Controller parameter optimization for complex industrial system with uncertainties




Chen, Heping
Bowels, Seth
Zhang, Biao
Fuhlbrigge, Thomas

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Proportional–integral–derivative control system has been widely used in industrial applications. For complex systems, tuning controller parameters to satisfy the process requirements is very challenging. Different methods have been proposed to solve the problem. However these methods suffer several problems, such as dealing with system complexity, minimizing tuning effort and balancing different performance indices including rise time, settling time, steady-state error and overshoot. In this paper, we develop an automatic controller parameter optimization method based on Gaussian process regression Bayesian optimization algorithm. A non-parametric model is constructed using Gaussian process regression. By combining Gaussian process regression with Bayesian optimization algorithm, potential candidate can be predicted and applied to guide the optimization process. Both experiments and simulation were performed to demonstrate the effectiveness of the proposed method.



proportional integral derivative control, controller parameter optimization, Gaussian process regression, Bayesian optimization, Ingram School of Engineering


Chen, H., Bowels, S., Zhang, B., & Fuhlbrigge, T. (2019). Controller parameter optimization for complex industrial system with uncertainties. Measurement and Control, 52(7-8), pp. 888-895.


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