An Improved Homogeneous Polynomial Approach for Adaptive Sliding-Mode Control of Markov Jump Systems With Actuator Faults

Du, Chenglong; Li, Fanbiao; Yang, Chunhua

doi:10.1109/tac.2019.2915006

Cited by 59 publications

(33 citation statements)

References 43 publications

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“…In order to enhance the disturbance rejection performance of the closed-loop systems. The sliding mode control (SMC) technique provides a feasible way [34], [35]. Inspired by the above references, we will employ SMC scheme to design controllers for switched/Marokovian Jump nonlinear systems with strict feedback form or nonstrict feedback form.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Fuzzy PI Prescribed Performance Tracking Control for Switched Nonlinear Systems With Dead-Zone Input and External Disturbances

Sun

Hou

2020

IEEE Access

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This paper studies the problem of adaptive fuzzy proportional-integral (PI) tracking control for a class of switched nonlinear systems with dead-zone input, unknown nonlinear functions and external disturbances. By using backstepping method with fuzzy logic systems, a set of adaptive fuzzy PI tracking controllers are proposed, which have a simple structure and a clear physical meaning. The designed controllers don't need priori information of dead-zone input via introducing Nussbaum function scheme. To enhance the tracking performance, the prescribed performance technique is utilized to constrain the tracking error. The stability analysis of the closed-loop switched systems is established via common Lyapunov function, and the tracking error remains within a prescribed bound and all signals of the closed-loop system are bounded. Finally, the effectiveness of the scheme is verified by two examples.

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Section: Discussionmentioning

confidence: 99%

Adaptive Fuzzy PI Prescribed Performance Tracking Control for Switched Nonlinear Systems With Dead-Zone Input and External Disturbances

Sun

Hou

2020

IEEE Access

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“…FDD approaches can be classified into two main categories: data-driven [5], [6] and model-based approaches [7], [8]. Model-based FDD approaches consist in comparing systems measurements with system variables computed from the mathematical model, which is usually calculated using some fundamental understanding of the system under normal operating conditions [7], [9]. The difference between the measurements and the predicted model prediction (so-called residuals), can be applied as a diagnosis metric for decision making [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

et al. 2020

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The operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (ML) technique for fault detection and diagnosis (FDD) of uncertain HVAC systems. The main goal of the developed MSIPCA-ML approach is to enhance the diagnosis performance, improve the indoor environment quality, and minimize the energy consumption in uncertain building systems. The model uncertainty is addressed by considering the interval-valued data representation. The performance of the proposed FDD is investigated using sets of synthetic and emulated data extracted under different operating conditions. The presented results confirm the high-efficiency of the developed technique in monitoring uncertain HVAC systems due to the high diagnosis capabilities of the interval feature-based support vector machines and k-nearest neighbors and their ability to distinguish between the different operating modes of the HVAC system.

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“…For nonlinear MJSs, the adaptive SMC problem is considered in [28]. In [29], the adaptive sliding mode controller design for a class of MJSs with actuator faults has been investigated, where both completely known Markov transition rates and uncertain Markov transition rates have been considered. To the best of author's knowledge, although the SMC design method has been widely applied for many kinds of systems,no one has studied the design of sliding mode controller for OSL nonlinear Markovian jump systems with partially unknown transition rates, not to mention the case when unmatched nonlinearities, unmatched uncertaities and external disturbances are also involved simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of One-Sided Lipschitz Nonlinear Markovian Jump Systems With Partially Unknown Transition Rates

Sun

Ren

et al. 2020

IEEE Access

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This paper investigates the problem of sliding mode control (SMC) for one-sided Lipschitz (OSL) nonlinear Markovian jump systems with partially unknown transition rates. Unmatched normbounded uncertainties of state matrices and output matrices are considered. First, a suitable integral-type sliding surface is proposed and a sufficient condition is given such that the sliding mode dynamics is stochastically stable with an H ∞ performance level γ. Next, a SMC law is synthesized such that reachability of the specified sliding surface can be ensured. Finally, two simulation examples are provided to prove the effectiveness of the proposed approach. INDEX TERMS Markovian jump systems, partially unknown transition rates, sliding mode control, onesided Lipschitz.

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An Improved Homogeneous Polynomial Approach for Adaptive Sliding-Mode Control of Markov Jump Systems With Actuator Faults

Cited by 59 publications

References 43 publications

Adaptive Fuzzy PI Prescribed Performance Tracking Control for Switched Nonlinear Systems With Dead-Zone Input and External Disturbances

Adaptive Fuzzy PI Prescribed Performance Tracking Control for Switched Nonlinear Systems With Dead-Zone Input and External Disturbances

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

Sliding Mode Control of One-Sided Lipschitz Nonlinear Markovian Jump Systems With Partially Unknown Transition Rates

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