Nonlinear multiobjective model-predictive control scheme for wastewater treatment process |
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| Affiliation: | 1. College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China;2. Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong;1. Dpto. Informática y Automática. Facultad de Ciencias. Plaza de la Merced s/n, 37008 Salamanca, Spain;2. Dpto. Informática y Automática. E.T.S. Ingeniería Industrial de Béjar. Av. Fernando Ballesteros s/n, 37700 Béjar, Salamanca, Spain;1. Department of Civil and Environmental Engineering, Aalto University, Finland;2. Department of Chemical Engineering, Federal University of Campina Grande, Brazil;3. Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Italy;4. Department of Information and Computer Science, Aalto University, Finland;5. Department of Teleinformatics Engineering, Federal University of Ceará, Brazil;6. Helsinki Region Environmental Services Authority, Finland;1. Dipartimento di Ingegneria Meccanica, Chimica e dei Materiali, Universitá degli Studi di Cagliari, Italy;2. Department of Built Environment, Aalto University, Finland;3. Department of Chemical Engineering, Federal University of Campina Grande, Brazil;1. School of Environmental Engineering and Science, Yangzhou University, 196 West Huayang Road, Yangzhou, Jiangsu 225127, China;1. State Key Laboratory of Urban Water Resources and Environment, Harbin Institute of Technology (HIT), Harbin 150090, China;2. Space Control and Inertial Technology Research Center, HIT, Harbin 150080, China;3. Laboratory of Modeling, Information and Systems, University of Picardie Jules Verne, Amiens, France |
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| Abstract: | A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method. |
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| Keywords: | Nonlinear multiobjective model predictive control Multiobjective optimization Self-organizing radial basis function neural network Multi-gradient method |
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