期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 27, 期 2, 页码 402-415出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2465174
关键词
Dissolved oxygen (DO) concentration; nonlinear model predictive control (NMPC); recurrent radial basis function (SR-RBF) neural networks; self-organizing; wastewater treatment process (WWTP)
类别
资金
- Beijing Nova Program [Z131104000413007]
- China Post-Doctoral Science Foundation through Hong Kong Scholar Program [2014M550017, XJ2013018]
- Beijing Science and Technology Project [Z141100001414005, Z141101004414058]
- National Science Foundation of China [61203099, 61225016, 61533002]
- Ph.D. Program Foundation from Ministry of Chinese Education [20121103120020]
- Beijing Municipal Education Commission Foundation [km201410005001, KZ201410005002]
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
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