Hybrid Controller with the Combination of FLC and Neural Network-Based IMC for Nonlinear Processes

This work presents a hybrid controller based on the combination of fuzzy logic control (FLC) mechanism and internal model-based control (IMC). Neural network-based inverse and forward models are developed for IMC. After designing the FLC and IMC independently, they are combined in parallel to produce a single control signal. Mean averaging mechanism is used to combine the prediction of both controllers. Finally, performance of the proposed hybrid controller is studied for a nonlinear numerical plant model (NNPM). Simulation result shows the proposed hybrid controller outperforms both FLC and IMC.

[1]  E. Bristol.  On a new measure of interaction for multivariable process control , 1966 .

[2]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[3]  Saeid Nahavandi,et al.  Prediction interval-based neural network modelling of polystyrene polymerization reactor – A new perspective of data-based modelling , 2014 .

[4]  T. Poggio,et al.  Networks and the best approximation property , 1990, Biological Cybernetics.

[5]  Jie Zhang,et al.  Recurrent neuro-fuzzy networks for nonlinear process modeling , 1999, IEEE Trans. Neural Networks.

[6]  Saeid Nahavandi,et al.  Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation , 2014 .

[7]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[8]  Saeid Nahavandi,et al.  Fuzzy system with tabu search learning for classification of motor imagery data , 2015, Biomed. Signal Process. Control..

[9]  Oscar Castillo,et al.  Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems , 2015, Expert Syst. Appl..

[10]  M. Karrari,et al.  A neuro-fuzzy based sensor and actuator fault estimation scheme for unknown nonlinear systems , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[11]  Saeid Nahavandi,et al.  An optimized mean variance estimation method for uncertainty quantification of wind power forecasts , 2014 .

[12]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[13]  Raied Salman.  Neural networks of adaptive inverse control systems , 2005, Appl. Math. Comput..

[14]  Abbas Khosravi,et al.  A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources , 2015 .

[15]  Saeid Nahavandi,et al.  Prediction interval-based modelling of polymerization reactor: A new modelling strategy for chemical reactors , 2014 .

[16]  Peng-Yung Woo,et al.  An adaptive fuzzy sliding mode controller for robotic manipulators , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[17]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[18]  Abbas Khosravi,et al.  Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .

[19]  Saeid Nahavandi,et al.  Control of Polystyrene Batch Reactor Using Fuzzy Logic Controller , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[20]  Saeid Nahavandi,et al.  Improving the Quality of Prediction Intervals Through Optimal Aggregation , 2015, IEEE Transactions on Industrial Electronics.

[21]  Saeid Nahavandi,et al.  EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems , 2015, Expert Syst. Appl..