Comparative Study of Optimised Artificial Intelligence Based First Order Sliding Mode Controllers for Position Control of a DC Motor Actuator

• Autorzy: Nyong-Bassey B. E. , Akinloye B.

Identyfikatory

DOI

10.14313/JAMRIS_3-2016/25

• Języki publikacji: EN

Abstrakty

EN

This paper aims at critically reviewing various sliding mode control measures applied to Permanent Magnet DC Motor actuator for position control. At first, a hybrid sliding mode controller was examined with its advantages and disadvantages. Then, the smooth sliding mode controller in the same manner. The shortcomings of the two methods were overcome by proper switch design and also using tanh-sinh hyperbolic function. The sliding mode controller switches on when either disturbance or noise is detected. Genetic Algorithm Computational tuning technique is employed to optimize the gains of the controllers for optimal response.The performance of the proposed controller architecture, as well as the reviewed controllers, have been compared for performance evaluation with respect to several operating conditions. This includes load torque disturbance injection, noise injection in a feedback loop, motor nonlinearity exhibited by parameters variation, and a step change in reference input demand.

Słowa kluczowe

EN

adaptive fuzzy control; DC motor position control; genetic algorithm; particle swarm optimization (PSO); sliding mode control

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2016
• Tom: Vol. 10, No. 3
• Strony: 58--71
• Opis fizyczny: Bibliogr. 32 poz., rys.

Twórcy

autor

Nyong-Bassey B. E.

• Department of Electrical/ Electronics Engineering, Federal University of Petroleum Resources Effurun, P.M.B 1221 Effurun, Delta State, Nigeria

autor

Akinloye B.

• Department of Electrical/ Electronics Engineering, Federal University of Petroleum Resources Effurun, P.M.B 1221 Effurun, Delta State, Nigeria

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.