Adaptive fuzzy sliding mode control of uncertain nonlinear systems

Bessa, Wallace Moreira; Barreto, R.

doi:10.1590/s0103-17592010000200002

Cited by 21 publications

(11 citation statements)

References 24 publications

(31 reference statements)

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“…In this case, computational intelligence might be used to overcome the shortcomings of smooth sliding controllers [59]. As demonstrated by Bessa and Barrêto [60], adaptive fuzzy inference systems, for example, can be properly embedded within the boundary layer to compensate for modeling imprecisions, for the purpose of enhancing the overall control efficiency. This procedure has already been successfully applied to the dynamic positioning of underwater vehicles [61,62], vibration suppression in smart structures [63], tracking of unstable periodic orbits in a chaotic pendulum [64], and the control of electrohydraulic servosystems [65].…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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“…The vector of adjustable parameters is automatically updated by the adaptation lawḊ = ϕsΨ(s), where ϕ is a strictly positive constant related to the adaptation rate [2].…”

Section: Stabilizing the Cart-pole Underactuated Systemmentioning

confidence: 99%

Adaptive fuzzy sliding mode control of the cart‐pole underactuated system

Bessa

Kreuzer

2016

Proc Appl Math and Mech

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“…It consists in bringing the status path to the sliding area and to switch by means clustering of a switching logic sliding around the lath up to it is balanced or of the sliding phenomenon. This makes the system insensitive to curly some parametric variations and disturbances (Bessa and Barrêto, 2010;Wang, 1993).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Control Using Adaptive Fuzzy Sliding Mode for Diagnosis of Stator Fault in PMSM

Bechkaoui¹,

Ameur²,

Bouras³

et al. 2016

Period. Polytech. Transp. Eng.

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In nonlinear control systems when we have a non-constant parameters, conventional control laws may be insufficient because they are not robust especially when the requirement on accuracy and other characteristics dynamic systems are strict. We must use control laws insensitive against to parameter variations, disturbance and nonlinearities. For this purpose, several tools are proposed in the literature, which is quoted a hybrid fuzzy logic and variable structure control (Fl_VSC). This per presents an application of the fuzzy logic scheme to control the speed of PMSM by taking account of the presence of interturn short circuit fault. We were interested in the sliding mode control (SMC) of the PMSM using controller's fuzzy logic controller (FLC) and Adaptive fuzzy logic controller (AFLC). The combination of these two theories has given great performance with fast dynamic response without overshoot. As it has a very robust control, insensitive against to parameters variation and external disturbances. Simulation results confirm the choice of hybrid controllers compared with the conventional controllers and grants a robust performance and precise response to the reference model regardless of load disturbance, stator faults and PMSM parameter uncertainties.

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Adaptive fuzzy sliding mode control of uncertain nonlinear systems

Cited by 21 publications

References 24 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Adaptive fuzzy sliding mode control of the cart‐pole underactuated system

Hybrid Control Using Adaptive Fuzzy Sliding Mode for Diagnosis of Stator Fault in PMSM

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