Design of hybrid regrouping PSO–GA based sub-optimal networked control system with random packet losses

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.

Research optimization on logistics distribution center location based on adaptive particle swarm algorithm

“…Some results focus on the structural hybrid of PSO with other algorithm PSO-GA (genetic algorithm) [27] and PSO-ACO (ant colony optimization) [28]. However, increasing computational cost is the main problem of hybrid methods, regardless of promising results.…”

“…As can be seen from Fig.5, six distribution centers (25,18,13,5,12,9) are selected in the 30 customers location with the weight by demand, the 25-th distribution center can supply the demand for six customers (1,2,16,19,23,26), the 18-th distribution center can supply the demand for two customers (28,29), the 13-th distribution center can supply the demand for six customers (6,15,14,18,27,30), the 5-th distribution center can supply the demand for six customers (7,21), the 12-th distribution center can supply the demand for six customers (3,4,8,11,17,24), and the 9-th distribution center can supply the demand for two customers (10,22).…”

Missile attitude control via a hybrid LQG-LTR-LQI control scheme with optimum weight selection

“…In literature, the popular method is to set T Q C C = and varying R to meet design specifications. The other approach includes simultaneous tuning of both { } , Q R as in [23]- [24]. For the present study the following two cases are compared:…”

“…Even with the LQG/LTR design, the set-point tracking performance is not guaranteed. The tracking can be enforced by externally tuning the LQR weights using an optimization algorithm but the tracking cannot be guaranteed under parametric variation in the system matrices unless the controller contains a built-in proportional-integralderivative (PID) type scheme or its variants like a simple PI or at least an integral control [23]- [24]. To overcome the tracking problem in state-feedback control, the LQI scheme is proposed which considers the presence of an integrator in the forward path and acts as an output feedback controller beside the statefeedback controller implemented on either directly measured or observed states [25]- [27].…”

A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems

“…For this reason, the hybrid meta-heuristic methods are currently enjoying an increasing interest in the optimization community. Some of the hybrid optimization algorithms available in the literature are found in [4,6,38]. An algorithm is successful, if it depends on its exploration and exploitation ability.…”