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Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics

机译:Metaheuristics based tuning of robust PID controllers for controlling voltage and current on photonics and optics

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摘要

Abstract Optics means a general area of physics that covers a broad range of topics relevant to the study of light. Optics has subfields they are quantum optics, geometrical optics, and physical optics. Photonics refers to a subset of the optics discipline. The simplicity, high efficiency, reliability, transparency, and robust feature of PID controllers were few factors behind the reasons for its acceptance and more popularity for control in processing industries globally. All the conventional techniques for PID controller design and tuning offer early workable values for reaching favorable performances. The manual tuning of the PID controller variables becomes a difficult role that needs comprehensive knowledge and expertise in that field. Certain meta-heuristic techniques are explored to design PID controllers with the main aims of attaining minimum overshoot in lesser settling time and steady state response. For accomplishing enhanced robustness, minimization of disturbance elimination, operating, and physical restraints are treated as multi-optics functions. Therefore, this article presents a modified honey badger optimization (MoHBO) algorithm-based parameter tuning of the PID controller for optics and photonics. The tuning of the PID controller with constraints on the relative delay margin was considered an optimum disturbance rejection issue and summarized the tuning process. The presented MoHBO algorithm is derived by the inclusion of oppositional based learning (OBL) with standard HBO algorithm. In addition, the MoHBO algorithm is tested using the Magnetic Levitation System (MLS) and Phase Locked Loop system (PLL) to assure the enhanced robust nature of the PID controller. A wide ranging comparison study with other metaheuristic optimization algorithms demonstrates the better performance of the MoHBO algorithm over recent algorithms.

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