Control and Systems Engineering Department, University of Technology, Baghdad, Iraq


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.