Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 61, Issue 4, Pages 1970-1982Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2013.2266086
Keywords
Multiobjective optimization; nonlinear model-predictive control (NMPC); self-organizing radial basis function neural network (SORBFNN); wastewater treatment process (WWTP)
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Funding
- National Science Foundation of China [61203099, 61225016, 61034008, 61004051]
- Beijing Municipal Natural Science Foundation [4122006]
- Ph.D. Program Foundation from the Ministry of Chinese Education [20121103120020]
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Because of their complex behavior, wastewater treatment processes (WWTPs) are very difficult to control. In this paper, the design and implementation of a nonlinear model-predictive control (NMPC) system are discussed. The proposed NMPC comprises a self-organizing radial basis function neural network (SORBFNN) identifier and a multiobjective optimization method. The SORBFNN with concurrent structure and parameter learning is developed as a model identifier for approximating the online states of dynamic systems. Then, the solution of the multiobjective optimization is obtained by a gradient method which can shorten the solution time of optimal control problems. Moreover, the conditions for the stability analysis of NMPC are presented. Experiments reveal that the proposed control technique gives satisfactory tracking and disturbance rejection performance for WWTPs. Experimental results on a real WWTP show the efficacy of the proposed NMPC for industrial processes in many applications. Index
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