Observer-Based Adaptive Fuzzy Finite-Time Control Design With Prescribed Performance for Switched Pure-Feedback Nonlinear Systems

Zhou, Peng; Zhang, Liang; Zhang, Shuo; Alkhateeb, Abdulhameed F.

doi:10.1109/access.2020.3036927

Cited by 31 publications

(25 citation statements)

References 48 publications

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“…The resulting robust controller will be in a dynamical/static output-feedback form. Some recent works on the deterministic framework [19][20][21] offer some potential directions to be explored and adapted to our setting. On the other hand, we are interested by the generalization of our results to a more general setting.…”

Section: Discussionmentioning

confidence: 99%

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Robust Stability of Time-Varying Markov Jump Linear Systems with Respect to a Class of Structured, Stochastic, Nonlinear Parametric Uncertainties

Drăgan

Aberkane

2021

Axioms

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This note is devoted to a robust stability analysis, as well as to the problem of the robust stabilization of a class of continuous-time Markovian jump linear systems subject to block-diagonal stochastic parameter perturbations. The considered parametric uncertainties are of multiplicative white noise type with unknown intensity. In order to effectively address the multi-perturbations case, we use scaling techniques. These techniques allow us to obtain an estimation of the lower bound of the stability radius. A first characterization of a lower bound of the stability radius is obtained in terms of the unique bounded and positive semidefinite solutions of adequately defined parameterized backward Lyapunov differential equations. A second characterization is given in terms of the existence of positive solutions of adequately defined parameterized backward Lyapunov differential inequalities. This second result is then exploited in order to solve a robust control synthesis problem.

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Section: Discussionmentioning

confidence: 99%

“…@t ě t 0 ě 0, i P N , x 0 P X t 0 and xpsq " xps; t 0 , x 0 q is the solution of (1) or, equivalently, of its version (21). Employing (21b) and (25), we rewrite the first integral from the right hand side of (31) as…”

Section: A Lower Bound Of the Stability Radiusmentioning

confidence: 99%

Robust Stability of Time-Varying Markov Jump Linear Systems with Respect to a Class of Structured, Stochastic, Nonlinear Parametric Uncertainties

Drăgan

Aberkane

2021

Axioms

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“…It was known that the neural networks (NNs) and the fuzzy‐logic systems (FLSs) have capable of can approximate unknown smooth nonlinear functions 8‐13 . With the help of these tools and the adaptive control, using NNs or FLSs to approximate unknown nonlinear terms of systems, the backstepping control design is developed to the uncertain nonlinear systems with various nonlinear features and removes these assumptions, such as dead‐zone, time‐delay, input saturation, backlash, hysteresis, see References 14‐32 and the references therein. For instance, by designing the Lyapunov–Krasovskii function to compensate the delay terms, an adaptive fuzzy backstepping control method was developed to the single‐input and single‐output (SISO) uncertain nonlinear system with time‐delay 19 .…”

Section: Introductionmentioning

confidence: 99%

“…With the help of these tools and the adaptive control, using NNs or FLSs to approximate unknown nonlinear terms of systems, the backstepping control design is developed to the uncertain nonlinear systems with various nonlinear features and removes these assumptions, such as dead‐zone, time‐delay, input saturation, backlash, hysteresis, see References 14‐32 and the references therein. For instance, by designing the Lyapunov–Krasovskii function to compensate the delay terms, an adaptive fuzzy backstepping control method was developed to the single‐input and single‐output (SISO) uncertain nonlinear system with time‐delay 19 . For handling the control problem of the uncertain nonlinear systems with unknown dead‐zone, an adaptive fuzzy control approach with the dead zone inverse method is addressed in Reference 20.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, by designing the Lyapunov-Krasovskii function to compensate the delay terms, an adaptive fuzzy backstepping control method was developed to the single-input and single-output (SISO) uncertain nonlinear system with time-delay. 19 For handling the control problem of the uncertain nonlinear systems with unknown dead-zone, an adaptive fuzzy control approach with the dead zone inverse method is addressed in Reference 20. Recently, these results were extended to multi-input and multi-output (MIMO) nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

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Small‐gain technique‐based adaptive fuzzy command filtered control for uncertain nonlinear systems with unmodeled dynamics and disturbances

Xuning

NiuBen

et al. 2021

Adaptive Control & Signal

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Summary This article studies the adaptive tracking control problem for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances. First, a fuzzy state observer is established to estimate unmeasurable states. To overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, the command filter with a compensation mechanism is applied to the controller design procedure. Meanwhile, with the help of the fuzzy logic systems and the backstepping technique, an adaptive fuzzy control scheme is proposed, which guarantees that all signals in the closed‐loop systems are bounded, and the tracking error can converge to a small region around the origin. Furthermore, the stability of the systems is proven to be input‐to‐state practically stable based on the small‐gain theorem. Finally, a simulation example verifies the effectiveness of the proposed control approach.

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Adaptive neural tracking control for high angle of attack maneuver with average dwell time

Sun

Yan

2021

Adaptive Control & Signal

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This article attempts to study the high angle of attack maneuver from the perspective of switched system control. In view of the complex aerodynamic characteristics, an improved longitudinal attitude motion model is presented, which is a switched stochastic nonstrict feedback nonlinear system with distributed delays. The significant design difficulty is the completely unknown diffusion and drift terms and distributed delays with all state variables. Based on a technical lemma and neural networks, an improved smooth state feedback control law for nonstrict feedback systems is proposed without any growth assumptions. To eliminate the influence of distributed delays, an improved Lyapunov-Krasovskii function is constructed, which skillfully removes the constraint of the upper bound of the delay change rate. Then, by combining the average dwell-time scheme and stochastic backstepping technique, an adaptive neural network tracking control law is designed, which extends a newly proposed switched system stability condition to the stochastic switched system. Theoretical analysis and flight control simulation experiments are provided to illustrate the effectiveness of the proposed control method.

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Observer-Based Adaptive Fuzzy Finite-Time Control Design With Prescribed Performance for Switched Pure-Feedback Nonlinear Systems

Cited by 31 publications

References 48 publications

Robust Stability of Time-Varying Markov Jump Linear Systems with Respect to a Class of Structured, Stochastic, Nonlinear Parametric Uncertainties

Robust Stability of Time-Varying Markov Jump Linear Systems with Respect to a Class of Structured, Stochastic, Nonlinear Parametric Uncertainties

Small‐gain technique‐based adaptive fuzzy command filtered control for uncertain nonlinear systems with unmodeled dynamics and disturbances

Adaptive neural tracking control for high angle of attack maneuver with average dwell time

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