Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network


The design and simulation of the Spiking Neural Network (SNN) areproposed in this paper to control a plant without and with load. The proposedcontroller is performed using Spike Response Model. SNNs are more powerfulthan conventional artificial neural networks since they use fewer nodes to solvethe same problem. The proposed controller is implemented using SNN to workwith different structures as P, PI, PD or PID like to control linear andnonlinear models. This controller is designed in discrete form and has threeinputs (error, integral of error and derivative of error) and has one output. Thetype of controller, number of hidden nodes, and number of synapses are setusing external inputs. Sampling time is set according to the controlled model.Social-Spider Optimization algorithm is applied for learning the weights of theSNN layers. The proposed controller is tested with different linear andnonlinear models and different reference signals. Simulation results proved theefficiency of the suggested controller to reach accurate responses with minimumMean Squared Error, small structure and minimum number of epochs under noload and load conditions.