Multi-objective Pole Placement with Evolutionary Algorithms

Sánchez, Gustavo; Villasana, Minaya; Strefezza, Miguel

doi:10.1007/978-3-540-70928-2_33

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…Thus, different methods of PID controllers tuning have been proposed. Conventional tuning method of PID control proposed in [12] is one of the well-known technique.…”

Section: Issn: 2088-8708 mentioning

confidence: 99%

“…where it is assumed that there is observable system, that is observability matrix [ ] contains a rank that is full. The output feedback techniques have been proved to work well in linear systems theory in the literature [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. For more detailed derivation and designs refer to [2].…”

Section: System Model Of the State Feedback Controlmentioning

confidence: 99%

“…However, the lack of graphical tools will make it difficult to tune the rise time, settling time, and overshoot in the state feedback controller for a desired performance. To tackle this problem in [11][12][13][14], authors employ the linear quadratic regulator LQR to rely on optimal control. However, this method is still requiring manual tuning before a desirable response can be achieved.…”

Section: System Model Of the State Feedback Controlmentioning

confidence: 99%

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Robust pole placement using firefly algorithm

Sababha¹,

Zohdy²,

Kafafy³

2019

IJECE

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In this paper, the new automatic tool that is based on the firefly algorithm whose purpose is optimization of pole location in the control of state feedback has been presented. The aim is satisfying specifications of performance like settling and rise time, steady state as well as overshoot error. Utilization of Firefly algorithm has demonstrated the benefits of controllers based on this kind of time domain over controllers based on the frequency domain like Proportional-Integral Derivative (PID). The presented method is more particular for the multi-input multi-output (MIMO) systems that have substantial state numbers. The simulation results indicated that the proposed method had superior performance in providing solution to the problems that involved stabilization of helicopter under the Rationalized Model of helicopter/ Moreover, it demonstrates the Firefly algorithm effectiveness with regards to, the state observer design and feedback controller and auto-tuning.

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“…Thus, different methods of PID controllers tuning have been proposed. Conventional tuning method of PID control proposed in [12] is one of the well-known technique.…”

Section: Issn: 2088-8708 mentioning

confidence: 99%

Section: System Model Of the State Feedback Controlmentioning

confidence: 99%

Section: System Model Of the State Feedback Controlmentioning

confidence: 99%

See 1 more Smart Citation

Robust pole placement using firefly algorithm

Sababha¹,

Zohdy²,

Kafafy³

2019

IJECE

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“…Regarding the initial population, it can be generated using the algorithm proposed in (Sánchez et al, 2007). After that, SPEA2 is used to drive the design process, taking advantage of its ability to manage an archive of non-dominated solutions.…”

Section: Multi-objective Pole Placement With Evolutionary Algorithms mentioning

confidence: 99%

GENETIC SOLUTIONS TO MIXED H2/H∞ PROBLEMS - Limits of Performance

Sánchez

Strefezza

Villasana

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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One of the most relevant problems for control engineers is the so-called "mixed H 2 /H ∞ ". To solve it, different convexifying strategies became popular in the later 1990s, mainly based on Linear Matrix Inequalities (LMIs). On the other hand, genetic algorithms have also been applied for H 2 /H ∞ synthesis. Indeed, several authors agree that they are able to find good solutions to this important control problem. However, in most of the published papers, only low-order SISO models have been considered. In the present paper a LMI-based algorithm is compared against a genetic algorithm, with respect to three performance indicators: Set Coverage, Maximum Distance and Efficient Set Spacing. Five open-loop MIMO models extracted from COMPl e ib are studied, for which the degree varies between 5 and 10. Based on numerical results, the genetic algorithm is not able to improve LMI solutions for problems with more than 42 variables, restricted to a budget of 20.000 function evaluations.

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“…Wang et al (2006) designed a PI/ PD controller for the non-minimum phase system and used PSO to tune the controller gains. Sanchez et al (2007) formulated a classical observer-based feedback controller as a multiobjective optimization problem and solved it using GA. Mohammadi et al (2011) applied an evolutionary tuning technique for a type-2 fuzzy logic controller. Zargari et al (2012) designed a fuzzy sliding mode controller with Kalman estimator for a small hydro-power plant based on PSO.…”

Section: Introductionmentioning

confidence: 99%

An online optimal linear state feedback controller based on MLS approximations and a novel straightforward PSO algorithm

Mahmoodabadi

Bisheban

2014

Transactions of the Institute of Measurement and Control

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Selection of control parameters is an important issue in the field of control design. This selection depends on the initial condition of the systems. On the other hand, controller parameters should be adjusted under all initial conditions to reach the optimal performance. To overcome this problem, in this paper, an online optimal linear state feedback control is introduced. Firstly, a straightforward particle swarm optimization is used to optimize the state feedback control parameters under some certain initial conditions. Then, in order to adapt the optimal controller to different initial conditions, the moving least squares approximation is used. The proposed technique is applied to an inverted pendulum system. The feasibility and efficiency of the proposed controller are assessed in simulation results.

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Multi-objective Pole Placement with Evolutionary Algorithms

Cited by 13 publications

References 9 publications

Robust pole placement using firefly algorithm

Robust pole placement using firefly algorithm

GENETIC SOLUTIONS TO MIXED H2/H∞ PROBLEMS - Limits of Performance

An online optimal linear state feedback controller based on MLS approximations and a novel straightforward PSO algorithm

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