An intelligent hybrid BFO-PSO algorithm for multi-loop robust controller design in discrete-time networked control systems

In this paper, the effectiveness of hybrid bacteria foraging optimization (BFO) algorithm and particle swarm optimization (PSO) algorithm have been tested for multi-loop robust controller design in discrete-time networked control systems (NCSs). Of particular interest is the case that varying transmission delays and varying transmission intervals in NCSs cause the optimal function to be nonconvex and even the gradient is not easily computed. The new intelligent hybrid BFO-PSO algorithm is employed to search for the optimal controller parameters, and then to minimize the constrained object function subject to robust control specifications by using a randomized technique is developed which does not need any gradient information. For this purpose, we first extend the results in robust controller synthesis via parameter-dependent Lyapunov function, where the study of interplay between the stability and robust performance of NCSs feedback loops are investigated. Next, the hybrid BFO-PSO algorithm is introduced to minimize the robust control function with communication constraints. Finally, the proposed method is applied to some typical numerical examples. Simulation results show that the system oscillations are effectively damped by the proposed approach.