43 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 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
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
- Full Text
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6. 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
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7. 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
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8. 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
- View/download PDF
9. 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
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10. 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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11. 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
12. 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
13. 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
14. 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
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15. 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
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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
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- View/download PDF
16. 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
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17. 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
- View/download PDF
18. 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
19. Adaptive Decentralized Controller Design for a Class of Switched Interconnected Nonlinear Systems.
- Author
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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
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20. 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
21. 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
22. Fuzzy-Based Multierror Constraint Control for Switched Nonlinear Systems and Its Applications.
- Author
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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
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23. Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems.
- Author
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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
24. Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay.
- Author
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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
25. 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
26. Adaptive Fuzzy Tracking Control Based Barrier Functions of Uncertain Nonlinear MIMO Systems With Full-State Constraints and Applications to Chemical Process.
- Author
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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
27. Adaptive NN Control Using Integral Barrier Lyapunov Functionals for Uncertain Nonlinear Block-Triangular Constraint Systems.
- Author
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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
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28. 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
29. 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
30. 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
31. Neural Network Controller Design for an Uncertain Robot With Time-Varying Output Constraint.
- Author
-
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
32. Adaptive Neural Network-Based Tracking Control for Full-State Constrained Wheeled Mobile Robotic System.
- Author
-
Ding, Liang, Li, Shu, Liu, Yan-Jun, Gao, Haibo, Chen, Chao, and Deng, Zongquan
- Subjects
MOBILE robots ,ARTIFICIAL neural networks ,TRACKING control systems - Abstract
In this paper, an adaptive neural network (NN)-based tracking control algorithm is proposed for the wheeled mobile robotic (WMR) system with full state constraints. It is the first time to design an adaptive NN-based control algorithm for the dynamic WMR system with full state constraints. The constraints come from the limitations of the wheels’ forward speed and steering angular velocity, which depends on the motors’ driving performance. By employing adaptive NNs and a barrier Lyapunov function with error variables, then, the unknown functions in the systems are estimated, and the constraints are not violated. Based on the assumptions and lemmas given in this paper and the references, while the design and the system parameters chose properly, our proposed scheme can guarantee the uniform ultimate boundedness for all signals in the WMR system, and the tracking error converge to a bounded compact set to zero. The numerical experiment of a WMR system is presented to illustrate the good performance of the proposed control algorithm. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
33. Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems.
- Author
-
Liu, Yan-Jun, Lu, Shumin, Li, Dongjuan, and Tong, Shaocheng
- Subjects
- *
ADAPTIVE control systems , *NONLINEAR systems - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
34. Fuzzy Adaptive Control With State Observer for a Class of Nonlinear Discrete-Time Systems With Input Constraint.
- Author
-
Liu, Yan-Jun, Tong, Shaocheng, Li, Dong-Juan, and Gao, Ying
- Subjects
ADAPTIVE fuzzy control ,OBSERVABILITY (Control theory) ,FUZZY logic ,NONLINEAR statistical models ,DISCRETE-time systems ,APPROXIMATION theory - Abstract
In this paper, an adaptive fuzzy controller is constructed for a class of nonlinear discrete-time systems with unknown functions and bounded disturbances. The main characteristics of the systems are that they take into account the effect of discrete-time dead zone and the system states are not required to be measurable. The stability problem of this class of systems is for the first time to be addressed in this paper. Due to the unavailability of the states and the presence of the discrete-time dead zone, the controller design becomes more difficult. To stabilize the uncertain nonlinear discrete-time systems, the fuzzy logic systems are used to approximate the unknown functions, a fuzzy state observer is designed to estimate the immeasurable states, and the effect caused by discrete-time dead zone can be solved via establishing an adaptation auxiliary signal. Based on the Lyapunov approach, it is proved that all the signals of the closed-loop system are the semiglobal uniformly ultimately bounded, and the tracking error is made within a small neighborhood around zero. The feasibility of the developed control scheme is verified via two simulation examples. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
35. Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone.
- Author
-
Liu, Yan-Jun, Gao, Ying, Tong, Shaocheng, and Li, Yongming
- Subjects
OPTIMAL control theory ,APPROXIMATION theory ,DISCRETE-time systems ,ANOXIC zones ,PERFORMANCE evaluation - Abstract
In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame and contain unknown functions and nonsymmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but achieves the optimal control performance as well. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of nonsymmetric dead-zone on the control performance, and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
36. Adaptive Fuzzy Identification and Control for a Class of Nonlinear Pure-Feedback MIMO Systems With Unknown Dead Zones.
- Author
-
Liu, Yan-Jun and Tong, Shaocheng
- Subjects
ADAPTIVE computing systems ,FUZZY systems ,CONTROL theory (Engineering) ,NONLINEAR systems ,FEEDBACK control systems ,MIMO systems - Abstract
The adaptive fuzzy identification and control problems are considered for a class of multi-input multi-output nonlinear systems with unknown functions and unknown dead-zone inputs. The main characteristics of the considered systems are that 1) they are composed of n subsystems and each subsystem is in nested lower triangular form, 2) dead-zone inputs are in nonsymmetric nonlinear form, and 3) dead-zone inputs appear nonlinearly in the systems and their parameters are not required to be known. The controller design for this class of systems is a difficult and complicated task because of the existences of unknown functions, the couplings among the nested subsystems, and the dead-zone inputs. In the controller design, the fuzzy logic systems are employed to approximate the unknown functions and the differential mean value theorem is used to separate dead-zone inputs. To compensate for dead-zone inputs, the compensative terms are designed in the controllers. The stability of the closed-loop system is proved via the Lyapunov stability theorem. A simulation example is provided to validate the feasibility of the approach. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
37. Adaptive NN Tracking Control of Uncertain Nonlinear Discrete-Time Systems With Nonaffine Dead-Zone Input.
- Author
-
Liu, Yan-Jun and Tong, Shaocheng
- Abstract
In the paper, an adaptive tracking control design is studied for a class of nonlinear discrete-time systems with dead-zone input. The considered systems are of the nonaffine pure-feedback form and the dead-zone input appears nonlinearly in the systems. The contributions of the paper are that: 1) it is for the first time to investigate the control problem for this class of discrete-time systems with dead-zone; 2) there are major difficulties for stabilizing such systems and in order to overcome the difficulties, the systems are transformed into an n-step-ahead predictor but nonaffine function is still existent; and 3) an adaptive compensative term is constructed to compensate for the parameters of the dead-zone. The neural networks are used to approximate the unknown functions in the transformed systems. Based on the Lyapunov theory, it is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero. Two simulation examples are provided to verify the effectiveness of the control approach in the paper. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
38. Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints.
- Author
-
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
39. A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input.
- Author
-
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
40. Adaptive Fuzzy Control for a Class of Nonlinear Discrete-Time Systems With Backlash.
- Author
-
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
41. Adaptive NN Controller Design for a Class of Nonlinear MIMO Discrete-Time Systems.
- Author
-
Liu, Yan-Jun, Tang, Li, Tong, Shaocheng, and Chen, C. L. Philip
- Subjects
- *
ADAPTIVE control systems , *ARTIFICIAL neural networks , *MIMO systems , *NONLINEAR systems , *DISCRETE-time systems , *COORDINATE transformations - Abstract
An adaptive neural network tracking control is studied for a class of multiple-input multiple-output (MIMO) nonlinear systems. The studied systems are in discrete-time form and the discretized dead-zone inputs are considered. In addition, the studied MIMO systems are composed of $N$ subsystems, and each subsystem contains unknown functions and external disturbance. Due to the complicated framework of the discrete-time systems, the existence of the dead zone and the noncausal problem in discrete-time, it brings about difficulties for controlling such a class of systems. To overcome the noncausal problem, by defining the coordinate transformations, the studied systems are transformed into a special form, which is suitable for the backstepping design. The radial basis functions NNs are utilized to approximate the unknown functions of the systems. The adaptation laws and the controllers are designed based on the transformed systems. By using the Lyapunov method, it is proved that the closed-loop system is stable in the sense that the semiglobally uniformly ultimately bounded of all the signals and the tracking errors converge to a bounded compact set. The simulation examples and the comparisons with previous approaches are provided to illustrate the effectiveness of the proposed control algorithm. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
42. Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems.
- Author
-
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
43. Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems.
- Author
-
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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