based Adaptive Control for State and Input Time-Delayed Wheeled Mobile Robots
- Author
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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.
- Published
- 2020
7. Actuator Failure Compensation-Based Adaptive Control of Active Suspension Systems With Prescribed Performance
- Author
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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.
- Published
- 2020
8. Adaptive neural network control for nonlinear state constrained systems with unknown dead-zones input
- Author
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Wei Zhao, Yan-Jun Liu, and Lei Liu
- Subjects
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
9. 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
10. 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
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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
11. 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
12. 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
13. 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
14. Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input
- Author
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Shaocheng Tong, Yan-Jun Liu, C. L. Philip Chen, and Shu Li
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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
15. Adaptive Vehicle Stability Control of Half-Car Active Suspension Systems With Partial Performance Constraints
- Author
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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
16. Adaptive Fuzzy Output-Feedback Control for Switched Uncertain Nonlinear Systems With Full-State Constraints.
- Author
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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
17. IBLF-Based Adaptive Neural Control of State-Constrained Uncertain Stochastic Nonlinear Systems.
- Author
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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
18. Adaptive Finite-Time Neural Network Control of Nonlinear Systems With Multiple Objective Constraints and Application to Electromechanical System
- Author
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Yue-Ying Wang, Lei Liu, Wei Zhao, Shaocheng Tong, and Yan-Jun Liu
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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
19. Adaptive NN Control Using Integral Barrier Lyapunov Functionals for Uncertain Nonlinear Block-Triangular Constraint Systems
- Author
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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
20. Distributed adaptive fuzzy control for multi-agent systems with full state constraints and unmeasured states.
- Author
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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
21. Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints
- Author
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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
22. Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint
- Author
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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
23. Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems
- Author
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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
24. Model Identification and Control Design for a Humanoid Robot
- Author
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Wei He, Yan-Jun Liu, Chenguang Yang, Changyin Sun, Yunchuan Li, and Weiliang Ge
- Subjects
0209 industrial biotechnology ,Robot kinematics ,Adaptive control ,Computer science ,media_common.quotation_subject ,System identification ,Particle swarm optimization ,02 engineering and technology ,Inertia ,Computer Science Applications ,Robot control ,Computer Science::Robotics ,Human-Computer Interaction ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Trajectory ,Robot ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Software ,Humanoid robot ,media_common - Abstract
In this paper, model identification and adaptive control design are performed on Devanit-Hartenberg model of a humanoid robot. We focus on the modeling of the 6 degree-of-freedom upper limb of the robot using recursive Newton-Euler (RNE) formula for the coordinate frame of each joint. To obtain sufficient excitation for modeling of the robot, the particle swarm optimization method has been employed to optimize the trajectory of each joint, such that satisfied parameter estimation can be obtained. In addition, the estimated inertia parameters are taken as the initial values for the RNE-based adaptive control design to achieve improved tracking performance. Simulation studies have been carried out to verify the result of the identification algorithm and to illustrate the effectiveness of the control design.
- Published
- 2017
25. Adaptive Event-Triggered Control for Unknown Second-Order Nonlinear Multiagent Systems.
- Author
-
Li, Zhenxing, Yan, Jun, Yu, Wenwu, and Qiu, Jianlong
- Abstract
This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader–follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
26. Optimal Control-Based Adaptive NN Design for a Class of Nonlinear Discrete-Time Block-Triangular Systems
- Author
-
Yan-Jun Liu and Shaocheng Tong
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Mathematical optimization ,Adaptive control ,02 engineering and technology ,Linear-quadratic-Gaussian control ,Optimal control ,Computer Science Applications ,Human-Computer Interaction ,symbols.namesake ,020901 industrial engineering & automation ,Design objective ,Discrete time and continuous time ,Control and Systems Engineering ,Control theory ,Adaptive system ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Gradient descent ,Software ,Information Systems ,Mathematics - Abstract
In this paper, we propose an optimal control scheme-based adaptive neural network design for a class of unknown nonlinear discrete-time systems. The controlled systems are in a block-triangular multi-input-multi-output pure-feedback structure, i.e., there are both state and input couplings and nonaffine functions to be included in every equation of each subsystem. The design objective is to provide a control scheme, which not only guarantees the stability of the systems, but also achieves optimal control performance. The main contribution of this paper is that it is for the first time to achieve the optimal performance for such a class of systems. Owing to the interactions among subsystems, making an optimal control signal is a difficult task. The design ideas are that: 1) the systems are transformed into an output predictor form; 2) for the output predictor, the ideal control signal and the strategic utility function can be approximated by using an action network and a critic network, respectively; and 3) an optimal control signal is constructed with the weight update rules to be designed based on a gradient descent method. The stability of the systems can be proved based on the difference Lyapunov method. Finally, a numerical simulation is given to illustrate the performance of the proposed scheme.
- Published
- 2016
27. PDE Based Adaptive Control of Flexible Riser System With Input Backlash and State Constraints.
- Author
-
Tang, Li, Zhang, Xin-Yu, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
RISER pipe ,ADAPTIVE control systems ,PARTIAL differential equations ,LYAPUNOV functions ,LYAPUNOV stability ,STABILITY theory - Abstract
In this paper, a class of flexible riser systems modeled by partial differential equations (PDEs) with the backlash is considered. The backlash is formulated as the addition of a linear input and a interference-like term, then an new auxiliary item is introduced to compensate for the impact of this backlash. In addition, the constraint problem for the position and the velocity is also taken into consideration. To solve this constrain problem, the logarithmic barrier Lyapunov function is employed. For the flexible riser system, two kinds of adaptive controllers are proposed under the following two cases. One controller is designed when only the parameter of backlash is unknown. On the basis of this result, the other controller is presented when some system parameters cannot be measured through actual measurement. Then, combing the theory of Lyapunov stability, the two controllers can guarantee the boundedness of all signals in the closed-loop flexible riser system. Further, both the position and the velocity satisfy their corresponding constraint condition. Finally, the simulation example verifies that the proposed control method is effective. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
28. Adaptive Neural Network Learning Controller Design for a Class of Nonlinear Systems With Time-Varying State Constraints
- Author
-
Lei Liu, Yan-Jun Liu, Shaocheng Tong, Lei Ma, and C. L. Philip Chen
- Subjects
Lyapunov stability ,Adaptive control ,Artificial neural network ,Computer simulation ,Computer Networks and Communications ,Computer science ,02 engineering and technology ,Computer Science Applications ,Nonlinear system ,Artificial Intelligence ,Control theory ,Backstepping ,0202 electrical engineering, electronic engineering, information engineering ,Uniform boundedness ,020201 artificial intelligence & image processing ,Software - Abstract
This paper studies an adaptive neural network (NN) tracking control method for a class of uncertain nonlinear strict-feedback systems with time-varying full-state constraints. As we all know, the states are inevitably constrained in the actual systems because of the safety and performance factors. The main contributions of this paper are that: 1) in order to ensure that the states do not violate the asymmetric time-varying constraint regions, an adaptive NN controller is constructed by introducing the asymmetric time-varying barrier Lyapunov function (TVBLF) and 2) the amount of the learning parameters is reduced by introducing a TVBLF at each step of the backstepping. Based on the Lyapunov stability analysis, it can be proven that all the signals in the closed-loop system are the semiglobal ultimately uniformly bounded and the time-varying full-state constraints are never violated. Finally, a numerical simulation is given, and the effectiveness of this adaptive control method can be verified.
- Published
- 2019
29. Finite-Time Convergence Adaptive Neural Network Control for Nonlinear Servo Systems
- Author
-
Jing Na, Shubo Wang, Xuemei Ren, Yingbo Huang, and Yan-Jun Liu
- Subjects
Adaptive control ,Friction ,Computer science ,02 engineering and technology ,Servomotor ,Servomechanism ,law.invention ,Feedback ,Tracking error ,Robustness (computer science) ,law ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Computer Simulation ,Electrical and Electronic Engineering ,Artificial neural network ,Estimation theory ,020208 electrical & electronic engineering ,Computer Science Applications ,Human-Computer Interaction ,Nonlinear system ,Nonlinear Dynamics ,Control and Systems Engineering ,Control system ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Gradient descent ,Software ,Algorithms ,Information Systems - Abstract
Although adaptive control design with function approximators, for example, neural networks (NNs) and fuzzy logic systems, has been studied for various nonlinear systems, the classical adaptive laws derived based on the gradient descent algorithm with ${\sigma }$ -modification or ${e}$ -modification cannot guarantee the parameter estimation convergence. These nonconvergent learning methods may lead to sluggish response in the control system and make the parameter tuning complex. The aim of this paper is to propose a new learning strategy driven by the estimation error to design the alternative adaptive laws for adaptive control of nonlinear servo systems. The parameter estimation error is extracted and used as a new leakage term in the adaptive laws. By using this new learning method, the convergence of both the estimated parameters and the tracking error can be achieved simultaneously. The proposed learning algorithm is further tailored to retain finite-time convergence. To handle unknown nonlinearities in the servomechanisms, an augmented NN with a new friction model is used, where both the NN weights and some friction model coefficients are estimated online via the proposed algorithms. Comparisons with the ${\sigma }$ -modification algorithm are addressed in terms of convergence property and robustness. Simulations and practical experiments are given to show the superior performance of the suggested adaptive algorithms.
- Published
- 2019
30. Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs
- Author
-
Yan-Jun Liu, C. L. Philip Chen, Dong-Juan Li, and Shaocheng Tong
- Subjects
0209 industrial biotechnology ,Adaptive control ,MIMO ,02 engineering and technology ,Nonlinear control ,020901 industrial engineering & automation ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Computer Simulation ,Radial basis function ,Electrical and Electronic Engineering ,Mathematics ,Artificial neural network ,Models, Theoretical ,Computer Science Applications ,Human-Computer Interaction ,Nonlinear system ,Nonlinear Dynamics ,Control and Systems Engineering ,Backstepping ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Intelligent control ,Algorithms ,Software ,Information Systems - Abstract
This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of ${N}$ subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the studied systems consider the effects of Prandtl–Ishlinskii (PI) hysteresis model. It is for the first time to study the control problem for such a class of systems. In addition, the proposed scheme removes an important assumption imposed on the previous works that the bounds of the parameters in PI hysteresis are known. The radial basis functions neural networks are employed to approximate unknown functions. The adaptation laws and the controllers are designed by employing the backstepping technique. The closed-loop system can be proven to be stable by using Lyapunov theorem. A simulation example is studied to validate the effectiveness of the scheme.
- Published
- 2016
31. Adaptive Finite-Time Neural Network Control of Nonlinear Systems With Multiple Objective Constraints and Application to Electromechanical System.
- Author
-
Liu, Lei, Zhao, Wei, Liu, Yan-Jun, Tong, Shaocheng, and Wang, Yue-Ying
- Subjects
NONLINEAR systems ,ADAPTIVE control systems ,LYAPUNOV functions ,ARTIFICIAL neural networks ,DYNAMICAL systems ,PSYCHOLOGICAL feedback - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
32. Neural‐network‐based adaptive leader‐following consensus control for second‐order non‐linear multi‐agent systems
- Author
-
C. L. Philip Chen, Zhi Liu, Yan-Jun Liu, and Guoxing Wen
- Subjects
Lyapunov stability ,Control and Optimization ,Adaptive control ,Artificial neural network ,Computer simulation ,Computer science ,Multi-agent system ,Numerical analysis ,Computer Science Applications ,Computer Science::Multiagent Systems ,Human-Computer Interaction ,Nonlinear system ,Matrix (mathematics) ,Control and Systems Engineering ,Control theory ,Electrical and Electronic Engineering - Abstract
In this study, a novel adaptive neural network (NN)-based leader-following consensus approach is proposed for a class of non-linear second-order multi-agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN-based adaptive consensus algorithms require the number of NN nodes to must be large enough, and thus the online computation burden often are very heavy. However, the proposed adaptive consensus scheme can greatly reduce the online computation burden, because the adaptive adjusting parameters are designed in scalar form, which is the norm of the estimation of the optimal NN weight matrix. According to Lyapunov stability theory, the proposed approach can guarantee the leader-following consensus behaviour of non-linear second-order multi-agent systems to be obtained. Finally, a numerical simulation and a multi-manipulator simulation are carried out to further demonstrate the effectiveness of the proposed consensus approach.
- Published
- 2015
33. Adaptive Neural Control Using Tangent Time-Varying BLFs for a Class of Uncertain Stochastic Nonlinear Systems With Full State Constraints.
- Author
-
Gao, Tingting, Liu, Yan-Jun, Li, Dapeng, Tong, Shaocheng, and Li, Tieshan
- Abstract
In this paper, an adaptive neural network (NN) control scheme is developed for a class of stochastic nonlinear systems with time-varying full state constraints. In the controller design, RBF NNs are employed to approximate the unknown terms, and the backtracking technique is introduced to overcome the restriction of matching conditions. At the same time, tangent type time-varying barrier Lyapunov functions (tan-TVBLFs) are constructed to ensure the full state constraints are never violated, where tan-TVBLFs are beneficial to integrate constraint analysis into a common method. Furthermore, the Lyapunov stability theory is used to prove that all closed-loop signals are semiglobal uniformly ultimately bounded in probability and error signals remain in the compact set do not violate the time-varying constraints. A simulation example will be used to exhibit the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
34. Adaptive Vehicle Stability Control of Half-Car Active Suspension Systems With Partial Performance Constraints.
- Author
-
Zeng, Qiang, Liu, Yan-Jun, and Liu, Lei
- Subjects
- *
MOTOR vehicle springs & suspension , *STABILITY theory , *LYAPUNOV stability , *CLOSED loop systems , *CONTINUOUS functions , *HYPERSONIC planes - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
35. Fuzzy Observer Constraint Based on Adaptive Control for Uncertain Nonlinear MIMO Systems With Time-Varying State Constraints.
- Author
-
Liu, Yan-Jun, Gong, Mingzhe, Liu, Lei, Tong, Shaocheng, and Chen, C. L. Philip
- 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
36. An Adaptive Neural Network Controller for Active Suspension Systems With Hydraulic Actuator.
- Author
-
Liu, Yan-Jun, Zeng, Qiang, Liu, Lei, and Tong, Shaocheng
- Subjects
- *
MOTOR vehicle springs & suspension , *SERVOMECHANISMS , *ACTUATORS , *ATTITUDE (Psychology) , *ADAPTIVE control systems , *AUTOMOBILE dynamics - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
37. Reinforcement Learning Neural Network-Based Adaptive Control for State and Input Time-Delayed Wheeled Mobile Robots.
- Author
-
Li, Shu, Ding, Liang, Gao, Haibo, Liu, Yan-Jun, Li, Nan, and Deng, Zongquan
- Subjects
TRACKING control systems ,MOBILE robots ,ADAPTIVE control systems ,REINFORCEMENT learning ,TIME delay systems ,RADIAL basis functions - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
38. Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems
- Author
-
Shu Li, Yan-Jun Liu, C. L. Philip Chen, and Shaocheng Tong
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Adaptive control ,Artificial neural network ,Computer Networks and Communications ,Computer science ,Stability (learning theory) ,Approximation algorithm ,02 engineering and technology ,Computer Science Applications ,Nonlinear system ,symbols.namesake ,020901 industrial engineering & automation ,Discrete time and continuous time ,Artificial Intelligence ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Uniform boundedness ,020201 artificial intelligence & image processing ,Software - Abstract
In this paper, an adaptive control approach-based neural approximation is developed for a class of uncertain nonlinear discrete-time (DT) systems. The main characteristic of the considered systems is that they can be viewed as a class of multi-input multioutput systems in the nonstrict feedback structure. The similar control problem of this class of systems has been addressed in the past, but it focused on the continuous-time systems. Due to the complicacies of the system structure, it will become more difficult for the controller design and the stability analysis. To stabilize this class of systems, a new recursive procedure is developed, and the effect caused by the noncausal problem in the nonstrict feedback DT structure can be solved using a semirecurrent neural approximation. Based on the Lyapunov difference approach, it is proved that all the signals of the closed-loop system are semiglobal, ultimately uniformly bounded, and a good tracking performance can be guaranteed. The feasibility of the proposed controllers can be validated by setting a simulation example.
- Published
- 2017
39. Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks
- Author
-
Yan-Jun Liu, C. L. Philip Chen, Fei-Yue Wang, and Guo-Xing Wen
- Subjects
Lyapunov stability ,Lyapunov function ,Adaptive control ,Artificial neural network ,Computer Networks and Communications ,Multi-agent system ,Linear system ,Nonlinear control ,Computer Science Applications ,Computer Science::Multiagent Systems ,symbols.namesake ,Nonlinear system ,Computer Science::Systems and Control ,Artificial Intelligence ,Control theory ,symbols ,Software ,Mathematics - Abstract
Because of the complicity of consensus control of nonlinear multiagent systems in state time-delay, most of previous works focused only on linear systems with input time-delay. An adaptive neural network (NN) consensus control method for a class of nonlinear multiagent systems with state time-delay is proposed in this paper. The approximation property of radial basis function neural networks (RBFNNs) is used to neutralize the uncertain nonlinear dynamics in agents. An appropriate Lyapunov–Krasovskii functional, which is obtained from the derivative of an appropriate Lyapunov function, is used to compensate the uncertainties of unknown time delays. It is proved that our proposed approach guarantees the convergence on the basis of Lyapunov stability theory. The simulation results of a nonlinear multiagent time-delay system and a multiple collaborative manipulators system show the effectiveness of the proposed consensus control algorithm.
- Published
- 2014
40. Adaptive fuzzy optimal control using direct heuristic dynamic programming for chaotic discrete-time system
- Author
-
Ying Gao and Yan-Jun Liu
- Subjects
Lyapunov function ,0209 industrial biotechnology ,Mathematical optimization ,Adaptive control ,Property (programming) ,Mechanical Engineering ,Stability (learning theory) ,Chaotic ,Aerospace Engineering ,02 engineering and technology ,Function (mathematics) ,Nonlinear Sciences::Chaotic Dynamics ,Tracking error ,symbols.namesake ,020901 industrial engineering & automation ,Mechanics of Materials ,Control theory ,Automotive Engineering ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,020201 artificial intelligence & image processing ,General Materials Science ,Mathematics - Abstract
In this paper, we aim to solve the optimal tracking control problem for the Henon Mapping chaotic system using the direct heuristic dynamic programming (DHDP) setting with filtered tracking error. The fuzzy logic system is used to approximate the long-term utility function. Compared with the results for chaotic discrete-time system, the cost of the controller is reduced. The Lyapunov analysis approach is utilized to prove the stability of the chaotic system. It is shown that the tracking error, the adaptation law and the control input retain the property of uniformly ultimate boundedness. A simulation example is given to demonstrate the effectiveness of the proposed approach.
- Published
- 2014
41. Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems
- Author
-
Guo-Xing Wen, Yan-Jun Liu, and C. L. Philip Chen
- Subjects
Lyapunov function ,Stochastic Processes ,Mathematical optimization ,Adaptive neuro fuzzy inference system ,Adaptive control ,Stochastic process ,Networked control system ,Computer Science Applications ,Human-Computer Interaction ,symbols.namesake ,Fuzzy Logic ,Nonlinear Dynamics ,Control and Systems Engineering ,Control theory ,Backstepping ,Bounded function ,symbols ,Neural Networks, Computer ,Electrical and Electronic Engineering ,Stochastic neural network ,Algorithms ,Software ,Information Systems ,Mathematics - Abstract
This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.
- Published
- 2014
42. Decentralised adaptive control of cooperating Robotic manipulators with disturbance observers
- Author
-
Shuming Deng, Zhangguo Yu, Min Wang, Chun-Yi Su, Zhijun Li, Guanglin Li, and Yan-Jun Liu
- Subjects
Control and Optimization ,Adaptive control ,Disturbance (geology) ,Computer science ,Robot manipulator ,Uncertain systems ,Control engineering ,Motion (physics) ,Computer Science Applications ,Human-Computer Interaction ,Control and Systems Engineering ,Control theory ,Disturbance observer ,Trajectory ,Electrical and Electronic Engineering ,Adaptation (computer science) - Abstract
In this study, the authors present decentralised adaptive controllers for two cooperating robotic manipulators moving an object with constrained trajectory/force in the presence of dynamics uncertainties and external disturbances. The cooperating manipulators are described as an aggregation of subsystems. For control design, first a decentralised local dynamics coupled with physical interactions between subsystems is developed, and then a decentralised adaptive control merging parameter adaptation and disturbance observer is constructed, such that motion and force trajectories converge to the desired manifolds and the effect of non-parametrisable uncertainties is compensated by the disturbance observer. Experiment studies are carried out to show the efficiency of the control design.
- Published
- 2014
43. Event-Triggered Adaptive Output Regulation for a Class of Nonlinear Systems With Unknown Control Direction.
- Author
-
Lei, Yan, Wang, Yan-Wu, Guan, Zhi-Hong, and Shen, Yan-Jun
- Subjects
NONLINEAR systems ,ADAPTIVE control systems ,UNCERTAIN systems ,ROBUST control ,JUDGE-made law ,ADAPTIVE fuzzy control ,LINEAR systems - Abstract
In this paper, the global robust output regulation problem of a class of uncertain nonlinear systems is investigated by the event-triggered adaptive control law for the case of unknown control direction. The Nussbaum-type function-based technique is proposed to tackle the presence of unknown control direction. Then, by applying the adaptive control technique and the internal model principle, a new event-triggered adaptive control method is proposed. With the proposed corresponding event-triggered mechanism, the output regulation of the nonlinear systems can be realized regardless of the unknown control direction, meanwhile the Zeno behavior can be excluded. Finally, a numerical example is presented to verify the effectiveness of the proposed control law. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
44. Actuator Failure Compensation-Based Adaptive Control of Active Suspension Systems With Prescribed Performance.
- Author
-
Liu, Yan-Jun, Zeng, Qiang, Tong, Shaocheng, Chen, C. L. Philip, and Liu, Lei
- Subjects
- *
ADAPTIVE control systems , *ACTUATORS , *AUTOMOBILE springs & suspension , *ALGORITHMS , *CONTINUOUS functions , *MAXIMUM power point trackers - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
45. Finite-Time Convergence Adaptive Neural Network Control for Nonlinear Servo Systems.
- Author
-
Na, Jing, Wang, Shubo, Liu, Yan-Jun, Huang, Yingbo, and Ren, Xuemei
- Abstract
Although adaptive control design with function approximators, for example, neural networks (NNs) and fuzzy logic systems, has been studied for various nonlinear systems, the classical adaptive laws derived based on the gradient descent algorithm with ${\sigma }$ -modification or ${e}$ -modification cannot guarantee the parameter estimation convergence. These nonconvergent learning methods may lead to sluggish response in the control system and make the parameter tuning complex. The aim of this paper is to propose a new learning strategy driven by the estimation error to design the alternative adaptive laws for adaptive control of nonlinear servo systems. The parameter estimation error is extracted and used as a new leakage term in the adaptive laws. By using this new learning method, the convergence of both the estimated parameters and the tracking error can be achieved simultaneously. The proposed learning algorithm is further tailored to retain finite-time convergence. To handle unknown nonlinearities in the servomechanisms, an augmented NN with a new friction model is used, where both the NN weights and some friction model coefficients are estimated online via the proposed algorithms. Comparisons with the ${\sigma }$ -modification algorithm are addressed in terms of convergence property and robustness. Simulations and practical experiments are given to show the superior performance of the suggested adaptive algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
46. Adaptive Decentralized Controller Design for a Class of Switched Interconnected Nonlinear Systems.
- Author
-
Zhai, Ding, Liu, Xuan, and Liu, Yan-Jun
- Abstract
This paper is concerned with the switched decentralized adaptive control design problem for switched interconnected nonlinear systems under arbitrary switching, where the actuator failures may occur infinite times and the control directions are allowed to be unknown. By introducing a Nussbaum-type function and an integrable auxiliary signal, a switched decentralized adaptive control scheme is developed to deal with the potentially infinite times of actuator failures and the unknown control directions. The basic idea is to design different parameter update laws and control laws for distinct switched subsystems. It is proved that the state variables of the resulting closed-loop system are asymptotically stable. Finally, a numerical simulation on a double-inverted pendulum model is given to verify the proposed control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
47. Adaptive Neural Network Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints.
- Author
-
Liu, Yan-Jun, Zeng, Qiang, Tong, Shaocheng, Chen, C. L. Philip, and Liu, Lei
- Subjects
- *
TIME-varying systems , *LYAPUNOV functions , *CLOSED loop systems , *SPEED , *ADAPTIVE control systems - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
48. Adaptive Neural Network Control for Uncertain Time-Varying State Constrained Robotics Systems.
- Author
-
Lu, Shu-Min, Li, Da-Peng, and Liu, Yan-Jun
- Subjects
DISCRETE-time systems ,ROBOTICS ,LYAPUNOV functions ,TIME-varying systems - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
49. Adaptive NN Control Without Feasibility Conditions for Nonlinear State Constrained Stochastic Systems With Unknown Time Delays.
- Author
-
Li, Dapeng, Liu, Lei, Liu, Yan-Jun, Tong, Shaocheng, and Chen, C. L. Philip
- 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
50. Adaptive control design for Arneodo chaotic system with state constraint
- Author
-
Lei Liu, Yan-Jun Liu, and Li Tang
- Subjects
Scheme (programming language) ,Mathematical optimization ,Adaptive control ,Current (mathematics) ,Mechanical Engineering ,Chaotic ,Aerospace Engineering ,Nonlinear control ,Constraint (information theory) ,Mechanics of Materials ,Control theory ,Bounded function ,Automotive Engineering ,General Materials Science ,State (computer science) ,computer ,Mathematics ,computer.programming_language - Abstract
This article focuses on the adaptive control problem for Arneodo chaotic systems. It is considered a phenomenon of single state constraint for the systems. In the proposed scheme, the barrier Lyapunov function approach was successfully used to prevent the single state from violating constraint conditions. In the current circumstances, a great many of the results for chaotic systems neglect the situation of constraint. Finally, it is proved that all the signals in the Arneodo chaotic system are bounded. A representative example is proposed in numerical simulations compared with existing results. The performance of the proposed control scheme was validated by using a simulated example.
- Published
- 2013
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