Multi-Objective Reconfigurable Output Feedback Controller for Mimo System Using Moeas

Subbian, Sutha; Lavanya, S.; Thyagarajan, T.

doi:10.1080/00986445.2011.651183

Cited by 7 publications

(1 citation statement)

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“…NSGA II is a popular multi-objective evolutionary algorithm for two or three objectives. It is used in the paper Ayala and Coelho 36 and Sutha and Thyagarajan 37 as a tool for parameter tuning of the controllers. NSGA III is an improvement algorithm of NSGA II, which introduces the setting of reference points in contrast to NSGA II.…”

Section: Multi-objective Optimization Using Evolutionary Algorithmsmentioning

confidence: 99%

Intelligent high-type control based on evolutionary multi-objective optimization

Zhang,

Liu,

Mao

2023

Measurement and Control

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In this paper, we formulate high-type intelligent control as a multi-objective problem and apply evolutionary algorithms to search for optimal solutions. Specifically, we consider the metrics of the system in both the frequency domain and the time domain. Integrated time and absolute error is used as a performance metric in the time domain, while bandwidth is used as a measure in the frequency domain. Simultaneously, the amplitude margin and phase margin are used as constraints to ensure the stability of the high-type control system. Then, we adopt evolutionary algorithms to solve the formulated multi-objective problem. Unlike most of the existing approaches, we formulate intelligent high type control as a multi-objective optimization problem based on our knowledge about the control system. Furthermore, evolutionary algorithms are adopted to search for optimal solutions to real-world controlling systems. Extensive experiments are conducted to evaluate the effectiveness of our proposed approach. Compared to the Z-N method and the extending symmetrical optimum criterion, our proposed method achieves an improvement in bandwidth of more than 126.6%, while reducing the overshoot by more than 56.8% and the settling time by more than 48.4% for all controlled objects used in the experiments. At the same time, the tracking errors of the ramp and parabolic signals are significantly reduced, which means this method effectively improves the system performance.

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Section: Multi-objective Optimization Using Evolutionary Algorithmsmentioning

confidence: 99%