Unified neuroadaptive fault-tolerant control of fractional-order systems with or without state constraints

Cheng, Hong; Huang, Xiucai; Li, Zeqiang

doi:10.1016/j.neucom.2022.12.035

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…In contrast, the proposed approach removes these restrictive assumptions by considering the control gain as an unknown nonlinear function. Contrary to the literature 4,11,19,21–23,27,29,32–34,43,45–49 of adaptive control of fractional order nonlinear systems where the practical issue of input saturation was not treated, in the proposed method, the saturation nonlinearity is considered in the design phase to adhere with control signal limits and avoid any unexpected alteration. Using only two NNs, independent of the nonlinear system's order, the suggested strategy greatly decreases the number of online adaptive learning parameters. The methods outlined in References 30,50–52 require many NN units to approximate the unknown nonlinear functions at every recursive step equal to the order of the system.…”

Section: Introductionmentioning

confidence: 99%

Neural network‐based output‐feedback adaptive control for a class of uncertain strict feedback fractional‐order nonlinear systems subject to input saturation

Rabahi,

Daoudi,

Chemachema

2024

Adaptive Control & Signal

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SummaryThis paper presents neural networks (NNs) adaptive controller for an uncertain fractional‐order nonlinear system in strict‐feedback form, subject to input saturation, unavailable states for measurement, and external disturbances. The fractional‐order adaptive laws are derived based only on the output tracking error thanks to the implementation of the strictly positive real (SPR) property, differently from the existing results in the literature of fractional‐order strict‐feedback systems where all system states are used in the adaptive laws. The proposed design approach addresses the nonaffine nature of the control input due to saturation nonlinearity by using the mean value theorem and follows a nonrecursive design by using a state transformation where the advantage is twofold. First, it eliminates the explosion complexity found in back‐stepping control‐based approaches design, and second, it reduces the approximators units and parameters in controller implementation. Furthermore, an observer is introduced to estimate the unavailable newly defined states, and then an output adaptive feedback control design is ensured. An NN is used to approximate the unknown ideal control law, and an auxiliary control term is appended to deal with saturation effect, unknown disturbance, and approximation errors. The tracking error is proved to converge asymptotically to a bounded set using the Lyapunov theory. Simulation results on three examples show the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Neural network‐based output‐feedback adaptive control for a class of uncertain strict feedback fractional‐order nonlinear systems subject to input saturation

Rabahi,

Daoudi,

Chemachema

2024

Adaptive Control & Signal

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“…To address the constraint issue to guarantee the steadystate and transient behavior of the FOSs, the barrier Lyapunov function (BLF) by employing the relevant error constraints is the prevalent method to indirectly restrict system states, and a number of the constraint control strategies for FOSs has been achieved. The authors (1) Compared with state constrained controller [23,25,27,34] or finite-time controller for nonlinear FOSs [35][36][37][38], an event-triggered adaptive fuzzy finite-time DSC approach for strict-feedback uncertain nonlinear FOSs with actuator saturation and full-state constraints is proposed, in which the fuzzy logic systems are employed to approximate uncertain nonlinear functions in the backstepping process and the dynamic surface method is applied to overcome the inherent computational complexity from the virtual controller. (2) Compared with the results in [39,40], the event-triggered mechanism is designed together with finite-time full-state constrained adaptive controller, and the finite-time stability of the closed-loop systems is proved based on fractional-order Lyapunov criterion, which reduces the consumption of network resources to make the proposed controller more general for application.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Adaptive Fuzzy Control for Strict-Feedback Nonlinear FOSs Subjected to Finite-Time Full-State Constraints

Wang,

Li,

Liang

2024

Fractal Fract

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In this article, an event-triggered adaptive fuzzy finite-time dynamic surface control (DSC) is presented for a class of strict-feedback nonlinear fractional-order systems (FOSs) with full-state constraints. The fuzzy logic systems (FLSs) are employed to approximate uncertain nonlinear functions in the backstepping process, the dynamic surface method is applied to overcome the inherent computational complexity from the virtual controller and its fractional-order derivative, and the barrier Lyapunov function (BLF) is used to handle the full-state constraints. By introducing the finite-time stability criteria from fractional-order Lyapunov method, it is verified that the tracking error converges to a small neighborhood near the zero and the full-state constraints are satisfied within a predetermined finite time. Moreover, reducing the communication burden can be guaranteed without the occurrence of Zeno behavior, and the example is given to demonstrate the effectiveness of the proposed controller.

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Command filter‐based adaptive finite‐time control of fractional‐order nonlinear constrained systems with input saturation

Chen,

Fang

2024

Asian Journal of Control

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In this article, the command filter‐based finite‐time tracking control issue is explored for fractional‐order nonlinear constrained systems (FONCSs) with saturated input. Firstly, a novel fractional‐order Lyapunov stability lemma is put forward, thereby offering a reliable strategy for finite‐time convergence of FONCSs. By employing an improved error compensation system and introducing auxiliary signal, a new finite‐time command filtered backstepping technique is developed. The proposed control strategy guarantees the finite‐time convergence characteristic of filtering signals, in addition to alleviate the impacts produced by saturated input. After that, a modified nonlinear state‐dependent function (NSDF) is developed to solve the state constraint problem while fulfilling the concavity condition. In comparison to the existing constrained control techniques in FONCSs, this approach not only ensures finite‐time convergence of the tracking error but also gets rid of feasibility demands. Ultimately, two simulation scenarios are supplied to illustrate the feasibility and usefulness of the suggested strategy.

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Unified neuroadaptive fault-tolerant control of fractional-order systems with or without state constraints

Cited by 8 publications

References 39 publications

Neural network‐based output‐feedback adaptive control for a class of uncertain strict feedback fractional‐order nonlinear systems subject to input saturation

Neural network‐based output‐feedback adaptive control for a class of uncertain strict feedback fractional‐order nonlinear systems subject to input saturation

Event-Triggered Adaptive Fuzzy Control for Strict-Feedback Nonlinear FOSs Subjected to Finite-Time Full-State Constraints

Command filter‐based adaptive finite‐time control of fractional‐order nonlinear constrained systems with input saturation

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