Abstract
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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bristol, E.: On a new measure of interaction for multivariable process control. IEEE Trans. Autom. Control 11, 133–134 (1966)
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control, Signals Syst. 2(4), 303–314 (1989)
Girosi, F., Poggio, T.: Networks and the best approximation property. Biol. Cybern. 63(3), 169–176 (1990)
Guo, Y., Woo, P.Y.: An adaptive fuzzy sliding mode controller for robotic manipulators. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 33(2), 149–159 (2003)
Hosen, M.A., Hussain, M.A., Mjalli, F.S., Khosravi, A., Creighton, D., Nahavandi, S.: Performance analysis of three advanced controllers for polymerization batch reactor: an experimental investigation. Chem. Eng. Res. Des. 92(5), 903–916 (2014)
Hosen, M.A., Khosravi, A., Creighton, D., Nahavandi, S.: Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors. J. Taiwan Inst. Chem. Eng. 45(5), 2246–2257 (2014)
Hosen, M.A., Khosravi, A., Nahavandi, S., Creighton, D.: Control of polystyrene batch reactor using fuzzy logic controller. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4516–4521. IEEE (2013)
Hosen, M.A., Khosravi, A., Nahavandi, S., Creighton, D.: Prediction interval-based neural network modelling of polystyrene polymerization reactor: a new perspective of data-based modelling. Chem. Eng. Res. Des. 92(11), 2041–2051 (2014)
Hosen, M.A., Khosravi, A., Nahavandi, S., Creighton, D.: Improving the quality of prediction intervals through optimal aggregation. IEEE Trans. Ind. Electron. 62(7), 4420–4429 (2015)
Khosravi, A., Talebi, H., Karrari, M.: A neuro-fuzzy based sensor and actuator fault estimation scheme for unknown nonlinear systems. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 4, pp. 2335–2340, July 2005
Khosravi, A., Nahavandi, S.: An optimized mean variance estimation method for uncertainty quantification of wind power forecasts. Int. J. Electr. Power Energy Syst. 61, 446–454 (2014)
Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1(1), 4–27 (1990)
Nguyen, T., Khosravi, A., Creighton, D., Nahavandi, S.: Eeg signal classification for bci applications by wavelets and interval type-2 fuzzy logic systems. Expert Syst. Appl. 42(9), 4370–4380 (2015)
Nguyen, T., Khosravi, A., Creighton, D., Nahavandi, S.: Fuzzy system with tabu search learning for classification of motor imagery data. Biomed. Signal Process. Control 20, 61–70 (2015)
Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Comput. 3(2), 246–257 (1991)
Quan, H., Srinivasan, D., Khambadkone, A.M., Khosravi, A.: A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources. Appl. Energy 152, 71–82 (2015)
Quan, H., Srinivasan, D., Khosravi, A.: Uncertainty handling using neural network-based prediction intervals for electrical load forecasting. Energy 73, 916–925 (2014)
Salman, R.: Neural networks of adaptive inverse control systems. Appl. Math. Comput. 163(2), 931–939 (2005)
Sanchez, M.A., Castillo, O., Castro, J.R.: Generalized type-2 fuzzy systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. Expert Syst. Appl. 42(14), 5904–5914 (2015)
Wang, L.X., Mendel, J.M.: Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Netw. 3(5), 807–814 (1992)
Zhang, J., Morris, A.J.: Recurrent neuro-fuzzy networks for nonlinear process modeling. IEEE Trans. Neural Netw. 10(2), 313–326 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Hosen, M.A., Salaken, S.M., Khosravi, A., Nahavandi, S., Creighton, D. (2015). Hybrid Controller with the Combination of FLC and Neural Network-Based IMC for Nonlinear Processes. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-26555-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26554-4
Online ISBN: 978-3-319-26555-1
eBook Packages: Computer ScienceComputer Science (R0)