Robust PID design by chance-constrained optimization

摘要

A method for synthesizing proportional-integral-derivative (PID) controllers for process models with probabilistic parametric uncertainty is presented. The proposed method constitutes a stochastic extension to the well-studied maximization of integral gain optimization (MIGO) approach, i.e., maximization of integral gain under constraints on the H-infinity-norm of relevant closed-loop transfer functions. The underlying chance-constrained optimization problem is solved using a gradient-based algorithm once it has been approximated by a deterministic optimization problem. The approximate solution is then probabilistically verified using randomized algorithms (RAs). The proposed method is demonstrated through several realistic synthesis examples. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.