Multi-Objective On-Line Optimization Approach for the DC Motor Controller Tuning Using Differential Evolution

Villarreal-Cervantes, Miguel Gabriel; Rodríguez-Molina, Alejandro; García-Mendoza, Consuelo-Varinia; Peñaloza-Mejía, Ollin; Sepulveda-Cervantes, Gabriel

doi:10.1109/access.2017.2757959

Cited by 33 publications

(18 citation statements)

References 27 publications

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“…Many MOEAs have emerged and obtained satisfactory achievement [46]. In recent years, MODE is overwhelmingly applied for several engineering problems [27], [47], which uses DE [48] as the underlying optimization technique. The salient features of MODE are: (a) it has a low computational complexity, (b) it is easy to implement with simple structure, (c) it has fast convergence speed and strong robustness, and (d) it has efficient constraint processing method.…”

Section: B Optimization Strategy For Burden Surface Based On Mode Anmentioning

confidence: 99%

Burden Surface Decision Using MODE With TOPSIS in Blast Furnace Ironmkaing

Zhang

et al. 2020

IEEE Access

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Burden surface distribution plays a key role in achieving an energy-efficient status of blast furnace (BF). However, actual adjustment of burden surface usually depends on the operator's experience when the production status changes. Meanwhile, due to the characteristics of high dimension, strong coupling, and distributed parameters, it is difficult to establish the accurate mechanism model for BF ironmaking process. Considering the aforementioned issues, this paper proposes an integrated multi-objective optimization framework for optimizing burden surface distribution based on the analysis of BF operation characteristics. Firstly, data-driven models are constructed for two objectives, i.e., gas utilization ratio (GUR) and coke ratio (CR), and two constraints using adaptive particle swarm optimization (APSO) based extreme learning machine (ELM), named APSO-ELM. Multi-objective optimization is subsequently carried out between GUR and CR using the multi-objective differential evolution algorithm (MODE) to generate the Pareto optimal solutions. Finally, TOPSIS is applied to select a best compromise solution among the Pareto optimal solutions for this optimization problem. Comprehensive experiments are presented to illustrate the performance of the proposed integrated multi-objective optimization framework. The experimental results demonstrate that the proposed framework can give a reasonable burden surface profile according to the production status changes to guarantee the BF operation more efficient and stable.

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Section: B Optimization Strategy For Burden Surface Based On Mode Anmentioning

confidence: 99%

Burden Surface Decision Using MODE With TOPSIS in Blast Furnace Ironmkaing

Zhang

et al. 2020

IEEE Access

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“…In both cases, two design concepts are proposed: c 1 (diagonal concept) and c 2 (off-diagonal concept) as shown in (25) and (26), and (29) and (30). To stabilize the system, 1-DOF PIs controllers with anti-windup are used as shown in (27) and (28), and (31) and (32).…”

Section: Example 1: Non-linear Coupled Two-tank Systemmentioning

confidence: 99%

A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

et al. 2020

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In the multivariable control literature there are few techniques that face the problem of selecting suitable loop pairings in non-linear multivariable systems. Most techniques analyze the linearized system at a specific operating point. This paper proposes a new methodology to optimally and simultaneously select the loop pairings and the tuning of the parameters of the decentralized control by applying a multi-objective optimization approach directly on the non-linear system. The main contribution of this work is that the proposed methodology enables a detailed multi-dimensional analysis of the performances and trade-offs in the available loop pairings to control a multivariable non-linear system. The methodology is applied in this paper to three examples that analyze how the different types of loop pairings conflict. In one of the examples, the proposed methodology was applied first in the linearized system and later in the non-linear system. The results were contradictory and show how the application of loop pairing techniques for linear systems can be inaccurate when they are applied on a non-linear system previously linearized at an operating point. The following examples show that the operating point of a non-linear system, the design objectives of each multiobjective problem, as well as the designer's preferences have important roles in the selection of an optimal loop pairing. INDEX TERMS Multivariable control system, non-linear systems, loop pairing, decentralized control structures, multi-objective evolutionary optimization, Pareto front.

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“…In the same way as evolutionary algorithms, DE presents operators of recombination, mutation, and selection strategies. DE has had a remarkable acceptance in the diverse areas such as mechanical engineering design [28], mechatronic design [21], automatic control tuning [29], and so on. This fact is because of the suitable convergence and robustness properties, the reduced number of tuning parameters, and the success in solving real-world applications.…”

Section: Multiobjective Differential Evolution Algorithmmentioning

confidence: 99%

Synergistic Design of the Bipedal Lower-Limb through Multiobjective Differential Evolution Algorithm

Pantoja-García

Villarreal-Cervantes

García-Mendoza

et al. 2019

Mathematical Problems in Engineering

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The complexity in the design of bipedal robots has motivated the use of simple mechanisms to accomplish the desired locomotion task with a minimum control effort. Nevertheless, a diverse set of conflictive design criteria must be met to develop the bipedal gait. In this paper, the synergy in the eight-bar mechanism design criteria to satisfy the bipedal lower-limb behavior is promoted by proposing a Pareto-based Nonlinear Mixed Discrete-Continuous Constrained Multiobjective Optimization Problem and by improving the search in the optimizer through the inclusion of the Multiselection Strategy into the multiobjective differential evolution algorithm, where the conflictive design objectives are the continuous path generation based on an approximation error to track twenty precision points of the bipedal gait and also the force transmission exerted when the foot reaches the ground. The manufacture of a prototype with a specific design trade-off experimentally validates the obtained synergistic design with the proposed design approach. In addition, the empirical analysis in simulation through a statistical validation indicates that the Multiselection Strategy explores and exploits the design solutions promoting the diversity, convergence, and capacity features of the obtained Pareto front with respect to other four multiobjective optimizers and consequently improves the reconfigurability in the design such that more alternatives result to the designer decision making.

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Multi-Objective On-Line Optimization Approach for the DC Motor Controller Tuning Using Differential Evolution

Cited by 33 publications

References 27 publications

Burden Surface Decision Using MODE With TOPSIS in Blast Furnace Ironmkaing

Burden Surface Decision Using MODE With TOPSIS in Blast Furnace Ironmkaing

A Loop Pairing Method for Non-Linear Multivariable Control Systems Under a Multi-Objective Optimization Approach

Synergistic Design of the Bipedal Lower-Limb through Multiobjective Differential Evolution Algorithm

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