Differential evolution techniques for the structure-control design of a five-bar parallel robot

Villarreal-Cervantes, Miguel Gabriel; Cruz-Villar, Carlos A.; Álvarez-Gallegos, Jaime; Portilla-Flores, Edgar Alfredo

doi:10.1080/03052150903325557

Cited by 28 publications

(18 citation statements)

References 21 publications

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“…, k s i , the mass center angle γ i can be in the interval γ i ∈ [0, ±π], thus providing more diversity to the design solution than a rectangular shape, whose mass center angle could only be zero or ±π rad. The mathematical expressions relating the mass, the mass center length, the mass center angle, and the inertia with the structure design variable vector can be found in [18].…”

Section: B Design Variablesmentioning

confidence: 99%

Robust Structure-Control Design Approach for Mechatronic Systems

Villarreal-Cervantes¹,

Cruz-Villar

Álvarez-Gallegos³

et al. 2013

IEEE/ASME Trans. Mechatron.

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In this paper, a robust formulation for the structurecontrol design of mechatronic systems is developed. The proposed robust approach aims at minimization of the sensitivity of the nominal design objectives with respect to uncertain parameters. The robust integrated design problem is established as a nonlinear multiobjective dynamic optimization one, which in order to consider synergetic interactions uses mechanical and control nominal design objectives. A planar parallel robot and its controller are simultaneously designed with the proposed approach when the nominal design objectives are the tracking error and the manipulability measure. The payload at the end-effector is considered as the uncertain parameter. Experimental results show that a robustly designed parallel robot presents lower sensitivity of the nominal design objectives under the effects of changes at the payload than a nonrobustly designed one.Index Terms-Differential evolution algorithm, mechatronic design, robust structure-control design.

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Section: B Design Variablesmentioning

confidence: 99%

Robust Structure-Control Design Approach for Mechatronic Systems

Villarreal-Cervantes¹,

Cruz-Villar

Álvarez-Gallegos³

et al. 2013

IEEE/ASME Trans. Mechatron.

Self Cite

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“…DE is an efficient, effective and robust evolutionary optimization method and has been widely used in many fields (Das and Suganthan 2011, Babu and Rakesh 2008, Villarreal-Cervantes et al 2010, Ramesh et al 2012. However, for a specific problem, it cannot be guaranteed to determine a suitable mutation strategy and control parameters, which play an important role in the optimization performance of DE (Das andSuganthan 2011, Qin et al 2009).…”

Section: L(x(t) U(t) T)dtmentioning

confidence: 99%

Chemical process dynamic optimization based on the differential evolution algorithm with an adaptive scheduling mutation strategy

Zhu¹,

Yan²,

Zhao³

2013

Engineering Optimization

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To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process. The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem.

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“…Kulturel-Konak andKonak (2011) proposed PSO with flexible bay structure to solve the unequal area facility layout problem with two objectives, the material handling cost and the layout area requirement. Villarreal-Cervantes et al (2010) used differential evolution to optimize the structural parameters of a non-redundant parallel robot and the control parameters simultaneously for a five-bar parallel robot. A self-adaptive differential evolution algorithm (Wang et al 2010) with elitist archive and entropy-based diversity preservation strategy was presented to solve multi-objective optimization based on Pareto dominance.…”

Section: Introductionmentioning

confidence: 99%

Decomposition-based multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor

Wang

Chen

Cai

et al. 2013

Engineering Optimization

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This article proposes a decomposition-based multi-objective differential evolution particle swarm optimization (DMDEPSO) algorithm for the design of a tubular permanent magnet linear synchronous motor (TPMLSM) which takes into account multiple conflicting objectives. In the optimization process, the objectives are evaluated by an artificial neural network response surface (ANNRS), which is trained by the samples of the TPMSLM whose performances are calculated by finite element analysis (FEA). DMDEPSO which hybridizes differential evolution (DE) and particle swarm optimization (PSO) together, first decomposes the multi-objective optimization problem into a number of single-objective optimization subproblems, each of which is associated with a Pareto optimal solution, and then optimizes these subproblems simultaneously. PSO updates the position of each particle (solution) according to the best information about itself and its neighbourhood. If any particle stagnates continuously, DE relocates its position by using two different particles randomly selected from the whole swarm. Finally, based on the DMDEPSO, optimization is gradually carried out to maximize the thrust of TPMLSM and minimize the ripple, permanent magnet volume, and winding volume simultaneously. The result shows that the optimized TPMLSM meets or exceeds the performance requirements. In addition, comparisons with chosen algorithms illustrate the effectiveness of DMDEPSO to find the Pareto optimal solutions for the TPMLSM optimization problem.

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Differential evolution techniques for the structure-control design of a five-bar parallel robot

Cited by 28 publications

References 21 publications

Robust Structure-Control Design Approach for Mechatronic Systems

Robust Structure-Control Design Approach for Mechatronic Systems

Chemical process dynamic optimization based on the differential evolution algorithm with an adaptive scheduling mutation strategy

Decomposition-based multi-objective differential evolution particle swarm optimization for the design of a tubular permanent magnet linear synchronous motor

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