15 results on '"Liu, Yan-jun"'
Search Results
2. PDE Based Adaptive Control of Flexible Riser System With Input Backlash and State Constraints.
- Author
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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
3. Adaptive Finite-Time Neural Network Control of Nonlinear Systems With Multiple Objective Constraints and Application to Electromechanical System.
- Author
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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
4. Observer-Based Adaptive Neural Output Feedback Constraint Controller Design for Switched Systems Under Average Dwell Time.
- Author
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Liu, Lei, Cui, Yujie, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
PSYCHOLOGICAL feedback ,TRACKING control systems ,LYAPUNOV functions ,NONLINEAR systems ,ADAPTIVE fuzzy control ,ARTIFICIAL neural networks - Abstract
Aiming at a class of switched uncertain nonlinear strict-feedback systems under the action of average dwell time switching signal, this paper proposes a novel adaptive neural network output feedback tracking control based on the consideration of the full state constraints. The controller is proposed based on neural networks. One of the key characteristics of the system discussed is that the state variables cannot be measured and the system states need to be kept within the constraint ranges. For the sake of estimating the unmeasured states, the observer is constructed. In order to ensure all states which are within the time-varying boundary, the tangent barrier Lyapunov function (BLF-Tan) is selected in the design process. The boundedness of the closed-loop signals with average dwell time is guaranteed by the designed controllers and all the states limit in their constrained sets. It has been proved that the output tracking error converge to a small neighborhood of zero. In addition, the significance of the presented control strategy is verified and tested by a simulation example. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
5. Adaptive Neural Network Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints.
- Author
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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
6. Adaptive Neural Network Control for Uncertain Time-Varying State Constrained Robotics Systems.
- Author
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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
7. Adaptive Neural Network Learning Controller Design for a Class of Nonlinear Systems With Time-Varying State Constraints.
- Author
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Liu, Yan-Jun, Ma, Lei, Liu, Lei, Tong, Shaocheng, and Chen, C. L. Philip
- Subjects
- *
DISCRETE-time systems , *TIME-varying systems , *STATE feedback (Feedback control systems) , *NONLINEAR systems , *LYAPUNOV stability , *LYAPUNOV functions , *ADAPTIVE control systems , *REINFORCEMENT learning - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
8. Neural Network Controller Design for a Class of Nonlinear Delayed Systems With Time-Varying Full-State Constraints.
- Author
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Li, Dapeng, Chen, C. L. Philip, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
TIME-varying systems ,NONLINEAR systems ,ADAPTIVE control systems ,LYAPUNOV functions ,ARTIFICIAL neural networks ,TIME delay systems - Abstract
This paper proposes an adaptive neural control method for a class of nonlinear time-varying delayed systems with time-varying full-state constraints. To address the problems of the time-varying full-state constraints and time-varying delays in a unified framework, an adaptive neural control method is investigated for the first time. The problems of time delay and constraint are the main factors of limiting the system performance severely and even cause system instability. The effect of unknown time-varying delays is eliminated by using appropriate Lyapunov–Krasovskii functionals. In addition, the constant constraint is the only special case of time-varying constraint which leads to more complex and difficult tasks. To guarantee the full state always within the time-varying constrained interval, the time-varying asymmetric barrier Lyapunov function is employed. Finally, two simulation examples are given to confirm the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
9. Adaptive Fuzzy Output Feedback Control for a Class of Nonlinear Systems With Full State Constraints.
- Author
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Liu, Yan-Jun, Gong, Mingzhe, Tong, Shaocheng, Chen, C. L. Philip, and Li, Dong-Juan
- Subjects
LYAPUNOV functions ,NONLINEAR systems ,ADAPTIVE control systems ,FUZZY systems ,FEEDBACK control systems - Abstract
In the paper, the adaptive observer and controller designs based fuzzy approximation are studied for a class of uncertain nonlinear systems in strict feedback. The main properties of the considered systems are that all the state variables are not available for measurement and at the same time, they are required to limit in each constraint set. Due to the properties of systems, it will be a difficult task for designing the controller and the stability analysis. Based on the structure of the considered systems, a fuzzy state observer is framed to estimate the unmeasured states. To ensure that all the states do not violate their constraint bounds, the Barrier type of functions will be employed in the controller and the adaptation laws. In the stability analysis, the effect caused by the constraints for all the states can be overcome by using the Barrier Lyapunov functions. Based on the proposed control approach, it is proved that the system output is driven to track the reference signal to a bounded compact set, all the signals in the closed-loop system are guaranteed to be bounded, and all the states do not transgress their constrained sets. The effectiveness of the proposed control approach can be verified by setting a simulation example. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
10. Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint.
- Author
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Liu, Yan-Jun, Lu, Shumin, and Tong, Shaocheng
- Subjects
- *
ARTIFICIAL neural networks , *MIMO systems , *LYAPUNOV functions - 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. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
11. Adaptive Fuzzy Control for a Class of Nonlinear Discrete-Time Systems With Backlash.
- Author
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Liu, Yan-Jun and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,UNCERTAINTY ,APPROXIMATION theory ,ESTIMATION theory ,LYAPUNOV functions - Abstract
An adaptive fuzzy controller design is studied for uncertain nonlinear systems in this paper. The considered systems are of the discrete-time form in a triangular structure and include the backlash and the external disturbance. By using the prediction function of future states, the systems are transformed into an n-step ahead predictor. The fuzzy logic systems (FLSs) are used to approximate the unknown functions, unknown backlash, and backlash inversion, respectively. A discrete-time tuning algorithm is developed to estimate the optimal fuzzy parameters. Compared with the previous works for the discrete-time systems with backlash, the main contributions of the paper are that 1) the rigorous restriction for the functional estimation error is removed, and 2) the external disturbance is bounded, but the bound is not required to be known. A novel controller and the adaptation laws are constructed by using the discrete Taylor series expansion and the difference Lyapunov analysis, and thus, those limitations in the previous works are overcome. It is proven that all the signals in the closed-loop system are bounded and that the system output can be to follow the reference signal to a bounded compact set. A simulation example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
12. Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints.
- Author
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Liu, Yan-Jun, Li, Jing, Tong, Shaocheng, and Chen, C. L. Philip
- Subjects
- *
NONLINEAR systems , *NEURAL circuitry , *LYAPUNOV functions , *SIMULATION methods & models , *CLOSED loop systems - Abstract
In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
13. A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input.
- Author
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Liu, Yan-Jun, Gao, Ying, Tong, Shaocheng, and Chen, C. L. Philip
- Subjects
- *
ADAPTIVE computing systems , *NONLINEAR systems , *DISCRETE-time systems , *ARTIFICIAL neural networks , *LYAPUNOV functions - Abstract
In this paper, an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete-time systems, which contain unknown functions, unknown dead-zone input, and unknown control direction. Different from linear dead zone, the dead zone, in this paper, is a kind of nonlinear dead zone. To overcome the noncausal problem, which leads to the control scheme infeasible, the systems can be transformed into a $m$ -step-ahead predictor. Due to nonlinear dead-zone appearance, the transformed predictor still contains the nonaffine function. In addition, it is assumed that the gain function of dead-zone input and the control direction are unknown. These conditions bring about the difficulties and the complicacy in the controller design. Thus, the implicit function theorem is applied to deal with nonaffine dead-zone appearance, the problem caused by the unknown control direction can be resolved through applying the discrete Nussbaum gain, and the neural networks are used to approximate the unknown function. Based on the Lyapunov theory, all the signals of the resulting closed-loop system are proved to be semiglobal uniformly ultimately bounded. Moreover, the tracking error is proved to be regulated to a small neighborhood around zero. The feasibility of the proposed approach is demonstrated by a simulation example. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
14. Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems.
- Author
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Liu, Yan-Jun, Tong, Shao-Cheng, Wang, Dan, Li, Tie-Shan, and Chen, C. L. Philip
- Subjects
- *
ADAPTIVE control systems , *FEEDBACK control systems , *OBSERVABILITY (Control theory) , *UNCERTAINTY (Information theory) , *NONLINEAR systems , *SIMULATION methods & models , *ARTIFICIAL neural networks , *LYAPUNOV functions - Abstract
An adaptive output feedback control is studied for uncertain nonlinear single-input–single-output systems with partial unmeasured states. In the scheme, a reduced-order observer (ROO) is designed to estimate those unmeasured states. By employing radial basis function neural networks and incorporating the ROO into a new backstepping design, an adaptive output feedback controller is constructively developed. A prominent advantage is its ability to balance the control action between the state feedback and the output feedback. In addition, the scheme can be still implemented when all the states are not available. The stability of the closed-loop system is guaranteed in the sense that all the signals are semiglobal uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
15. Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems.
- Author
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Liu, Yan-Jun, Chen, C. L. Philip, Wen, Guo-Xing, and Tong, Shaocheng
- Subjects
- *
ARTIFICIAL neural networks , *NONLINEAR systems , *APPROXIMATION theory , *FEEDBACK control systems , *SIMULATION methods & models , *MIMO systems , *LYAPUNOV functions - Abstract
This brief studies an adaptive neural output feedback tracking control of uncertain nonlinear multi-input–multi-output (MIMO) systems in the discrete-time form. The considered MIMO systems are composed of n subsystems with the couplings of inputs and states among subsystems. In order to solve the noncausal problem and decouple the couplings, it needs to transform the systems into a predictor form. The higher order neural networks are utilized to approximate the desired controllers. By using Lyapunov analysis, it is proven that all the signals in the closed-loop system is the semi-globally uniformly ultimately bounded and the output errors converge to a compact set. In contrast to the existing results, the advantage of the scheme is that the number of the adjustable parameters is highly reduced. The effectiveness of the scheme is verified by a simulation example. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
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