The objective of this research paper is to design a control system to optimize the operating works of the gantry crane system. The dynamic model of the gantry crane system is derived in terms of trolley position and payload oscillation, which is highly nonlinear. The crane system should have the capability to transfer the material to destination end with desired speed along with reducing the load oscillation, obtain expected trolley position and preserving the safety. Proposed controlling method is based on the proportional-integral-derivative (PID) controller with a series differential compensator to control the swinging of the payload and the system trolley movement in order to perform the optimum utilization of the gantry crane. Standard sine cosine optimization algorithm is one of the most-recent optimization techniques based on a stochastic algorithm was presented to tune the PID controller with a series differential compensator. Furthermore, the considered optimization algorithm is modified in order to overcome the inherent drawbacks and solve complex benchmark test functions and to find the optimal design's parameters of the proposed controller. The simulation results show that the modified sine cosine optimization algorithm has better global search performance and exhibits good computational robustness based on test functions. Moreover, the results of testing the gantry crane model reveal that the proposed controller with standard and modified algorithms is effective, feasible and robust in achieving the desired requirements.

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.