Optimal feedback control design using genetic algorithm in multimachine power system

Robandi, Imam; Nishimori, Kentaro; Nishimura, Ryo; Ishihara, Naganori

doi:10.1016/s0142-0615(00)00062-4

Cited by 60 publications

(28 citation statements)

References 7 publications

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“…The tournament's size, by which the number of chromosomes can be specified, is selected after so many trials to be 4. b. Crossover combines the two chromosomes, or parents, to create a new offspring chromosome, or child, for the next generation. Generally the crossover probability has a value from 0.6 to 1.0 [20]. In this study, after so many trials, a crossover probability of 0.9 is found to be quite satisfactory.…”

Section: The Proposed Optimization Algorithmmentioning

confidence: 50%

Application of genetic algorithm in optimization of unified power flow controller parameters and its location in the power system network

Hassan¹,

Moghavvemi²,

Almurib³

et al. 2013

International Journal of Electrical Power & Energy Systems

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Section: The Proposed Optimization Algorithmmentioning

confidence: 50%

Application of genetic algorithm in optimization of unified power flow controller parameters and its location in the power system network

Hassan¹,

Moghavvemi²,

Almurib³

et al. 2013

International Journal of Electrical Power & Energy Systems

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“…(15) Since the system considered here has two identical torsion modules, the desired characteristic equation of the sixth order system is assumed as…”

Section: Vol 66 (2017)mentioning

confidence: 99%

“…As an alternate approach, several authors have also assessed the efficacy of evolutionary computation techniques like GA and PSO to solve the LQR optimization problem. For instance, Robanti et al [15] investigated the efficacy of GA to solve the weight optimization problem of LQR on a multi-machine power system. Comparing the performance of GA optimized LQR with those of Bryson's method and a trial and error based method, they reported that the weights optimized via GA yield the best response of all three methods.…”

Section: Introductionmentioning

confidence: 99%

A new algebraic LQR weight selection algorithm for tracking control of 2 DoF torsion system

Elumalai

Subramanian

2017

Archives of Electrical Engineering

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This paper proposes a novel linear quadratic regulator (LQR) weight selection algorithm by synthesizing the algebraic Riccati equation (ARE) with the Lagrange multiplier method for command following applications of a 2 degree of freedom (DoF) torsion system. The optimal performance of LQR greatly depends on the elements of weighting matrices Q and R. However, normally these weighting matrices are chosen by a trial and error approach which is not only time consuming but cumbersome. Hence, to address this issue, blending the design criteria in time domain with the ARE, we put forward an algebraic weight selection algorithm, which makes the LQR design both simple and modular. Moreover, to estimate the velocity of a servo angle, a high gain observer (HGO) is designed and integrated with the LQR control scheme. The efficacy of the proposed control scheme is tested on a benchmark 2 DoF torsion system for a trajectory tracking application. Both the steady state and dynamic characteristics of the proposed controller are assessed. The experimental results accentuate that the proposed HGO based LQR scheme can guarantee the system to attain the design requirements with minimal vibrations and tracking errors.

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“…Genetic algorithm (GA), an evolution method based on natural selection and an evolutionary theory, has been employed to optimize parameters of control system that are difficult to solve by conventional optimization techniques [14][15]. The working of the GA is based on the Darwinian's theory of survival of the fittest.…”

Section: Genetic Algorithmmentioning

confidence: 99%

An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system

Vinodh¹,

Jerome²

2014

Archives of Electrical Engineering

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This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.

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Optimal feedback control design using genetic algorithm in multimachine power system

Cited by 60 publications

References 7 publications

Application of genetic algorithm in optimization of unified power flow controller parameters and its location in the power system network

Application of genetic algorithm in optimization of unified power flow controller parameters and its location in the power system network

A new algebraic LQR weight selection algorithm for tracking control of 2 DoF torsion system

An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system

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