Adaptive Tuning of Online Feedback Optimization for Process Control Applications
For process control practitioners, this work simplifies tuning of Online Feedback Optimization controllers by reducing operator-tunable parameters to scalar values, but the improvement is incremental over existing manual tuning methods.
The paper proposes an adaptive tuning method for Online Feedback Optimization controllers that adjusts algorithm parameters based on objective sensitivity, improving closed-loop performance over manual tuning in gas lift and continuously-stirred tank reactor processes.
Online Feedback Optimization leverages properties of optimization algorithms to develop controllers for systems with limited model availability, which is often the case in process control. The interplay between the parameters of the chosen optimization algorithm, as well as lack of direct connection to the characteristics of the underlying process make their tuning challenging. We propose a method for adaptive tuning of Online Feedback Optimization controllers based on scaled projected gradient descent by using sensitivity of the desired objective to the parameters of the algorithm. The proposed adaptive tuning method limits the operator-tunable parameters to scalar values that represent how much the control inputs and the objective can change between iterations without requiring either additional information about the controlled system or repeated experiments. Numerical studies on a gas lift and a continuously-stirred tank reactor processes confirm that our adaptive scheme improves closed-loop performance of Online Feedback optimization compared to standard manual tuning methods.