Open AccessDesign a Different Structures Controller for Controlled Systems Using a Spiking Neural Network
Author(s) -
Mohammed Y. Hassan,
Ahmed Abduljabbar Mahmood
Publication year - 2019
Publication title -
al-maǧallaẗ al-ʻirāqiyyaẗ li-handasaẗ al-ḥāsibāt wa-al-ittiṣālāt wa-al-sayṭaraẗ wa-al-naẓm
Language(s) - English
Resource type - Journals
eISSN - 2617-3352
pISSN - 1811-9212
DOI - 10.33103/uot.ijccce.19.2.3
Subject(s) - control theory (sociology) , controller (irrigation) , pid controller , computer science , artificial neural network , nonlinear system , set (abstract data type) , sampling (signal processing) , open loop controller , mean squared error , control engineering , control (management) , artificial intelligence , mathematics , temperature control , engineering , statistics , physics , filter (signal processing) , closed loop , quantum mechanics , agronomy , computer vision , biology , programming language
The design and simulation of the Spiking Neural Network (SNN) are proposed in this paper to control a plant without and with load. The proposed controller is performed using Spike Response Model. SNNs are more powerful than conventional artificial neural networks since they use fewer nodes to solve the same problem. The proposed controller is implemented using SNN to work with different structures as P, PI, PD or PID like to control linear and nonlinear models. This controller is designed in discrete form and has three inputs (error, integral of error and derivative of error) and has one output. The type of controller, number of hidden nodes, and number of synapses are set using external inputs. Sampling time is set according to the controlled model. Social-Spider Optimization algorithm is applied for learning the weights of the SNN layers. The proposed controller is tested with different linear and nonlinear models and different reference signals. Simulation results proved the efficiency of the suggested controller to reach accurate responses with minimum Mean Squared Error, small structure and minimum number of epochs under no load and load conditions.