Dual-mode predictive control algorithm for constrained Hammerstein systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)1609-1625
Journal / PublicationInternational Journal of Control
Volume81
Issue number10
Publication statusPublished - Oct 2008

Abstract

In the process industry, there exist many systems which can be approximated by a Hammerstein model. Moreover, these systems are usually subjected to input magnitude constraints. In this paper, a multi-channel identification algorithm (MCIA) is proposed, in which the coefficient parameters are identified by least squares estimation (LSE) together with a singular value decomposition (SVD) technique. Compared with traditional single-channel identification algorithms, the present method can enhance the approximation accuracy remarkably, and provide consistent estimates even in the presence of coloured output noises under relatively weak assumptions on the persistent excitation (PE) condition of the inputs. Then, to facilitate the following controller design, this MCIA is converted into a two stage single-channel identification algorithm (TS-SCIA), which preserves most of the advantages of MCIA. With this TS-SCIA as the inner model, a dual-mode non-linear model predictive control (NMPC) algorithm is developed. In detail, over a finite horizon, an optimal input profile found by solving a open-loop optimal control problem drives the non-linear system state into the terminal invariant set; afterwards a linear output-feedback controller steers the state to the origin asymptotically. In contrast to the traditional algorithms, the present method has a maximal stable region, a better steady-state performance and a lower computational complexity. Finally, simulation results on a heat exchanger are presented to show the efficiency of both the identification and the control algorithms.

Research Area(s)

  • Hammerstein systems, Model predictive control, Singular value decomposition