Fuzzy based sliding surface for shape memory alloy wire actuated classical super-articulated control system

Nakshatharan, S. Sunjai; Dhanalakshmi, K.; Ruth, D. Josephine Selvarani

doi:10.1016/j.asoc.2015.03.057

Cited by 16 publications

(13 citation statements)

References 20 publications

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“…A novel intelligent type reduction is presented in Nagaralea and Patre (2014). In Nakshatharan et al (2015), a type-2 fuzzy neural system was learned through its type-1 counterpart through the integration and extension of the membership function of type-1 and then implemented a type-2 fuzzy neural system in a physical programmable gate chip. Two times the learning and not taking the rules of the week are the disadvantages of this paper.…”

Section: Introductionmentioning

confidence: 99%

PMSM speed control based on intelligent sliding mode technique

Tavoosi

2020

COMPEL

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Purpose The purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. Design/methodology/approach A novel recurrent radial basis function network (RBFN) is used to is used to approximate unknown nonlinear functions in permanent magnet synchronous motor (PMSM) dynamics. Then, using the functions obtained from the neural network, it is possible to design a model-based and precise controller for PMSM using the immersive modeling method. Findings Experimental results indicate the appropriate performance of the proposed method. Originality/value This paper presents a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. A novel recurrent RBFN is used to is used to approximate unknown nonlinear functions in PMSM dynamics.

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Section: Introductionmentioning

confidence: 99%

PMSM speed control based on intelligent sliding mode technique

Tavoosi

2020

COMPEL

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“…On this basis, fuzzy logic has also been used to cope with uncertainties in the control of underactuated mechanical systems. Nakshatharan et al (2015) use a fuzzy sliding surface for the stabilization of an underactuated ball and beam system driven by shape memory alloy actuators. In Yue et al (2016), an adaptive fuzzy sliding mode controller is developed for a wheeled inverted pendulum.…”

Section: Introductionmentioning

confidence: 99%

“…In all aforementioned fuzzy schemes, either all error variables (Zhang et al, 2014;Nakshatharan et al, 2015) or all state variables (Park et al, 2008;Hwang et al, 2014;Li et al, 2014;Park et al, 2014;Azimi and Koofigar, 2015;Wu et al, 2016;Yue et al, 2016;Wu et al, 2017) are taken into account in the premises of the fuzzy rules. As a major drawback of this approach, as remarked by Bessa and Barreˆto (2010) and Bessa et al (2012), it can be asserted that the number of fuzzy sets and fuzzy rules becomes incredibly huge for large scale systems with several DOFs, which could compromise its applicability.…”

Section: Introductionmentioning

confidence: 99%

An adaptive fuzzy sliding mode controller for uncertain underactuated mechanical systems

Bessa

Otto

Kreuzer

et al. 2019

Journal of Vibration and Control

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Underactuated mechanical systems are frequently encountered in several industrial and real-world applications such as robotic manipulators with elastic components, aerospace vehicles, marine vessels, and overhead container cranes. The design of accurate controllers for this kind of mechanical system can become very challenging, especially if a high level of uncertainty is involved. In this paper, an adaptive fuzzy inference system is combined with a sliding mode controller in order to enhance the control performance of uncertain underactuated mechanical systems. The proposed scheme can deal with a large class of multiple-input–multiple-output underactuated systems subject to parameter uncertainties, unmodeled dynamics, and external disturbances. The convergence properties of the resulting intelligent controller are proved by means of a Lyapunov-like stability analysis. Experimental results obtained with an overhead container crane demonstrate not only the feasibility of the proposed scheme, but also its improved efficacy for both stabilization and trajectory tracking problems.

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“…Slide mode control (SMC) was utilized to reject the model uncertainties. Fuzzy logic [28] and neural network [25], [29] enhanced SMC were further proposed to avoid the frequent mode switches, which might excite the resonance vibrations of SMA actuators. Adaptive control algorithms, such as output feedback direct adaptive control [30], model reference active control [31], and robust indirect adaptive control [32], etc., were proposed to control SMA actuators.…”

Section: Introductionmentioning

confidence: 99%

Active Modeling and Control for Shape Memory Alloy Actuators

et al. 2019

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In this paper, an active modeling and control scheme is developed for Shape Memory Alloy (SMA) actuators to eliminate the negative influences caused by the uncertainties in its dynamics. First, a nonlinear SMA dynamic model based on Liang model and the empirical models is built and linearized, and all the uncertainties due to time-varying parameters, external disturbances, as well as the linearization, are considered as model error of the linearized model. Secondly, an active modeling based on Kalman filter is constructed to estimate the model error in real time, which intends to improve the model accuracy actively. Finally, an active modeling based control method is proposed to compensate the model error in order to improve control performance of SMA actuators. Experiments are conducted on a one degree-of-freedom (DOF) testbed actuated by a SMA wire. The experimental results of the active model error estimation, and the control performance with and without the active model based compensation are presented and compared to demonstrate the improvements of the proposed scheme. INDEX TERMS Shape memory alloy (SMA), model error, active modeling, active compensation control.

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Fuzzy based sliding surface for shape memory alloy wire actuated classical super-articulated control system

Cited by 16 publications

References 20 publications

PMSM speed control based on intelligent sliding mode technique

PMSM speed control based on intelligent sliding mode technique

An adaptive fuzzy sliding mode controller for uncertain underactuated mechanical systems

Active Modeling and Control for Shape Memory Alloy Actuators

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