Optimized Controller Design by Real-Time Model Identification
Hyung-Soo Hwang1, Joon-Ho Cho2
1Dyung-Soo Hwang, Department of Electronics Convergence Engineering, Wonkwang University, Iksan City, Jeonbuk, Republic of korea, East Asian.
2Joon-Ho Cho, Department of Electronics Convergence Engineering, Wonkwang University, Iksan City, Jeonbuk, Republic of korea, East Asian.
Manuscript received on 10 June 2019 | Revised Manuscript received on 17 June 2019 | Manuscript Published on 22 June 2019 | PP: 767-771 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H11280688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Most of the processes with various dynamic characteristics can be reduced to the SOPTD model by using the model reduction method. In this paper, we use the control structure with the Smith predictor to compensate the delay time of the SOPTD model to minimize the ITAE The control algorithm is used. We also propose an optimally adaptive PID controller design algorithm that estimates the coefficients of the SOPTD model in the Smith-Predictor control structure and changes the PID controller parameter values. The method of obtaining the reduced model is improved by using numerical calculation and genetic algorithm. As a result, the steady – state response of the higher – order model and the reduced model is perfectly matched for the unit feedback input. In the optimization adaptive PID controller design, the control parameter value is obtained so that the performance index ITAE is minimized in the Smith Predictor control structure using the reduced model, and the RLSE is used to obtain satisfactory control performance when the external influence is large We propose an adaptive PID controller algorithm that estimates the coefficients of the SOPTD model in real time and then changes the PID controller parameter values from the estimated coefficients by applying the previous method. Simulation results show that the proposed adaptive control scheme has good adaptability to disturbance and process variations.
Keywords: Model Reduction, Adaptive Control, Smith Predictor, PID Control.
Scope of the Article: Waveform Optimization for Wireless Power Transfer