Discrete-Step, Quasi-Newton Extremum Seeking Control for Multivariable Real-Time Optimization

Extremum seeking control is a well known approach for multivariate real-time optimization of dynamic systems. In classical extremum seeking control schemes, the estimated gradient of the process's steady-state map is continuously integrated towards a local optimum. Gradient estimation can be done by a combination of low- and high-/bandpass filters. Advanced approaches have been developed that use extended Kalman filters, which allow for a joined estimation of the multidimensional gradient. Within this work, a discrete-step real-time optimization scheme is investigated that is derived from the prevalent quasi-Newton method of numerical optimization. Gradient estimation is implemented by the identification of a local linear dynamic model. A significantly faster convergence to the optimum compared to classical extremum seeking is shown for an academic Hammerstein example and for the optimization of the power consumption within a multistage compressor simulation. Copyright (C) 2020 The Authors.