Optimal Operation of Discretely Controlled Continuous Systems under Uncertainty

MARIANO DE PAULA; ERNESTO C. MARTINEZ

INDUSTRIAL & ENGINEERING CHEMICAL RESEARCH

AMER CHEMICAL SOC

Lugar: Washington; Año: 2012 vol. 51 p. 13743 - 13764

0888-5885

Discretely controlled continuous systems constitute a special class of continuous-time hybrid dynamical systems where timely switching to alternative control modes is used for dynamic optimization in uncertain environments. Each mode implements a parameterized feedback control law until a stopping condition triggers due to the activation of a constraint related to states, controls or disturbances. For optimal operation under uncertainty, a novel simulation-based algorithm which combines dynamic programming with event-driven execution and Gaussian processes is proposed to learn a switching policy for mode selection. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Probabilistic models of the state transition dynamics following each mode execution are fitted upon data obtained by increasingly biasing operating conditions. Throughput maximization in a hybrid chemical plant is used as a representative case study.