Sliding Mode Neuro-Adaptive Control of Electric Drives

Topalov, Andon V.; Cascella, Giuseppe Leonardo; Giordano, Vincenzo; Cupertino, Francesco; Kaynak, Okyay

doi:10.1109/tie.2006.888930

Cited by 68 publications

(20 citation statements)

References 17 publications

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“…This has demonstrated that more stable learning algorithms need to be adopted. One possible solution could be the use of Variable Structure Systems theory based algorithms that are known for their stability (Topalov, Cascella, Giordano, Cupertino, & Kaynak, 2007).…”

Section: Discussionmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles

Kurnaz

Çetin

Kaynak

2010

Expert Systems with Applications

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187

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Section: Discussionmentioning

confidence: 99%

Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles

Kurnaz

Çetin

Kaynak

2010

Expert Systems with Applications

Self Cite

187

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“…The integer order version of the problem addressed here is studied in Efe (2002), where the crux of the approach is to extract a quantified error on the applied control signal utilizing the available measurements. In Efe (2002) and Topalov et al (2007), the map (·) is a monotonically increasing function of its argument and a common choice for it is a unit function, i.e. σ i = s p,i .…”

Section: Sliding Mode Control Through a Fractional Order Adaptation Smentioning

confidence: 99%

“…Error on the control signal is naturally not a computable quantity, however, such a quantity can be extracted based on the behavioral properties as considered here and inEfe (2002) andTopalov et al (2007) …”

mentioning

confidence: 99%

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

Efe

2008

Soft Comput

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This paper presents an approach to improve the performance of intelligent sliding model control achieved by the use of a fundamental constituent of soft computing, named Adaptive Linear Element (ADALINE). The proposed scheme is based on the fractional calculus. A previously considered tuning scheme is revised according to the rules of fractional order differintegration. After a comparison with the integer order counterpart, it is seen that the control system with the proposed adaptation scheme provides (1) better tracking performance, (2) suppression of undesired drifts in parameter evolution and (3) a very high degree of robustness and insensitivity to disturbances. The claims are justified through some simulations utilizing the dynamic model of a two degrees of freedom (DOF) direct drive robot arm and overall, the contribution of the paper is to introduce the fractional order calculus into a robust and nonlinear control problem with some outperforming features that are absent when the integer order differintegration operators are adopted.

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“…The use of adaptive control algorithms offers an attractive alternative for speed tracking of DC motors [10,11,13,14]. A direct adaptive fuzzy logic controller is exposed in [11]; it is estimated from two levels-one uses a Mamdani fuzzy controller and the other is an inverse model based on a Takagi-Sugeno method.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Speed Control Approach for DC Shunt Motors

et al. 2016

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A B-spline neural networks-based adaptive control technique for angular speed reference trajectory tracking tasks with highly efficient performance for direct current shunt motors is proposed. A methodology for adaptive control and its proper training procedure are introduced. This algorithm sets the control signal without using a detailed mathematical model nor exact values of the parameters of the nonlinear dynamic system. The proposed robust adaptive tracking control scheme only requires measurements of the velocity output signal. Thus, real-time measurements or estimations of acceleration, current and disturbance signals are avoided. Experimental results confirm the efficient and robust performance of the proposed control approach for highly demanding motor operation conditions exposed to variable-speed reference trajectories and completely unknown load torque. Hence, laboratory experimental tests on a direct current shunt motor prove the viability of the proposed adaptive output feedback trajectory tracking control approach.

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Sliding Mode Neuro-Adaptive Control of Electric Drives

Cited by 68 publications

References 17 publications

Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles

Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles

ADALINE based robust control in robotics: a Riemann-Liouville fractional differintegration based learning scheme

An Adaptive Speed Control Approach for DC Shunt Motors

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