Observer-Based Event-Triggered Adaptive Fuzzy Control for Unmeasured Stochastic Nonlinear Systems With Unknown Control Directions

Xia, Jianwei; Lian, Yuxiao; Su, Shun‐Feng; Shen, Hao; Chen, Guoliang

doi:10.1109/tcyb.2021.3069853

Cited by 68 publications

(26 citation statements)

References 41 publications

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“…Recently, event-triggered control (ETC), as a control strategy with aperiodic transmission characteristics, has been widely used in bandwidth-limited network control systems. [24][25][26][27] Under the event-triggering mechanism (ETM), a controller updates its value only at triggering instants that are determined by a predefined triggering condition, which effectively reduces the communication burden of a plant. In References 28-30, the adaptive fuzzy/neural control problem of nonlinear strict-feedback systems was discussed within the ETC architecture.…”

Section: Introductionmentioning

confidence: 99%

“…(1) In the existing ETC methods considering unknown control gains, 26,27,35 the ETM with relative threshold 28 is generally used to save communication resources. The key of this approach is to construct an affine system to eliminate the unknown control coefficient on the controller.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Different from the relative threshold strategy, 26,27,35 an adaptive threshold based on tracking error is used in the designed ETM. Under the proposed ETM, the affine process for treating unknown control directions is omitted, which simplifies the design process of the controller.…”

Section: Introductionmentioning

confidence: 99%

“…However, these works in References 36‐39 have not yet been extended to switched nonlinear systems with event‐triggered communication owing to the following two restrictions. In the existing ETC methods considering unknown control gains, 26,27,35 the ETM with relative threshold 28 is generally used to save communication resources. The key of this approach is to construct an affine system to eliminate the unknown control coefficient on the controller.…”

Section: Introductionmentioning

confidence: 99%

“…Then, a state observer is used to observe the unmeasurable affine states. However, when the states of the original system are considered to be known, the usage of the observer will significantly increase the complexity of the control scheme. Due to the existing of the state observer, the above works in References 26,27,35 cannot be extended to nonlinear systems suffering from the unknown control coefficient and input saturation 36‐39 . To be specific, the input of the system is unknown owing to the unknown saturation nonlinearity, which imposes a huge challenge when using the state observer.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Chen

Niu

Zhang

et al. 2022

Intl J Robust & Nonlinear

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In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Chen

Niu

Zhang

et al. 2022

Intl J Robust & Nonlinear

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Finite‐time adaptive fuzzy control for a class of output constrained nonlinear systems with dead‐zone

Liu

Zhang

2021

Adaptive Control & Signal

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The article investigates the finite-time adaptive fuzzy control for a class of nonlinear systems with output constraint and input dead-zone. First, by skillfully combining the barrier Lyapunov function, backstepping design method, and finite-time control theory, a novel adaptive state-feedback tracking controller is constructed, and the output constraint of the nonlinear system is not violated. Second, the fuzzy logic system is used to approximate unknown function in the nonlinear system. Third, the finite-time command filter is introduced to avoid the problem of "complexity explosion" caused by repeated differentiations of the virtual control signal in conventional backstepping control schemes. Meanwhile, a new saturation function is added in the compensating signal for filter error to improve control accuracy. Finally, based on Lyapunov stability analysis, all the signals of the closed-loop are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood region of the origin in a finite time. A simulation example is presented to demonstrate the effectiveness for the proposed control scheme.

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Adaptive deep neural network optimized control for a class of nonlinear strict‐feedback systems with prescribed performance

Lu,

Wu,

Wang

2024

Adaptive Control & Signal

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SummaryIn this article, an adaptive deep neural network (DNN) optimized control strategy is developed for a class of nonlinear strict‐feedback systems with prescribed performance. First, the DNN is applied to approximate the unknown function, and the weight update law is designed to reduce the mathematical challenge based on the first‐order Taylor's series. Second, the optimized backstepping technique is utilized to construct virtual and actual controllers in the backstepping process to achieve the overall control optimization of the system. Next, a control strategy based on the time‐varying switching function and the quartic barrier Lyapunov function is employed to achieve the prescribed performance. Then, the tracking error can converge to the prescribed accuracy within the prescribed time, and every signal within the system has a bound. Finally, the particle swarm optimization algorithm is utilized to search for the designed parameters and simulation examples to verify the effectiveness of the control strategy.

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Observer-Based Event-Triggered Adaptive Fuzzy Control for Unmeasured Stochastic Nonlinear Systems With Unknown Control Directions

Cited by 68 publications

References 41 publications

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

Finite‐time adaptive fuzzy control for a class of output constrained nonlinear systems with dead‐zone

Adaptive deep neural network optimized control for a class of nonlinear strict‐feedback systems with prescribed performance

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