A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

Authors:
Karim Salahshoor;Ehsan Safari;Iraj Ahangari
Affiliations:
Tehran Faculty of Petroleum, Petroleum University of Technology, Tehran, Iran;Tehran Faculty of Petroleum, Petroleum University of Technology, Tehran, Iran;Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology, Ahwaz, Iran
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

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Abstract

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi-Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.