한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

한국컴퓨터정보학회 (Korean Society of Computer Information)
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Deep Learning-based PID Control for ETB with Parameter Variation and Nonlinear Torque

  • Kap Rai Lee (Dept. of Smart Mobility, Pyeongtaek University)
  • 투고 : 2024.10.07
  • 심사 : 2024.11.01
  • 발행 : 2024.11.29
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초록

본 연구에서는 파라미터 변이 및 비선형 토크를 갖는 차량 전자식 스로틀 바디 시스템(ETB)의 딥러닝 기반의 파라미터 종속적인 PID 제어기 설계 방법을 나타낸다. 시스템 변화 및 부하용량 변화로 인하여 변이된 시스템의 주요 파라미터 값을 딥러닝을 이용하여 추정한다. 파라미터를 추정하기 위하여 심층 신경망을 설계하고, 시스템의 시간응답 특성(상승시간, 오버슈트, 정착시간)을 특성값으로 하여 신경망을 훈련한다. 훈련된 신경망으로부터 시간응답 특성값 이용하여 파라미터는 추정된다. 또한 추정된 파라미터에 동조되어 자동 조정되는 파라미터 종속 PID 제어기를 설계한다. PID 제어기의 최적 이득값은 ITAE 판별법에 의하여 찾아지며, 추정된 파라미터의 함수로 나타내어진다. 또한 비선형 토크의 영향을 감쇄하기 위하여 추정된 비선형 토크값을 상쇄시켜 줄 피드 포워드 제어기를 추가적으로 설계한다. 설계된 제어기의 제어 성능을 나타내기 위하여, 시뮬레이션을 통한 파라미터 변화 및 비선형 토크를 갖는 ETB 시스템의 제어 성능 결과를 나타낸다.

In this paper, an approach based on deep learning and parameter dependent control is proposed for electronic throttle body(ETB) control which has variable parameters and nonlinear torques. Firstly we present parameter estimation method for ETB system using deep neural network. To estimate parameters of ETB, we design deep neural networks and train by use time response characteristic such as rise time, overshoot and settling time. Parameters of ETB are estimated through trained neural networks by using time response data. Secondly we design parameter dependent PID controller which is adjusted automatically with the estimated system parameter of ETB. To design optimal parameter dependent gain of PID controller, we use ITAE(Integral of time multiplied by absolute error) criteria. In addition, we design feed-forward controller to reject nonlinear torque. Finally we present simulation results of ETB syatem with parameter variation and nonlinear torque to verify controller design method.

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