Design of Hybrid Regrouping PSO-GA based Sub-optimal Networked Control System with Random Packet Losses

TL;DR

This paper introduces a hybrid optimization approach combining Regrouping PSO and GA to design sub-optimal state feedback controllers for networked control systems with random packet losses, improving computational efficiency and performance.

Contribution

It presents a novel hybrid optimization method that reduces computational complexity in designing stabilizing controllers for NCS with packet losses, integrating PSO and GA techniques.

Findings

Reduces computational complexity of BMI feasibility checks.

Achieves improved stability and performance in NCS.

Optimizes weight matrices for better control performance.

Abstract

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.