  In this paper, a new approach has been presented to design sub-optimal state
feedback regulators over Networked Control Systems (NCS) with random packet
losses. The optimal regulator gains, producing guaranteed stability are
designed with the nominal discrete time model of a plant using Lyapunov
technique which produces a few set of Bilinear Matrix Inequalities (BMIs). In
order to reduce the computational complexity of the BMIs, a Genetic Algorithm
(GA) based approach coupled with the standard interior point methods for LMIs
has been adopted. A Regrouping Particle Swarm Optimization (RegPSO) based
method is then employed to optimally choose the weighting matrices for the
state feedback regulator design that gets passed through the GA based stability
checking criteria i.e. the BMIs. This hybrid optimization methodology put
forward in this paper not only reduces the computational difficulty of the
feasibility checking condition for optimum stabilizing gain selection but also
minimizes other time domain performance criteria like expected value of the
set-point tracking error with optimum weight selection based LQR design for the
nominal system.
