94 results on '"Liu, Yan-jun"'
Search Results
2. Adaptive Fuzzy Control of Nonlinear Systems With Function Constraints Based on Time-Varying IBLFs.
- Author
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Yu, Tianqi, Liu, Yan-Jun, Liu, Lei, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,ADAPTIVE control systems ,FUZZY control systems ,NONLINEAR functions ,TIME-varying systems ,NONLINEAR systems ,PSYCHOLOGICAL feedback - Abstract
In this article, an adaptive tracking control approach is developed for a class of strict-feedback nonlinear systems with time-varying full state constraints. As a breakthrough in this system, the special function constraints (whose constraint boundary is relevant to both state variables and time) are considered, which are rarely studied by research work. And there is no doubt that this method increases the complexity of designing this scheme. Furthermore, the time-varying integral barrier Lyapunov functions combining with backstepping technique is introduced to break the limitation of traditional methods as well as achieve the full state constraints. Meanwhile, fuzzy logic systems are selected to approximate unknown nonlinear functions. It is verified that all closed-loop signals are bounded and all states are forced in the time-varying boundness. In addition, the proposed control strategy has a good performance. The effectiveness of the theoretical analysis results is proved via a simulation example. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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3. Adaptive Event-Triggered Output Feedback Control for Nonlinear Switched Systems Based on Full State Constraints.
- Author
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Liu, Lei, Cui, Yujie, Liu, Yan-Jun, and Tong, Shaocheng
- Abstract
Aiming at the research content of tracking control for a class of nonlinear uncertain switched systems including full state constraints, a novel event-triggered adaptive fuzzy output feedback control scheme is given. The systems studied need to use the approximation principle of fuzzy logic systems (FLSs) to solve the nonlinear smooth function with unknown terms. For ensuring that all states of the systems are within the time-varying constraint limits, the stability of the switched systems is verified by utilizing tangent barrier Lyapunov function (Tan-BLF). Based on the potential barrier Lyapunov function (BLF) and backstepping recursive construction method, the adaptive law, controller and event-triggered mechanism of the subsystem are designed. The proposed method will make that the signal is bounded. Moreover, the tracking error can be adjusted to the neighborhood closed to the origin. Simulation examples indicate the feasibility of the method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Adaptive Fuzzy Fast Finite-Time Formation Control for Second-Order MASs Based on Capability Boundaries of Agents.
- Author
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Lan, Jie, Liu, Yan-Jun, Xu, Tongyu, Tong, Shaocheng, and Liu, Lei
- Subjects
MULTIAGENT systems ,ADAPTIVE fuzzy control ,NONLINEAR dynamical systems ,STABILITY theory ,LYAPUNOV stability ,DIGITAL computer simulation ,FUZZY logic ,INTEGRATORS - Abstract
This article addresses a new adaptive fuzzy fast finite-time state-constraint protocol for leader-follower formation control. Each agent in uncertain nonlinear dynamic multiagent systems is represented by second-order integrator, which synchronously governs its position and velocity. The fuzzy logic systems are employed to compensate and approximate uncertain functions. On the premise of maintaining formation structure and coupling communication topology, time-varying transformation equations containing exponential signals are introduced to ensure that state capability boundaries for different physical quantities of agents are not violated. It not only guarantees own state performance and collision avoidance among agents, but also realizes the specified transient and steady formation performance. Furthermore, focusing on convergence rate, the adaptive fuzzy fast finite-time strategy is designed that can guarantee all agents will follow the desired formation configuration in fast finite-time. Through the abovementioned approaches provide a good way to improve the convergence and ensure the security for decentralized formation control. Finally, the validity of the theoretical method is proved by fast finite-time stable theory and Lyapunov stability theory. The effectiveness of the protocol is verified by digital simulation and simulation comparison. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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5. 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
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- View/download PDF
6. 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
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7. Intelligent Motion Tracking Control of Vehicle Suspension Systems With Constraints via Neural Performance Analysis.
- Author
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Liu, Lei, Zhu, Changqi, Liu, Yan-Jun, and Tong, Shaocheng
- Abstract
A novel adaptive control scheme is developed for active suspension systems (ASSs) based on neural networks (NNs) and backstepping control strategies. Since the springs and piecewise dampers are nonlinear, the unknown internal dynamics are approximated by radial basis function neural networks (RBFNNs). Then, to solve the time-varying constrains of both vertical displacement and corresponding speed in vehicle body, the Tangent Barrier Lyapunov Functions (TBLFs) are incorporated into the controller design. Furthermore, the adaptive controller and adaptive laws are designed to improve the riding comfortable, handling stability and driving safety. In the end, the simulation results show the effectiveness and feasibility of the proposed adaptive algorithm compared with unconstrained adaptive approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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8. Active Suspension Control of Quarter-Car System With Experimental Validation.
- Author
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Na, Jing, Huang, Yingbo, Wu, Xing, Liu, Yan-Jun, Li, Yunpeng, and Li, Guang
- Subjects
MOTOR vehicle springs & suspension ,ACTUATORS ,TRANSIENT analysis - Abstract
A reliable, efficient, and simple control is presented and validated for a quarter-car active suspension system equipped with an electro-hydraulic actuator. Unlike the existing techniques, this control does not use any function approximation, e.g., neural networks (NNs) or fuzzy-logic systems (FLSs), while the unmolded dynamics, including the hydraulic actuator behavior, can be accommodated effectively. Hence, the heavy computational costs and tedious parameter tuning phase can be remedied. Moreover, both the transient and steady-state suspension performance can be retained by incorporating prescribed performance functions (PPFs) into the control implementation. This guaranteed performance is particularly useful for guaranteeing the safe operation of suspension systems. Apart from theoretical studies, some practical considerations of control implementation and several parameter tuning guidelines are suggested. Experimental results based on a practical quarter-car active suspension test-rig demonstrate that this control can obtain a superior performance and has better computational efficiency over several other control methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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9. Anti-Saturation-Based Adaptive Sliding-Mode Control for Active Suspension Systems With Time-Varying Vertical Displacement and Speed Constraints.
- Author
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Chen, Hao, Liu, Yan-Jun, Liu, Lei, Tong, Shaocheng, and Gao, Zhiwei
- Abstract
In this article, an adaptive sliding-mode control scheme is developed for a class of uncertain quarter vehicle active suspension systems with time-varying vertical displacement and speed constraints, in which the input saturation is considered. The integral terminal SMC is adopted to improve convergence accuracy and avoid singular problems. In addition, neural networks are used to model unknown terms in the system and the backstepping technique is taken into account to design the actual controller. To guarantee that the time-varying state constraints are not violated, the corresponding Barrier Lyapunov functions are constructed. At the same time, a continuous differentiable asymmetric saturation model is developed to improve the stability of the system. Then, the Lyapunov stability theory is used to verify that all signals of the resulting system are semi globally uniformly ultimately bounded, time-varying state constraints are not violated, and error variables can converge to the small neighborhood of 0. Finally, results of the simulation of the designed control strategy are given to further prove the effectiveness. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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10. Adaptive NN Constraint Control for Flexible Manipulator System Described by PDE Model
- Author
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Xu, Fangyuan, primary, Liu, Yan-Jun, additional, and Tang, Li, additional
- Published
- 2020
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11. Adaptive Fuzzy Finite-Time Tracking Control for Nonstrict Full States Constrained Nonlinear System With Coupled Dead-Zone Input.
- Author
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Li, Shu, Ding, Liang, Gao, Haibo, Liu, Yan-Jun, Huang, Lan, and Deng, Zongquan
- Abstract
This article proposes an adaptive finite-time tracking control based on fuzzy-logic systems (FLSs) for an uncertain nonstrict nonlinear multi-input–multi-output (MIMO) full-state-constrained system with the coupled uncertain dead-zone input. By using three kinds of FLSs: the uncertain system, the uncertain dead zone, and the uncertain input transfer inverse matrix are approximated using the system function FLS, dead-zone FLS, and input transfer inverse matrix FLS, respectively. After defining the barrier Lyapunov function, the fuzzy-based adaptive tracking controllers are designed, and the fuzzy weights are updated through the proposed adaptive laws. Then, based on the extended finite-time convergence theorem, with the design parameters chosen properly, the target uncertain nonlinear system is guaranteed to be semiglobal practical finite-time stable (SGPFS); and the full-state constraints are not violated while avoiding the effects of the dead zones. Furthermore, a simulation is presented to verify the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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12. Relative Threshold-Based Event-Triggered Control for Nonlinear Constrained Systems With Application to Aircraft Wing Rock Motion.
- Author
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Liu, Lei, Liu, Yan-Jun, Tong, Shaocheng, and Gao, Zhiwei
- Abstract
This article concentrates on the event-driven controller design problem for a class of nonlinear single input single output parametric systems with full state constraints. A varying threshold for the triggering mechanism is exploited, which makes the communication more flexible. Moreover, from the viewpoint of energy conservation and consumption reduction, the system capability becomes better owing to the contribution of the proposed event-triggered mechanism. In the meantime, the developed control strategy can avoid the Zeno behavior since the lower bound of the sample time is provided. The considered plant is in a lower triangular form, in which the match condition is not satisfied. To ensure that all the states retain in a predefined region, a barrier Lyapunov function (BLF) based adaptive control law is developed. Due to the existence of the parametric uncertainties, an adaptive algorithm is presented as an estimated tool. All the signals appearing in the closed-loop systems are then proven to be bounded. Meanwhile, the output of the system can track a given signal as far as possible. In the end, the effectiveness of the proposed approach is validated by an aircraft wing rock motion system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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13. Adaptive Tracking Control for Active Seat Suspension System with Time-Varying Full State Constraints
- Author
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Chen, Aiqing, primary, Liu, Yan-Jun, additional, Liu, Lei, additional, and Gao, Ying, additional
- Published
- 2019
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14. Adaptive Sliding Mode Control for Uncertain Active Suspension Systems With Prescribed Performance.
- Author
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Liu, Yan-Jun and Chen, Hao
- Subjects
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SLIDING mode control , *MOTOR vehicle springs & suspension , *LYAPUNOV stability , *PROBLEM solving , *STABILITY theory , *CLOSED loop systems - Abstract
In this article, the adaptive sliding mode (ASM) control scheme of half-car active suspension systems with prescribed performance is studied. Because of the affected by model uncertainty, time-varying parameter, pavement roughness excitation, etc., the study of suspension systems can be regarded as the multivariable nonlinear control problem. First of all, the prescribed performance function (PPF) is applied to constrain the displacement and pitch angle of the suspension systems to ensure the transient and steady-state suspension responses. Second, an integral terminal sliding mode control method with strong robustness is put forward, which can make the system converge rapidly in a finite-time when it is far from the equilibrium point, solve the singularity problem in the control process, and reduce the chattering phenomenon in the traditional sliding mode control. Then, the neural networks (NNs) approximation characteristics are used to deal with unknown items in the design of the controller, and the Lyapunov stability theory is employed to analyze the stability of the closed-loop system. In the end, the comparative simulation results demonstrate the feasibility and effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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15. 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
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16. Adaptive Output Feedback Tracking Control for a Class of Nonlinear Time-Varying State Constrained Systems With Fuzzy Dead-Zone Input.
- Author
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Lan, Jie, Liu, Yan-Jun, Liu, Lei, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,FUZZY systems ,PSYCHOLOGICAL feedback ,FUZZY logic ,SMOOTHNESS of functions ,CLOSED loop systems ,FUZZY algorithms - Abstract
This article proposes an adaptive fuzzy controller for a class of uncertain strict-feedback nonmatching nonlinear single-input single-output systems with fuzzy dead zone and full time-varying state constraints. The states considered here are immeasurable and full states of the systems are constrained in a bounded set with time-varying regions. Following the adaptive backstepping design framework, the tangent barrier Lyapunov functions are introduced to the integrated design to address the problems in such systems. Fuzzy logic systems are used to identify the unknown smooth functions and unknown parameters. An input-driven observer is designed to estimate the immeasurable states. To distinguish the conventional deterministic dead zone models, the output of dead zone is uncertainty. The form of indeterminate dead zone as a combination of a liner and a disturbance-like term is extended by the fuzzy algorithms. Even though the output of dead zone is fuzzy and adopting the integrated design, the proposed fuzzy controller can ensure that all the signals in the closed-loop systems are semiglobal uniformly ultimately bounded and guarantee the tracking performance. Finally, simulation results are shown to verify the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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17. 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
18. Adaptive Neural Control Using Tangent Time-Varying BLFs for a Class of Uncertain Stochastic Nonlinear Systems With Full State Constraints.
- Author
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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
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19. Adaptive Vehicle Stability Control of Half-Car Active Suspension Systems With Partial Performance Constraints.
- Author
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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
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20. Fuzzy Observer Constraint Based on Adaptive Control for Uncertain Nonlinear MIMO Systems With Time-Varying State Constraints.
- Author
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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
21. 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
22. Adaptive Finite-Time Control for Half-Vehicle Active Suspension Systems With Uncertain Dynamics.
- Author
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Liu, Yan-Jun, Zhang, Yan-Qi, Liu, Lei, Tong, Shaocheng, and Chen, C. L. Philip
- Abstract
The finite-time control design problem of half-vehicle active suspension systems with uncertain dynamics and external disturbances is investigated in this article. The unknown functions, which caused by uncertain parameters and unknown dynamics, are approximated with help of neural networks. An extended Lyapunov condition of finite-time stability is employed to achieve the control of the vertical and pitch motions more quickly. Then, assisted by the practical finite-time theory, the finite-time controller is proposed. It can ensure that half-vehicle active suspension systems achieve the stability in a finite time and the ride comfort can be enhanced. In addition, the developed adaptive finite-time control approach is performed to half-vehicle active suspension systems. By comparing analysis of simulation results, the validity of the established scheme is demonstrated and the performance of half-vehicle active suspension systems is exhibited. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
23. An Adaptive Neural Network Controller for Active Suspension Systems With Hydraulic Actuator.
- Author
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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
24. Reinforcement Learning Neural Network-Based Adaptive Control for State and Input Time-Delayed Wheeled Mobile Robots.
- Author
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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
25. Actuator Failure Compensation-Based Adaptive Control of Active Suspension Systems With Prescribed Performance.
- Author
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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
26. Barrier Lyapunov Function-Based Adaptive Fuzzy FTC for Switched Systems and Its Applications to Resistance–Inductance–Capacitance Circuit System.
- Author
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Liu, Lei, Liu, Yan-Jun, Li, Dapeng, Tong, Shaocheng, and Wang, Zhanshan
- Abstract
In this article, the adaptive fault-tolerant control (FTC) problem is solved for a switched resistance–inductance–capacitance (RLC) circuit system. Due to the existence of faults which may lead to instability of subsystems, the innovation of this article is that the unstable subsystems are taken into account in the frame of output constraint and unmeasurable states. Obviously, there are not any unstable subsystems in unswitched systems. The unstable subsystems will involve many serious consequences and difficulties. Since the system states are unavailable, a switched state observer is designed. In addition, the fuzzy-logic systems (FLSs) are employed to approximate unknown internal dynamics in the controller design procedure. Then, the barrier Lyapunov function (BLF) is exploited to guarantee that the system output satisfy its constrained interval. Moreover, by using the average dwell-time method, all signals in the resulting systems are proofed to be bounded even when faults occur. Finally, the proposed strategy is carried out on the switched RLC circuit system to show the effectiveness and practicability. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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27. Fuzzy Approximation-Based Adaptive Control of Nonlinear Uncertain State Constrained Systems With Time-Varying Delays.
- Author
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Li, Dapeng, Liu, Lei, Liu, Yan-Jun, Tong, Shaocheng, and Chen, C. L. Philip
- Subjects
TIME-varying systems ,ADAPTIVE fuzzy control ,ADAPTIVE control systems ,TRACKING control systems ,CLOSED loop systems ,ARTIFICIAL neural networks ,FUZZY logic - Abstract
In this paper, a novel adaptive fuzzy tracking control strategy is developed for nonlinear time-varying delayed systems with full state constraints. State constraints and time delays are normally found in various real-life plants, which are two important factors for degrading system performance significantly. In the framework of adaptive control, the effects of state constraints and time-varying delays are removed simultaneously. The integral Barrier Lyapunov functionals (IBLFs) are applied to achieve full-state-constraint satisfactions and remove the need of the transformed error constraints in previous BLFs. The unknown time-varying delays are completely compensated by introducing the separation technique and Lyapunov–Krasovskii functionals (LKFs). The unknown functions existing in systems are approximated by employing fuzzy logic systems (FLSs). With the help of less-adjustable parameters, only one parameter is needed to be adjusted online in each step of control design. The novel strategy can guarantee that a satisfactory tracking performance is achieved and the signals existing in the closed-loop system are bounded. Finally, by presenting simulation results, the efficiency of the proposed approach is revealed. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
28. ADP-Based Online Tracking Control of Partially Uncertain Time-Delayed Nonlinear System and Application to Wheeled Mobile Robots.
- Author
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Li, Shu, Ding, Liang, Gao, Haibo, Liu, Yan-Jun, Huang, Lan, and Deng, Zongquan
- Abstract
In this paper, an adaptive dynamic programming-based online adaptive tracking control algorithm is proposed to solve the tracking problem of the partial uncertain time-delayed nonlinear affine system with uncertain resistance. Using the discrete-time Hamilton–Jacobi–Bellman function, the input time-delay separation lemma, and the Lyapunov–Krasovskii functionals, the partial state and input time delay can be determined. With the approximation of the action and critic, and resistance neural networks, a near-optimal controller and appropriate adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the target system, and the tracking error convergence to a small compact set to zero. A numerical simulation of the wheeled mobile robotic system is presented to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
29. Finite-Time Convergence Adaptive Neural Network Control for Nonlinear Servo Systems.
- Author
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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
30. 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
31. 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
32. 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
33. 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
34. Adaptive Dynamic Surface Control of a Half-Car Active Suspension Systems with Hydraulic Actuator
- Author
-
Zeng, Qiang, primary, Liu, Yan-Jun, additional, Liu, Lei, additional, Chen, Jie, additional, and Gao, Ying, additional
- Published
- 2018
- Full Text
- View/download PDF
35. Adaptive Event-Triggered Control for Nonlinear Systems with Output Constraint
- Author
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Liu, Lei, primary and Liu, Yan-Jun, additional
- Published
- 2018
- Full Text
- View/download PDF
36. Adaptive Neural Network Control For Vehicle Active Suspension System with Unknown Dead-Zones
- Author
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Zhang, Yan-Qi, primary, Liu, Lei, additional, and Liu, Yan-Jun, additional
- Published
- 2018
- Full Text
- View/download PDF
37. Adaptive Neural Network Learning Controller Design for a Class of Nonlinear Systems With Time-Varying State Constraints.
- Author
-
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
38. Neural Network Controller Design for a Class of Nonlinear Delayed Systems With Time-Varying Full-State Constraints.
- Author
-
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
39. Fuzzy-Based Multierror Constraint Control for Switched Nonlinear Systems and Its Applications.
- Author
-
Liu, Lei, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
NONLINEAR systems ,TANKS ,COORDINATE transformations - Abstract
In this paper, a framework of adaptive control for a switched nonlinear system with multiple prescribed performance bounds is established using an improved dwell time technique. Since the prescribed performance bounds for subsystems are different from each other, the different coordinate transformations have to be tackled when the system is transformed, which have not been encountered in some switched systems. We deal with the different coordinate transformations by finding a specific relationship between any two different coordinate transformations. To obtain a much less conservative result, in contrast to the common adaptive law, different adaptive laws are established for both active and inactive time-interval of each subsystem. The proposed controllers and switching signals guarantee that all signals appearing in the closed-loop system are bounded. Furthermore, both transient-state and steady-state performances of the switched system are obtained. Finally, the effectiveness of the developed method is verified by the application to a continuous stirred tank reactor system. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
40. Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems.
- Author
-
Liu, Lei, Liu, Yan-Jun, and Tong, Shaocheng
- Abstract
This paper concentrates upon the problem of finite-time fault-tolerant control for a class of switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss of effectiveness and bias fault in actuator are taken into account. The method developed extends the traditional finite-time convergence from nonswitched lower-triangular nonlinear systems to switched version by designing appropriate controller and adaptive laws. In contrast to the previous results, it is the first time to handle the fault tolerant problem for switched system while the finite-time stability is also necessary. Meanwhile, there exist unknown internal dynamics in the switched system, which are identified by the radial basis function neural networks. It is proved that under the presented control strategy, the system output tracks the reference signal in the sense of finite-time stability. Finally, an illustrative simulation on a resistor-capacitor-inductor circuit is proposed to further demonstrate the effectiveness of the theoretical result. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
41. Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay.
- Author
-
Li, Da-Peng, Liu, Yan-Jun, Tong, Shaocheng, Chen, C. L. Philip, and Li, Dong-Juan
- 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
42. Narrow Linewidth and Temperature Insensitive Blue Phase Liquid Crystal Films.
- Author
-
Xu, Xiao Wan, Liu, Yan Jun, Wang, Fei, and Luo, Dan
- Abstract
Blue phase liquid crystal films are quite useful in reflective displays, flexible photonic devices, and optical sensors. However, the effects of refractive index properties of refilled liquid crystals and environment temperature on reflection properties of blue phase liquid crystal films are far from fully exploration. Herein, we demonstrate blue phase liquid crystal films refilled with different nematic liquid crystals. The effects of birefringence and average refractive index of refilled nematic liquid crystals on the reflection properties such as reflectance, central wavelength, and linewidth of photonic band have been studies. The temperature effect on reflectance and central wavelength has been studied. A narrow linewidth (19.4 nm) and temperature insensitivity blue phase liquid crystal film has been fabricated by refilling liquid crystals of DYX8013 with small birefringence. The narrow linewidth and excellent temperature insensitivity properties of BPLC film can be applied in color reflective display and flexible photonic devices. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
43. Reconfigurable Chiral Metasurface Absorbers Based on Liquid Crystals.
- Author
-
Yin, Shengtao, Xiao, Dong, Liu, Jianxun, Li, Ke, He, Huilin, Jiang, Shouzhen, Luo, Dan, Sun, Xiao Wei, Ji, Wei, and Liu, Yan Jun
- Abstract
We propose a liquid-crystal-based reconfigurable chiral metasurface absorber and numerically investigate its chiro-optical properties. The chiral metasurface absorber consists of a metal–insulator–metal structure with the substrate, which can strongly absorb a circularly polarized wave of one spin state and reflects that of the opposite spin, resulting a strong circular dichroism (CD). A birefringent liquid crystal (LC) is exploited to serve as the insulator layer in the metal–insulator–metal structure. We could then vary the circular state of the incident light by controlling the alignment of the LC molecules, hence inversing the CD. The simulation results show that the CD will change the sign as the LC molecules are realigned from 0° to 90°. The absorption efficiency for the specific circularly polarized wave is larger than 80% and the CD is nearly 70%. The simple and compact design of our proposed chiral metasurface absorber is especially favorable for integration, and such a reconfigurable chiral absorber could find many potential applications in biological detection/sensing, polarimetric imaging, and optical communications. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
44. Adaptive Fuzzy Output Feedback Control for a Class of Nonlinear Systems With Full State Constraints.
- Author
-
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
45. Adaptive Fuzzy Tracking Control Based Barrier Functions of Uncertain Nonlinear MIMO Systems With Full-State Constraints and Applications to Chemical Process.
- Author
-
Li, Dong-Juan, Lu, Shu-Min, Liu, Yan-Jun, and Li, Da-Peng
- Subjects
MIMO systems ,FUZZY systems ,ADAPTIVE control systems - Abstract
An adaptive control approach based on the fuzzy systems for a class of uncertain nonlinear multi-input multi-output (MIMO) systems is presented in this paper. This class of systems is in the nested multiple coupling structure and their states are constrained in the corresponding compact sets. The properties of the system structure are inevitable to bring about a complicated design and a difficult task. The fuzzy logic systems are employed to approximate the unknown functions of systems, and the decoupling backstepping way is proposed to design the stability controller and adaptation laws. Barrier Lyapunov functions (BLFs) are constructed in the backstepping design to guarantee that the constraint bounds are not violated. Based on Lyapunov analysis in barrier form, we can prove the stability of the closed-loop system. Two simulation examples are viewed to verify the feasibility of the approach. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
46. Formation Control With Obstacle Avoidance for a Class of Stochastic Multiagent Systems.
- Author
-
Wen, Guoxing, Chen, C. L. Philip, and Liu, Yan-Jun
- Subjects
MULTIAGENT systems ,STOCHASTIC systems ,WIENER processes ,POTENTIAL field method (Robotics) ,OBSTACLE avoidance (Robotics) ,ROBUST control ,LYAPUNOV stability ,COMPUTER simulation - Abstract
This paper addresses formation control with obstacle avoidance problem for a class of second-order stochastic nonlinear multiagent systems under directed topology. Different with deterministic multiagent systems, stochastic cases are more practical and challenging because the exogenous disturbances depicted by the Wiener process are considered. In order to achieve control objective, both the leader-follower formation approach and the artificial potential field (APF) method are combined together, where the artificial potential is utilized to solve obstacle avoidance problem. For obtaining good system robustness to the undesired side effects of the artificial potential, $H_\infty$ analysis is implemented. Based on the Lyapunov stability theory, it is proven that control objective can be achieved, of which obstacle avoidance is proven by finding an energy function satisfying that its time derivative is positive. Finally, a numerical simulation is carried out to further demonstrate the effectiveness of the proposed formation schemes. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
47. Adaptive NN Control Using Integral Barrier Lyapunov Functionals for Uncertain Nonlinear Block-Triangular Constraint Systems.
- Author
-
Liu, Yan-Jun, Tong, Shaocheng, Chen, C. L. Philip, and Li, Dong-Juan
- 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. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
48. Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.
- Author
-
Li, Da-Peng, Li, Dong-Juan, Liu, Yan-Jun, Tong, Shaocheng, and Chen, C. L. Philip
- 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. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
49. Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input.
- Author
-
Liu, Yan-Jun, Li, Shu, Tong, Shaocheng, and Chen, C. L. Philip
- Subjects
- *
NONLINEAR systems , *REINFORCEMENT learning - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
50. Adaptive Critic Design for Pure-Feedback Discrete-Time MIMO Systems Preceded by Unknown Backlashlike Hysteresis.
- Author
-
Tang, Li, Liu, Yan-Jun, and Chen, C. L. Philip
- Subjects
- *
MIMO systems , *ARTIFICIAL neural networks - Abstract
This paper concentrates on the adaptive critic design (ACD) issue for a class of uncertain multi-input multioutput (MIMO) nonlinear discrete-time systems preceded by unknown backlashlike hysteresis. The considered systems are in a block-triangular pure-feedback form, in which there exist nonaffine functions and couplings between states and inputs. This makes that the ACD-based optimal control becomes very difficult and complicated. To this end, the mean value theorem is employed to transform the original systems into input–output models. Based on the reinforcement learning algorithm, the optimal control strategy is established with an actor-critic structure. Not only the stability of the systems is ensured but also the performance index is minimized. In contrast to the previous results, the main contributions are: 1) it is the first time to build an ACD framework for such MIMO systems with unknown hysteresis and 2) an adaptive auxiliary signal is developed to compensate the influence of hysteresis. In the end, a numerical study is provided to demonstrate the effectiveness of the present method. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
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