A comparison between optimization algorithms for the tuning of fuzzy based controlled communication for networked controlled systems

The goal of this paper is improvement of the operation of networked control systems using optimized fuzzy logic controlled communication. Network traffic is reduced with optimal kalman filter estimator and fuzzy logic. It should be explained, there is no need to send information on the network continuously and the measurements are processed locally before sending. In this paper, population based optimization algorithms are used to set the parameters of fuzzy system for control the communication logic. The performance of regulated controllers are compared by the results of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Biogeography Based Optimization (BBO), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization with continuous domain (ACOR) algorithms. The proposed method is simulated on two overhead cranes over a network with nonlinear features. Simulation results prove the validation of the proposed solution. While the results of all algorithms are reasonable but PSO algorithm has best result in reducing tracking error and fitness function point of view. Simulation results illustrate the effectiveness of proposed control system in coordination of two cranes over the network in moving heavy loads.