This paper presents a multi-objective optimal PID (Proportional-Integral-Derivative) controller with the derivative filter factor as the fourth design parameter. The complete design of the PID controller should involve tuning four parameters instead of three. However, most of the research papers consider only three parameters. The fourth parameter, the filter factor, is assigned to a default value or selected experimentally. In all cases, the choice of this factor filter will alter the closed-loop response’s characteristics that were assumed before inserting the filter in the control loop. Therefore in this study, we include the filter factor in the decision variable space from the early stage of the control system design. Also, we formulate the design problem as a multi-objective optimization problem in order to show all the trade-offs among the system speed of response, percentage overshoot, sensitivity to external load disturbances, and sensitivity to noises impacting the measurements as the four parameters of the PID control are tuned. The optimal trade-offs solutions are then introduced to the decision-maker who can choose any one of them.
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ASME 2018 Dynamic Systems and Control Conference
September 30–October 3, 2018
Atlanta, Georgia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5189-0
PROCEEDINGS PAPER
Multi-Objective Optimal Design of Four-Parameter PID Controls Available to Purchase
Yousef Sardahi,
Yousef Sardahi
Marshall University, Huntington, WV
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Almuatazbellah Boker
Almuatazbellah Boker
Marshall University, Huntington, WV
Search for other works by this author on:
Yousef Sardahi
Marshall University, Huntington, WV
Almuatazbellah Boker
Marshall University, Huntington, WV
Paper No:
DSCC2018-8935, V001T01A001; 6 pages
Published Online:
November 12, 2018
Citation
Sardahi, Y, & Boker, A. "Multi-Objective Optimal Design of Four-Parameter PID Controls." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T01A001. ASME. https://doi.org/10.1115/DSCC2018-8935
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