The optimal weighting matrices selection of linear quadratic regulator (LQR) using state transformation search (STS) particle swarm optimization (PSO) is affianced in this work. The most challenging aspect in LQR is the selection of state (Q) and control (R) weighting matrices because proper selection of weights determines the efficacy of the controller. The selection of Q and R matrices is mostly done through trial and error approach resulting in non optimal response. This motivates the usage of PSO algorithm for an optimal selection of Q and R matrices, which also results in trapping of particles in local optima leading to suboptimal results. As a measure to overcome this drawback, STS-PSO algorithm is formulated. The efficacy of STS-PSO tuned LQR is compared with PSO tuned LQR by applying to the servo control of inverted pendulum. The computational performance shows that the performance of STS-PSO tuned LQR is better than the classical PSO.
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