Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/14441
Title: A New MIMO ANFIS-PSO Based NARMA-L2 Controller for nonlinear dynamic systems
Authors: Al-Dunainawi, Y
Abbod, M
Keywords: Intelligent control;ANFIS;Nonlinear systems;Fuzzy control.
Issue Date: 2017
Citation: Engineering Applications of Artificial Intelligence
Abstract: The proposal of this study is a new nonlinear autoregressive moving average, NARMA-L2 controller, which is based on an adaptive neuro-fuzzy inference system, ANFIS architecture. The new control configuration employs Sugeno-type fuzzy inference system FIS submodels to map input characteristics to the output of a dynamic and nonlinear system. The default hybrid learning algorithm (Backpropagation and Least Square Error) has been carried out as well as particle swarm optimisation (PSO) approach, in order to select the optimal parameters of the ANFIS submodels. Once the system has been modelled efficiently and accurately, the proposed controller is designed by rearranging the generalised FIS submodels. The controller performance is evaluated by simulations conducted on a binary distillation column, which is characterised by a nonlinear and dynamic behaviour. The obtained results show that the PSO-ANFIS based NARMA-L2 achieved more efficient modelling and control performances when compared with other controllers. These controllers include ANN-based NARMA-L2, (PD, PI and PID like) fuzzy-tuned by GA and PSO and traditional PID, which are also implemented to the column for comparison. Stability and robustness of the proposed controller regarding system inputs variance have also been tested by applying asynchronous setpoints of both inputs of the process.
URI: http://bura.brunel.ac.uk/handle/2438/14441
ISSN: 0952-1976
Appears in Collections:Dept of Mechanical Aerospace and Civil Engineering Research Papers

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