Discrete-Step, Quasi-Newton Extremum Seeking Control for Multivariable Real-Time Optimization. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Lange, A.; King, R.
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1608
• Page End: 1613
• Publication Date: 2020
• Subjects: Extremum seeking control -- Real-time optimization -- Multivariable control system -- Nonlinear control system -- Autoregressive model -- Compressor; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2206
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.