Compound Adaptive Fuzzy Output Feedback Control for Uncertain Fractional-Order Nonlinear Systems with Fuzzy Dead-Zone Input

Shi, Jiangteng; Cao, Jinde; Liu, Heng; Zhang, Xiulan

doi:10.1007/s40815-022-01457-y

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Although the proposed method is effective for solving full state constraints, the problem of "explosion of complexity" still exists. In following work, inspired by the ideas in [36] and [37], we will try to propose a backstepping design method embedded with time-varying command filters to solve the consensus problem of multiagent systems.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Output Feedback Control for Uncertain Nonlinear Systems Subject to Deferred State Constraints

Guan,

Wang,

Liu

2024

IEEE Access

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This paper addresses the tracking control problem for a class of uncertain nonlinear systems with full state deferred constraints. The constraints considered occur after a period of system operation rather than from the beginning. An attempt has been made to use a shifting function to realize the purpose of deferred constraint. Moreover, the difficulty of asymmetric constraint on states comes from the design on barrier function. Due to this, an unified barrier function is proposed to prevent system states from violating the constraint range, indirectly. Meanwhile, it effectively removes the restriction condition used in existing results that the upper and lower bound signs of constraint functions are the same. Consequently, the goals of tracking a reference signal and state constraints are achieved through the control scheme designed. Theoretical analysis and simulations are presented to demonstrate the efficacy of the proposed control strategy.INDEX TERMS Barrier Lyapunov function, deferred constraints, shifting function.

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Section: Discussionmentioning

confidence: 99%

Adaptive Output Feedback Control for Uncertain Nonlinear Systems Subject to Deferred State Constraints

Guan,

Wang,

Liu

2024

IEEE Access

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“…To obtain the Lyapunov stability condition (7), and in the following cases, ( 9) and ( 10) respectively discuss V (z) and V α (z).…”

Section: Preliminariesmentioning

confidence: 99%

“…and electronics [1][2][3]. Consequently, synchronization of UCSs has become a research hotspot, and researchers have provided multiple schemes, such as neural network control [4,5], adaptive fuzzy control [6,7], sampling control [8,9], backstepping control [10][11][12], and sliding mode control [13,14]. Note that some UCSs can be transformed into strict feedback forms, and backstepping control is a common method for dealing with these types of systems.…”

mentioning

confidence: 99%

Finite-time command filter backstepping control of uncertain chaotic systems

Zhu,

Liu,

Shi

2024

Preprint

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This article investigates the synchronization problem of a class of uncertain chaotic systems based on backstepping control technology and a finite-time command filter. Firstly, the virtual signal and its derivative are approximated by a finite-time command filter, which can guarantee that the estimation error converges to a small region within a finite-time. Meanwhile, a new error compensation mechanism is constructed, which effectively reduces the adverse impact of the filter error. Then, the designed controller ensures that the tracking error converges to a small region within a finite time by selecting appropriate parameters. Finally, two simulation examples validate the feasibility of the proposed method in this article.

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“…• Contrary to the literature 4,11,19,[21][22][23]27,29,[32][33][34]43,[45][46][47][48][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.…”

Section: Introductionmentioning

confidence: 98%

“…This is achieved by applying the mean value theorem to the intriguing

\arctan \left(\cdotp \right)

approximation function, effectively resolving the algebraic loop issue commonly faced in the existing literature. In the literature of strict‐feedback fractional order nonlinear systems the control gain was considered only as unity in References 12,14,28–30,44–47, known/unknown constant in References 9 and 13, and known nonlinearity in References 10,11,27,32,33, and 39. 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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Compound Adaptive Fuzzy Output Feedback Control for Uncertain Fractional-Order Nonlinear Systems with Fuzzy Dead-Zone Input

Cited by 6 publications

References 35 publications

Adaptive Output Feedback Control for Uncertain Nonlinear Systems Subject to Deferred State Constraints

Adaptive Output Feedback Control for Uncertain Nonlinear Systems Subject to Deferred State Constraints

Finite-time command filter backstepping control of uncertain chaotic systems

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

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