255 results on '"Tong, Shaocheng"'
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2. Adaptive Fuzzy Output-Feedback Decentralized Control for Fractional-Order Nonlinear Large-Scale Systems.
- Author
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Zhan, Yongliang and Tong, Shaocheng
- Abstract
This article studies the adaptive fuzzy output-feedback decentralized control problem for the fractional-order nonlinear large-scale systems. Since the considered strict-feedback systems contain unknown nonlinear functions and unmeasurable states, the fuzzy-logic systems (FLSs) are used to model unknown fractional-order subsystems, and a fuzzy decentralized state observer is established to obtain the unavailable states. By introducing the dynamic surface control (DSC) design technique into the adaptive backstepping control algorithm and constructing the fractional-order Lyapunov functions, an adaptive fuzzy output-feedback decentralized control scheme is developed. It is proved that the decentralized controlled system is stable and that the tracking and observer errors are able to converge to a neighborhood of zero. A simulation example is given to confirm the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
3. Fixed-Time Adaptive Fuzzy Containment Dynamic Surface Control for Nonlinear Multiagent Systems.
- Author
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Wu, Wei and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,MULTIAGENT systems ,NONLINEAR systems ,FUZZY logic ,NONLINEAR equations ,FUZZY systems - Abstract
This article investigates the fixed-time adaptive fuzzy containment control problem for nonlinear multiagent systems under the directed communication topologies. The controlled systems have the unknown internal dynamics and mismatched disturbances, and fuzzy logic systems are utilized to identify the unknown internal dynamics. The mismatched disturbances and approximate errors are reconstructed via a disturbance observer. Then, by introducing an adding power integral method, a fixed-time adaptive fuzzy containment DSC scheme is developed to deal with the problem of “computation complexity.” The presented containment control method can not only guarantee that the controlled system is semiglobal practical fixed-time stable, but also avoid the “singular problem” in fixed-time backstepping recursive control technology. Finally, an application of marine surface vehicle is provided to verify the effectiveness of the presented fixed-time fuzzy containment control method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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4. Adaptive Fuzzy Output Feedback Control of Switched Uncertain Nonlinear Systems With Constraint Conditions Related to Historical States.
- Author
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Liu, Lei, Li, Zheng, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,UNCERTAIN systems ,STABILITY theory ,LYAPUNOV stability ,LYAPUNOV functions - Abstract
In this article, a fuzzy adaptive output feedback control strategy is designed for a class of uncertain nonlinear switched system with full state constraints under arbitrary switching signal. The states of the system studied in this article are unmeasurable, so a fuzzy observer is designed to estimate the unmeasurable states. At the same time, in order to ensure that the states of the system do not violate the constraints related to the desired output and states, the log-type barrier Lyapunov function method is selected to solve this constraint problem. Finally, through Lyapunov stability theory analysis, it is found that the designed control strategy can ensure that all signals in the closed-loop system are bounded, and the states of the system do not violate their corresponding constraints. In addition, a numerical simulation verifies the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]
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- 2022
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5. Finite-Time Dynamic Event-Triggered Fuzzy Output Fault-Tolerant Control for Interval Type-2 Fuzzy Systems.
- Author
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Li, Xiaomei, Song, Wenting, Li, Yongming, and Tong, Shaocheng
- Subjects
FAULT-tolerant control systems ,FUZZY systems ,TUNNEL diodes ,LYAPUNOV functions ,FAULT-tolerant computing ,UNCERTAIN systems ,ADAPTIVE fuzzy control ,PSYCHOLOGICAL feedback - Abstract
The finite-time dynamic event-triggered fuzzy output feedback fault-tolerant control problem is studied in this article for the interval type-2 (IT2) Takagi–Sugeno fuzzy system with parameter uncertainties and actuator faults. A fuzzy state observer is first developed to solve the immeasurable state problem. Second, by using the sampled estimating states and measured output signals, a dynamic event-triggered mechanism is formulated via integrating sensor-to-observer with observer-to-controller. Third, an observer-based finite-time event-triggered fuzzy fault-tolerant controller is synthesized via the nonparallel distribution compensation design principle. Consequently, the finite-time stable conditions of the addressed IT2 fuzzy system are established by constructing an appropriate Lyapunov function. Furthermore, an output feedback control design algorithm of solving control and observer gains is given in terms of the established sufficient finite-time stable conditions. Finally, a practical example of a nonlinear tunnel diode circuit system is provided to verify the effectiveness of the proposed IT2 fuzzy control scheme. [ABSTRACT FROM AUTHOR]
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- 2022
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6. 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]
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- 2022
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7. Fuzzy Adaptive Finite-Time Consensus Control for High-Order Nonlinear Multiagent Systems Based on Event-Triggered.
- Author
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Zhou, Haodong, Sui, Shuai, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,MULTIAGENT systems ,NONLINEAR systems ,NONLINEAR dynamical systems ,STABILITY theory ,CLOSED loop systems - Abstract
This article studies the fuzzy adaptive finite-time consensus control problem for high-order nonlinear multiagent systems with unknown nonlinear dynamics. In control design,fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and under the frameworks of adaptive backstepping recursive design and finite-time stability theory, an adaptive fuzzy finite-time consensus control method is developed. To save communication resources and reduce the numbers of controller execution times, a dynamic event-triggered mechanism with a relative threshold is established. Subsequently, an event-triggered-based finite-time fuzzy adaptive control scheme is formulated. Furthermore, by constructing novel integral-type Lyapunov functions and adding a power integrator technique, the finite-time stability of the closed-loop system and the convergence of consensus tracking errors are proved. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed adaptive event-triggered consensus control method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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8. An Observer-Based Fuzzy Adaptive Consensus Control Method for Nonlinear Multiagent Systems.
- Author
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Li, Yongming, Li, Kewen, and Tong, Shaocheng
- Subjects
MULTIAGENT systems ,ADAPTIVE fuzzy control ,NONLINEAR systems ,TRACKING algorithms ,DIRECTED graphs ,LYAPUNOV functions ,FUZZY logic ,FUZZY systems - Abstract
This article investigates the problem of fuzzy adaptive consensus tracking control for nonlinear multiagent systems with unknown nonlinear control gain functions. In the control design, fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and a distributed state observer is constructed to estimate the unmeasured states. Under the case of directed graph, by constructing the logarithm Lyapunov functions, an adaptive fuzzy distributed control method is presented, which removes the restrictive assumptions about the unknown control gain functions must be constants in traditional adaptive intelligent output feedback control methods. The developed control scheme cannot only ensure that all signals of the controlled system are semiglobal uniformly ultimately bounded, but also make the outputs of all the followers keep consensus with the output trajectory of the leader. Finally, simulation results are given to illustrate the effectiveness of the developed consensus control scheme and theorem. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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9. Model-Free Containment Control of Underactuated Surface Vessels Under Switching Topologies Based on Guiding Vector Fields and Data-Driven Neural Predictors.
- Author
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Gu, Nan, Wang, Dan, Peng, Zhouhua, Li, Tieshan, and Tong, Shaocheng
- Abstract
This article investigates the model-free containment control of multiple underactuated unmanned surface vessels (USVs) subject to unknown kinetic models. A novel cooperative control architecture is presented for achieving a containment formation under switching topologies. Specifically, a path-guided distributed containment motion generator (CMG) is first proposed for generating reference points according to the underlying switching topologies. Next, guiding-vector-field-based guidance laws are designed such that each USV can track its reference point, enabling smooth transitions during topology switching. Finally, data-driven neural predictors by utilizing real-time and historical data are developed for estimating total uncertainties and unknown input gains, simultaneously. Based on the learned knowledge from neural predictors, adaptive kinetic control laws are designed and no prior information on kinetic model parameters is required. By using the proposed method, the fleet is able to converge to the convex hull spanned by multiple virtual leaders under switching topologies regardless of fully unknown kinetic models. Through stability analyses, it is proven that the closed-loop control system is input-to-state stable and the tracking errors are uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed cooperative control architecture for multiple underactuated USVs with fully unknown kinetic models. [ABSTRACT FROM AUTHOR]
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- 2022
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10. Adaptive Optimized Backstepping Control-Based RL Algorithm for Stochastic Nonlinear Systems With State Constraints and Its Application.
- Author
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Li, Yongming, Fan, Yanli, Li, Kewen, Liu, Wei, and Tong, Shaocheng
- Abstract
This article investigates the adaptive neural-network (NN) tracking optimal control problem for stochastic nonlinear systems, which contain state constraints and uncertain dynamics. First, to avoid the violation of state constraints in achieving optimal control, the novel barrier optimal performance index functions for subsystems are developed. Second, under the framework of the identifier-actor-critic, the virtual and actual optimal controllers are presented based on the backstepping technique, in which the unknown nonlinear dynamics are learned by the NN approximators. Moreover, the quartic barrier Lyapunov functions are constructed instead of square ones to cope with the Hessian term to ensure the stability of the systems with stochastic disturbance. The proposed optimal control strategy can guarantee the boundedness of closed-loop signals, and the output can follow the given reference signal. Meanwhile, the system states are restricted within some preselected compact sets all the while. Finally, both numerical and practical systems are carried out to further illustrate the validity of the proposed optimal control approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Fuzzy Adaptive Optimized Leader-Following Formation Control for Second-Order Stochastic Multiagent Systems.
- Author
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Li, Yongming, Zhang, Jiaxin, and Tong, Shaocheng
- Abstract
In this article, an adaptive optimized formation control problem is studied for the second-order stochastic multiagent systems (MASs) with unknown nonlinear dynamics. Compared with first-order formation control, the second-order MASs consider not only the states but also the states rates, which is certainly more challenging and difficult work. In the control design of this article, the fuzzy logic systems are applied to approximate the nonlinear functions. By employing the actor-critic architecture and Lyapunov stability theory, the proposed optimal formation control strategy ensures that all the error signals are bounded in probability. Finally, the simulation examples verify that the proposed formation control approach achieves desired results. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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12. 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
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13. Event-Triggered Adaptive Neural Control for Fractional-Order Nonlinear Systems Based on Finite-Time Scheme.
- Author
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Li, Yuan-Xin, Wei, Ming, and Tong, Shaocheng
- Abstract
This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteria are introduced with the aim to ensure that the tracking error enters into a small region around the origin in finite time. Finally, the stability of the closed-loop system is ensured via a fractional Lyapunov function theory and two simulation examples were used to prove the validity of the designed control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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14. 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
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15. Prescribed Performance Fuzzy Adaptive Output Feedback Control for Nonlinear MIMO Systems in a Finite Time.
- Author
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Sui, Shuai, Xu, Hao, Tong, Shaocheng, and Chen, C. L. Philip
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,MIMO systems ,PSYCHOLOGICAL feedback ,CLOSED loop systems ,ADAPTIVE control systems ,FUZZY logic - Abstract
This article studies the fuzzy adaptive output feedback control design problem for nonstrict feedback multi-input–multi-output nonlinear systems with full-states prescribed performance in finite time. Fuzzy logic systems are introduced to solve the problem of unknown nonlinear dynamics. And on this basis, a fuzzy-based state observer is designed to observe the unmeasurable state. Further, by combining the adaptive back-stepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which not only solves the computational complexity explosion problem inherent in the back-stepping control algorithm, but also improves the control performance, in contrast to the traditional DSC methods with linear filters. Besides, to further improve the tracking performance under the structure of full-states prescribed performance, a new Lyapunov function is designed considering the transform error constraint. Based on the Lyapunov theory, the stability of the closed-loop system is analyzed to ensure that all signals of the closed-loop system are semiglobal practical finite-time stability. Finally, to elaborate on the feasibility and effectiveness of the proposed control method, a simulation example is provided. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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16. Broad Learning System Approximation-Based Adaptive Optimal Control for Unknown Discrete-Time Nonlinear Systems.
- Author
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Yuan, Liang'En, Li, Tieshan, Tong, Shaocheng, Xiao, Yang, and Shan, Qihe
- Subjects
NONLINEAR systems ,DISCRETE-time systems ,INSTRUCTIONAL systems ,DYNAMIC programming ,SYSTEM dynamics ,ADAPTIVE control systems ,MULTICOLLINEARITY - Abstract
This article investigates optimal control problem for a class of discrete-time (DT) nonlinear systems with unknown dynamics. With the help of a broad learning system (BLS), a novel online adaptive dynamic programming (ADP) controller is presented. First, to approximate the unknown system dynamics, an approximator based on BLS is presented. The connection weights are calculated by the data of the system by using the ridge regression algorithm. Then, two BLSs are adopted to approximate the optimal cost function and optimal control law, respectively. The connection weights of these two BLSs are updated using the given weights tuning law at each sampling instant. The proposed optimal controller is proved to ensure that all the system states and estimation errors are uniform ultimate bounded. Finally, simulation examples are carried out to further demonstrate the effectiveness of the proposed BLS-based approximator and optimal controller. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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17. 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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18. Adaptive Fuzzy Decentralized Dynamic Surface Control for Fractional-Order Nonlinear Large-Scale Systems.
- Author
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Zhan, Yongliang, Sui, Shuai, and Tong, Shaocheng
- Subjects
NONLINEAR systems ,ADAPTIVE control systems ,ADAPTIVE fuzzy control ,NONLINEAR functions ,LYAPUNOV functions - Abstract
The aim of this article is to study a fuzzy-based decentralized adaptive control strategy for the nonstrict-feedback fractional-order nonlinear large-scale systems with unknown control directions. In each step of the recursive processes, the fuzzy logic systems are employed to identify unknown nonlinear functions. To handle the difficulties caused by unknown control directions, a Nussbaum function technique is adopted. Furthermore, by introducing the dynamic surface control technique into the adaptive backstepping recursive design algorithm, a fuzzy-based decentralized adaptive control strategy is formulated. Both the stability of the controlled system and the convergence of the tracking errors are proved by constructing the fractional-order Lyapunov functions. Finally, the validity and effectiveness of the designed decentralized control scheme are confirmed via two simulation examples. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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19. Event-Triggered Finite-Time Contrtol of IT2 T-S Fuzzy Interconnected Nonlinear Systems with Time-Delays
- Author
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Song, Wenting, primary and Tong, Shaocheng, additional
- Published
- 2021
- Full Text
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20. 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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21. Observer-Based Adaptive Optimized Control for Stochastic Nonlinear Systems With Input and State Constraints.
- Author
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Li, Yongming, Zhang, Jiaxin, Liu, Wei, and Tong, Shaocheng
- Subjects
NONLINEAR systems ,STOCHASTIC systems ,ADAPTIVE control systems ,REINFORCEMENT learning ,CLOSED loop systems ,NONLINEAR functions - Abstract
In this work, an adaptive neural network (NN) optimized output-feedback control problem is studied for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. A nonlinear state observer is designed to estimate the unmeasured states, and the NNs are used to approximate the unknown nonlinear functions. Under the framework of the backstepping technique, the virtual and actual optimal controllers are developed by employing the actor–critic architecture. Meanwhile, the tan-type Barrier optimal performance index functions are developed to prevent the nonlinear systems from the state constraints, and all the states are confined within the preselected compact sets all the time. It is worth mentioning that the proposed optimized control is clearly simple since the reinforcement learning (RL) algorithm is derived based on the negative gradient of a simple positive function. Furthermore, the proposed optimal control strategy ensures that all the signals in the closed-loop system are bounded. Finally, a practical simulation example is carried out to further illustrate the effectiveness of the proposed optimal control method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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22. 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
- Full Text
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23. Finite-Time Adaptive Fuzzy Prescribed Performance Control for High-Order Stochastic Nonlinear Systems.
- Author
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Sui, Shuai, Chen, C. L. Philip, and Tong, Shaocheng
- Subjects
NONLINEAR systems ,STOCHASTIC systems ,FUZZY logic ,ADAPTIVE fuzzy control ,FUZZY systems ,PSYCHOLOGICAL feedback - Abstract
In this article, the high-order nonlinear system is commonly studied in an underactuated weakly coupled mechanical system, the control design is difficult from the tractional control design for nonlinear systems. Thus, we study the finite-time fuzzy adaptive error constraint control problem for stochastic high-order nonlinear nonstrict feedback systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, a new error transfer variable is used to achieve the prescribed performance. Based on adding a power integrator technique and adaptive backstepping recursive control, a novel adaptive fuzzy finite-time prescribed performance control scheme is developed. By utilizing stochastically finite-time stable theory, the proposed control method can guarantee that the high-order system is semi-global finite-time stable in probability. Finally, both numerical and practical simulations are provided to verify the feasibility and effectiveness of the developed control method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
24. Neural Network Adaptive Output-Feedback Optimal Control for Active Suspension Systems.
- Author
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Li, Yongming, Wang, Tiechao, Liu, Wei, and Tong, Shaocheng
- Subjects
MOTOR vehicle springs & suspension ,ELECTRIC suspension ,REINFORCEMENT learning ,ARCHITECTURAL design ,ARTIFICIAL neural networks ,VERTICAL jump ,PSYCHOLOGICAL feedback - Abstract
The adaptive neural network (NN) output-feedback optimal control issue has been investigated for a quarter-car active electric suspension systems, where the suspension stiffness is unknown and partial state variables are unavailable for measurement. NNs are utilized to identify unknown nonlinearities, and an NN state observer is devised to estimate the unmeasurable states. For each backstepping step, via reinforcement learning (RL), a critic–actor architecture is designed to get the approximation solution of Hamilton–Jacobi–Bellman (HJB) equations and actual and virtual optimization controllers are designed, in which the input saturation constraint and road interference are considered. It is analytically proved that all controlled system signals remain bounded, while the power of the control input signal, as well as the amplitude of the vertical displacement, has been minimized. A comparative simulation is eventually given to elaborate the feasibility of the developed control algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
25. A Bound Estimation Approach for Adaptive Fuzzy Asymptotic Tracking of Uncertain Stochastic Nonlinear Systems.
- Author
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Li, Yuan-Xin and Tong, Shaocheng
- Abstract
The adaptive fuzzy tracking control problems for a class of uncertain stochastic nonlinear systems are investigated in this article using the backstepping control approach. Different from the existing research, the crucial but highly restrictive hypothesis on the prior knowledge of unknown virtual control coefficients (UVCCs) is removed from this article. An asymptotic tracking control scheme is proposed by applying smooth functions and a bounded estimation method. By delicately constructing a specific composite Lyapunov function for the controlled system and several useful inequalities, the stability and asymptotic tracking performance with unknown nonlinear function and unknown UVCCs can be guaranteed almost surely. Finally, the method is illustrated with simulation examples. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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26. Adaptive Fuzzy Control for Fractional-Order Nonlinear System with Unknown Dead Zone
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Zhan, Yongliang, primary and Tong, Shaocheng, additional
- Published
- 2020
- Full Text
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27. 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
- Full Text
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28. Observer-based Adaptive Fuzzy Control for Uncertain Nonlinear time-delay systems
- Author
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Zhao, Jipeng, primary, Tong, Shaocheng, additional, and Li, Yongming, additional
- Published
- 2019
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29. Adaptive Event-Triggered Control Design for Nonlinear Systems With Full State Constraints.
- Author
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Jin, Xin, Li, Yuan-Xin, and Tong, Shaocheng
- Subjects
NONLINEAR systems ,ADAPTIVE control systems ,UNCERTAIN systems ,SMOOTHNESS of functions ,CLOSED loop systems ,ADAPTIVE fuzzy control - Abstract
This article is concerned with the adaptive event-triggered control (ETC) problem for uncertain nonlinear systems with full state constraints. By combining the asymmetric barrier Lyapunov functions with the backstepping technique, an adaptive ETC method is designed for the system under consideration. In addition, by introducing some well-defined smooth functions and the bounded estimation approach, the effects caused by the unknown virtual control coefficients and unknown nonlinear functions are counteracted. The asymptotic stability of the closed-loop system is ensured without violating the state constraints. Finally, the effectiveness of the control method is evaluated through simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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30. Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints.
- Author
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Li, Yongming, Liu, Yanjun, and Tong, Shaocheng
- Subjects
ADAPTIVE control systems ,PSYCHOLOGICAL feedback ,NONLINEAR systems ,NONLINEAR dynamical systems ,SYSTEM dynamics ,COST functions ,LYAPUNOV functions - Abstract
This article proposes an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurable and constrained within some predefined compact sets. NNs are used to approximate the unknown internal dynamics, and an adaptive NN state observer is developed to estimate the immeasurable states. By constructing a barrier type of optimal cost functions for subsystems and employing an observer and the actor-critic architecture, the virtual and actual optimal controllers are developed under the framework of backstepping technique. In addition to ensuring the boundedness of all closed-loop signals, the proposed strategy can also guarantee that system states are confined within some preselected compact sets all the time. This is achieved by means of barrier Lyapunov functions which have been successfully applied to various kinds of nonlinear systems such as strict-feedback and pure-feedback dynamics. Besides, our developed optimal controller requires less conditions on system dynamics than some existing approaches concerning optimal control. The effectiveness of the proposed optimal control approach is eventually validated by numerical as well as practical examples. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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31. PDE Based Adaptive Control of Flexible Riser System With Input Backlash and State Constraints.
- Author
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Tang, Li, Zhang, Xin-Yu, Liu, Yan-Jun, and Tong, Shaocheng
- Subjects
RISER pipe ,ADAPTIVE control systems ,PARTIAL differential equations ,LYAPUNOV functions ,LYAPUNOV stability ,STABILITY theory - Abstract
In this paper, a class of flexible riser systems modeled by partial differential equations (PDEs) with the backlash is considered. The backlash is formulated as the addition of a linear input and a interference-like term, then an new auxiliary item is introduced to compensate for the impact of this backlash. In addition, the constraint problem for the position and the velocity is also taken into consideration. To solve this constrain problem, the logarithmic barrier Lyapunov function is employed. For the flexible riser system, two kinds of adaptive controllers are proposed under the following two cases. One controller is designed when only the parameter of backlash is unknown. On the basis of this result, the other controller is presented when some system parameters cannot be measured through actual measurement. Then, combing the theory of Lyapunov stability, the two controllers can guarantee the boundedness of all signals in the closed-loop flexible riser system. Further, both the position and the velocity satisfy their corresponding constraint condition. Finally, the simulation example verifies that the proposed control method is effective. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
32. Fuzzy Adaptive Tracking Control for State Constraint Switched Stochastic Nonlinear Systems With Unstable Inverse Dynamics.
- Author
-
Wu, Wei, Li, Yongming, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,SPACE robotics ,STOCHASTIC systems ,FUZZY logic ,FUZZY systems - Abstract
In this article, a novel fuzzy adaptive tracking control scheme is concerned for a class of stochastic state-constrained switched nonlinear systems. The considered stochastic switched nonlinear system contains unknown nonlinearities and unstable inverse dynamics. In the design process, first, fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear dynamics. Second, the stochastic barrier Lyapunov functions (BLFs) are constructed to deal with the state constraint problem. Then, an adaptive fuzzy state-feedback controller is designed by utilizing the Itô lemma and average dwell time (ADT) approach, which can guarantee both the control system and unstable inverse dynamics to be bounded in probability and all the states cannot violate their constrained sets. Two simulation examples are provided to show the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
33. Event-Trigger-Based Finite-Time Fuzzy Adaptive Control for Stochastic Nonlinear System With Unmodeled Dynamics.
- Author
-
Sui, Shuai, Chen, C. L. Philip, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,SYSTEM dynamics ,STOCHASTIC systems ,ALGORITHMS ,PSYCHOLOGICAL feedback - Abstract
This article investigates the problem of finite-time fuzzy adaptive event-triggered control design for stochastic nonlinear nonstrict feedback systems with unmodeled dynamics. The fuzzy logic systems are adopted to identify the unknown nonlinearities and a state observer is designed to estimate the unmeasured states. Using backstepping recursive design and combining it with a varying threshold event-triggered condition, a novel event-triggered-based fuzzy adaptive finite-time control algorithm is developed, where the dynamical signal function is employed to deal with the unmodeled dynamics. A power form of the errors is used to ensure a continuous stabilizer. The semi-global finite-time stability in probability of the closed-loop system is proved based on an It $\hat{\text{o}}$ differential equation and finite-time stability theory. Simulations are provided to verify the effectiveness of the developed control algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
34. Adaptive Output Feedback Tracking Control for a Class of Nonlinear Time-Varying State Constrained Systems With Fuzzy Dead-Zone Input.
- Author
-
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
35. Robust Fuzzy Adaptive Finite-Time Control for High-Order Nonlinear Systems With Unmodeled Dynamics.
- Author
-
Tong, Shaocheng, Li, Kewen, and Li, Yongming
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,SYSTEM dynamics ,FUZZY logic ,ADAPTIVE control systems ,NONLINEAR dynamical systems - Abstract
This article studies the problem of the robust fuzzy adaptive finite-time control design for a class of single-input single-output high-order nonlinear systems. The considered plants contain unknown nonlinear functions, unmodeled dynamics, and dynamical disturbances. In this control design, fuzzy logic systems are utilized to approximate unknown nonlinear functions, and dynamical signal functions are introduced to solve unmodeled dynamics and dynamical disturbances. Under the framework of an adaptive backstepping control and adding a power integrator control design technique, a robust fuzzy adaptive finite-time control scheme is developed, which can not only guarantee the controlled system to be semiglobal practical finite-time stable, but also have the robustness to unmodeled dynamics and dynamical disturbances. Both numerical and practical simulation examples are provided to check the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
36. Observer-Based Fuzzy Adaptive Inverse Optimal Output Feedback Control for Uncertain Nonlinear Systems.
- Author
-
Li, Yongming, Min, Xiao, and Tong, Shaocheng
- Subjects
ADAPTIVE fuzzy control ,NONLINEAR systems ,UNCERTAIN systems ,NONLINEAR dynamical systems ,CLOSED loop systems ,FUZZY logic - Abstract
In this article, an observer-based fuzzy adaptive inverse optimal output feedback control problem is studied for a class of nonlinear systems in strict-feedback form. The considered nonlinear systems contain unknown nonlinear dynamics and their states are not measured directly. Fuzzy logic systems are applied to identify the unknown nonlinear dynamics and an auxiliary nonlinear system is constructed. Based on this auxiliary system, a fuzzy state observer is first designed to estimate the immeasurable states. By using the inverse optimal principle and adaptive backstepping design theory, an observer-based fuzzy adaptive inverse optimal output feedback control scheme is then developed. The proposed inverse optimal control scheme need not assume that the states are measurable. It also guarantees that the closed-loop system is semiglobally uniformly ultimately bounded, and achieves the optimal control objective as well. Finally, two simulation examples are provided to check the validity of the presented control method. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
37. Roust Adaptive Tracking Control for Switched Reluctance Motor with Sensor Fault
- Author
-
Wu, Wei, primary and Tong, Shaocheng, additional
- Published
- 2019
- Full Text
- View/download PDF
38. Finite-Time Optimal Control for a Class of Lower-Triangular Nonlinear Systems
- Author
-
Yang, Tingting, primary, Li, Yongming, additional, Tong, Shaocheng, additional, and Zhu, Jiuzhou, additional
- Published
- 2019
- Full Text
- View/download PDF
39. Adaptive Neural Inverse Optimal Control for a Class of Strict Feedback Stochastic Nonlinear Systems
- Author
-
Cao, Feng, primary, Yang, Tingting, additional, Li, Yongming, additional, and Tong, Shaocheng, additional
- Published
- 2019
- Full Text
- View/download PDF
40. Adaptive Fuzzy Inverse Optimal Control for A Class of Two Order Nonlinear Systems
- Author
-
Yang, Tingting, primary, Cao, Feng, additional, Li, Yongming, additional, and Tong, Shaocheng, additional
- Published
- 2018
- Full Text
- View/download PDF
41. A Fuzzy Adaptive Control Strategy for Active Suspension Systems with Unknown Dynamics
- Author
-
Sun, Hao, primary, Li, Yongming, additional, and Tong, Shaocheng, additional
- Published
- 2018
- Full Text
- View/download PDF
42. Adaptive Finite-Time Neural Network Control of Nonlinear Systems With Multiple Objective Constraints and Application to Electromechanical System.
- Author
-
Liu, Lei, Zhao, Wei, Liu, Yan-Jun, Tong, Shaocheng, and Wang, Yue-Ying
- Subjects
NONLINEAR systems ,ADAPTIVE control systems ,LYAPUNOV functions ,ARTIFICIAL neural networks ,DYNAMICAL systems ,PSYCHOLOGICAL feedback - Abstract
This article investigates an adaptive finite-time neural control for a class of strict feedback nonlinear systems with multiple objective constraints. In order to solve the main challenges brought by the state constraints and the emergence of finite-time stability, a new barrier Lyapunov function is proposed for the first time, not only can it solve multiobjective constraints effectively but also ensure that all states are always within the constraint intervals. Second, by combining the command filter method and backstepping control, the adaptive controller is designed. What is more, the proposed controller has the ability to avoid the “singularity” problem. The compensation mechanism is introduced to neutralize the error appearing in the filtering process. Furthermore, the neural network is used to approximate the unknown function in the design process. It is shown that the proposed finite-time neural adaptive control scheme achieves a good tracking effect. And each objective function does not violate the constraint bound. Finally, a simulation example of electromechanical dynamic system is given to prove the effectiveness of the proposed finite-time control strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
43. Adaptive Neural Control Using Tangent Time-Varying BLFs for a Class of Uncertain Stochastic Nonlinear Systems With Full State Constraints.
- Author
-
Gao, Tingting, Liu, Yan-Jun, Li, Dapeng, Tong, Shaocheng, and Li, Tieshan
- Abstract
In this paper, an adaptive neural network (NN) control scheme is developed for a class of stochastic nonlinear systems with time-varying full state constraints. In the controller design, RBF NNs are employed to approximate the unknown terms, and the backtracking technique is introduced to overcome the restriction of matching conditions. At the same time, tangent type time-varying barrier Lyapunov functions (tan-TVBLFs) are constructed to ensure the full state constraints are never violated, where tan-TVBLFs are beneficial to integrate constraint analysis into a common method. Furthermore, the Lyapunov stability theory is used to prove that all closed-loop signals are semiglobal uniformly ultimately bounded in probability and error signals remain in the compact set do not violate the time-varying constraints. A simulation example will be used to exhibit the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
44. Fuzzy Adaptive Fault-Tolerant Control of Fractional-Order Nonlinear Systems.
- Author
-
Li, Yuan-Xin, Wang, Quan-Yu, and Tong, Shaocheng
- Subjects
NONLINEAR systems ,ADAPTIVE control systems ,ADAPTIVE fuzzy control ,STABILITY criterion ,LYAPUNOV stability ,SMOOTHNESS of functions ,FAULT-tolerant computing - Abstract
This paper studies the adaptive control problem for a class of uncertain fractional nonlinear systems with actuator faults, where the total number of failures is allowed to be infinite. A compensating term in a smooth function form of a conventional control law is introduced to compensate for the actuator faults. After the introduction of fractional-order adaptation laws, an adaptive controller is designed by using a modified backstepping technique. Fractional Lyapunov stability criterion is adopted to prove the convergence of the developed proposed controller even in the presence of actuator faults. Finally, a simulation example of fractional-order Chua–Hartley’s system is given to verify the effectiveness of the proposed fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
45. Fuzzy Observer Constraint Based on Adaptive Control for Uncertain Nonlinear MIMO Systems With Time-Varying State Constraints.
- Author
-
Liu, Yan-Jun, Gong, Mingzhe, Liu, Lei, Tong, Shaocheng, and Chen, C. L. Philip
- Abstract
This article presents an adaptive output feedback approach of nonlinear multi-input–multi-output (MIMO) systems with time-varying state constraints and unmeasured states. An adaptive approximator is designed to approximate the unknown nonlinear functions existing in the state-constrained systems with immeasurable states. To deal with the tracking problem of such systems, a state observer with time-varying barrier Lyapunov functions (BLFs) is introduced in the controller design procedure. The backstepping design with time-varying BLFs is utilized to guarantee that all system states remain within the time-varying-constrained interval. The constant constraint is only the special case of the time-varying constraint which is more general in the real systems. The proposed control approach guarantees that all signals in the closed-loop systems are bounded and the tracking errors converge to a bounded compact set, and time-varying full-state constraints are never violated. A simulation example is given to confirm the feasibility of the presented control approach in this article. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
46. Adaptive Finite-Time Control for Half-Vehicle Active Suspension Systems With Uncertain Dynamics.
- Author
-
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
47. Neural-Network-Based Adaptive DSC Design for Switched Fractional-Order Nonlinear Systems.
- Author
-
Sui, Shuai, Chen, C. L. Philip, and Tong, Shaocheng
- Subjects
NONLINEAR systems ,LYAPUNOV stability ,STABILITY criterion ,FRACTIONAL powers ,STABILITY theory ,PSYCHOLOGICAL feedback ,ADAPTIVE fuzzy control - Abstract
Due to the particularity of the fractional-order derivative definition, the fractional-order control design is more complicated and difficult than the integer-order control design, and it has more practical significance. Therefore, in this article, a novel adaptive switching dynamic surface control (DSC) strategy is first presented for fractional-order nonlinear systems in the nonstrict feedback form with unknown dead zones and arbitrary switchings. In order to avoid the problem of computational complexity and to continuously obtain fractional derivatives for virtual control, the fractional-order DSC technique is applied. The virtual control law, dead-zone input, and the fractional-order adaptive laws are designed based on the fractional-order Lyapunov stability criterion. By combining the universal approximation of neural networks (NNs) and the compensation technique of unknown dead-zones, and stability theory of common Lyapunov function, an adaptive switching DSC controller is developed to ensure the stability of switched fractional-order systems in the presence of unknown dead-zone and arbitrary switchings. Finally, the validity and superiority of the proposed control method are tested by applying chaos suppression of fractional power systems and a numerical example. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
48. Observer-Based Adaptive Neural Output Feedback Constraint Controller Design for Switched Systems Under Average Dwell Time.
- Author
-
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
49. Finite-Time Adaptive Fuzzy Decentralized Control for Nonstrict-Feedback Nonlinear Systems With Output-Constraint.
- Author
-
Li, Kewen, Tong, Shaocheng, and Li, Yongming
- Subjects
- *
ADAPTIVE fuzzy control , *NONLINEAR systems , *LARGE scale systems , *PSYCHOLOGICAL feedback , *LYAPUNOV stability , *FUZZY logic , *STABILITY theory - Abstract
This paper addresses the finite time adaptive fuzzy decentralized control problem for interconnected large scale nonlinear systems in nonstrict feedback forms with output-constraint. The fuzzy logic systems are used to approximate the unknown nonlinear functions and a state observer is constructed to estimate the immeasurable states. In order to deal with the output constraint problem, the barrier Lyapunov function is introduced. By combining backstepping recursion design with a command filter, a finite time fuzzy adaptive decentralized control method is presented. The stability analysis can be obtained based on the finite time Lyapunov stability theory, which demonstrates that the closed-loop system are semi-global practical finite-time stability, the system outputs can track the given reference signals and keep in the given constraint bounds in a finite time. Finally, two simulation examples are provided to elaborate the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
50. An Adaptive Neural Network Controller for Active Suspension Systems With Hydraulic Actuator.
- Author
-
Liu, Yan-Jun, Zeng, Qiang, Liu, Lei, and Tong, Shaocheng
- Subjects
MOTOR vehicle springs & suspension ,SERVOMECHANISMS ,ACTUATORS ,ATTITUDE (Psychology) ,ADAPTIVE control systems ,AUTOMOBILE dynamics - Abstract
In this paper, an adaptive neural network (NN) controller is proposed for a class of nonlinear active suspension systems (ASSs) with hydraulic actuator. To eliminate the problem of “explosion of complexity” inherently in the traditional backstepping design for the hydraulic actuator, a dynamic surface control technique is developed to stabilize the attitude of the vehicle by introducing a first-order filter. Meanwhile, the presented scheme improves the ride comfort even when the uncertain parameter exists. Due to the existence of uncertain terms, the NNs are used to approximate unknown functions in the ASSs. Finally, a simulation for a servo system with hydraulic actuator is shown to verify the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
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