Adaptive robust PID sliding control of a liquid level system based on multi-objective genetic algorithm optimization

• Autorzy: Mahmoodabadi M. J. , Taherkhorsandi M. , Talebipour M.
• Języki publikacji: EN

Abstrakty

EN

Adaptive robust PID sliding mode control optimized by means of multi-objective genetic algorithm is presented in this paper to control a three-tank liquid level system with external disturbances. While PID constitutes a reliable and stable controller, when compared to sliding mode control (SMC); robustness and tracking performance of SMC are higher than those of the PID control. To use the unique features of both controllers, optimal sliding mode control is executed in terms of a supervisory controller to enhance the performance of optimal adaptive PID control and to provide the necessary control inputs. After the design of the control law, control coefficients of all four involved controllers are optimized by using the multi-objective genetic algorithm so as to minimize errors and the input of the controller. Simulations illustrate that the adaptive robust PID sliding controller based on multi-objective genetic algorithm optimization provides a superior response in comparison to the results obtained separately by PID control, sliding mode control, and adaptive PID control, respectively.

Słowa kluczowe

EN

sliding mode control; PID control; adaptive control; genetic algorithm; multi-objective optimization; liquid level system

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2017
• Tom: Vol. 46, no. 3
• Strony: 227--246
• Opis fizyczny: Bibliogr. 69 poz., rys.

Twórcy

autor

Mahmoodabadi M. J.

• Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran

autor

Taherkhorsandi M.

• Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA

autor

Talebipour M.

• Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).