A New Point of View in Multivariable Controller Tuning Under Multiobjective Optimization by Considering Nearly Optimal Solutions

Pajares, Alberto; Blasco, X.; Herrero, J. M.; Reynoso-Meza, Gilberto

doi:10.1109/access.2019.2915556

Cited by 10 publications

(11 citation statements)

References 40 publications

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“…Each of these points is plotted on a different diagram (there are eight diagrams). If we call N i to f (x i ) 2 (see equation 21), then the eight points are:…”

Section: Resultsmentioning

confidence: 99%

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Design and Experimental Validation of the Temperature Control of a PEMFC Stack by Applying Multiobjective Optimization

et al. 2020

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The current environmental challenges require the implementation of environmentally friendly energy production systems. In this context, proton exchange membrane fuel cell stacks (PEMFC) represent, due to their high electrical efficiency and their low level of CO 2 emissions, a promising alternative technology. However, there are still many technical aspects that need to be improved before they become a commercial reality. One of them is the temperature control of the stack, since its electrical efficiency and its lifetime depend on the performance of this control. In this work, we design a multiloop PID control of the temperature of a PEMFC stack and validate it experimentally. The stack is the prime mover of a micro combined heat and power system (micro-CHP). For this task, we use a previously developed nonlinear model and apply a multiobjective optimization methodology. To assess its performance, the PID control is compared to a second PID control designed with a linearized model. The results show, on the one hand, the importance of having a nonlinear model valid in a wide operation range for the correct design of the temperature control of a PEMFC stack and, on the other hand, the advantages of applying a multiobjective optimization methodology to this problem.

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“…Each of these points is plotted on a different diagram (there are eight diagrams). If we call N i to f (x i ) 2 (see equation 21), then the eight points are:…”

Section: Resultsmentioning

confidence: 99%

“…This prevents the optimization algorithm from ruling out relevant solutions that are nearly optimal solutions in the aggregate formulation. For this question see [21].…”

Section: B Mop Design Objectives Constraints and Formulationmentioning

confidence: 99%

Design and Experimental Validation of the Temperature Control of a PEMFC Stack by Applying Multiobjective Optimization

et al. 2020

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“…e designer can then analyze these controllers (significantly different from the alternatives obtained in a classical MOP) and make the final decision with higher quality information. us, it is possible to find nearly optimal controllers with better performances than the optimal ones in some features not included in the design objectives [14]. In this situation, the designer could choose a nearly optimal controller instead of an optimal one.…”

Section: Introductionmentioning

confidence: 99%

“…erefore, the designer usually chooses only some of the indicators to define the MOP. e rest of the interesting indicators can be analyzed more cheaply in the decision stage [14].…”

Section: Introductionmentioning

confidence: 99%

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Multivariable Controller Design for the Cooling System of a PEM Fuel Cell by considering Nearly Optimal Solutions in a Multiobjective Optimization Approach

et al. 2020

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This paper presents a design for the multivariable control of a cooling system in a PEM (proton exchange membrane) fuel cell stack. This system is complex and challenging enough: interactions between variables, highly nonlinear dynamic behavior, etc. This design is carried out using a multiobjective optimization methodology. There are few previous works that address this problem using multiobjective techniques. Also, this work has, as a novelty, the consideration of, in addition to the optimal controllers, the nearly optimal controllers nondominated in their neighborhood (potentially useful alternatives). In the multiobjective optimization problem approach, the designer must make decisions that include design objectives; parameters of the controllers to be estimated; and the conditions and characteristics of the simulation of the system. However, to simplify the optimization and decision stages, the designer does not include all the desired scenarios in the multiobjective problem definition. Nevertheless, these aspects can be analyzed in the decision stage only for the controllers obtained with a much less computational cost. At this stage, the potentially useful alternatives can play an important role. These controllers have significantly different parameters and therefore allow the designer to make a final decision with additional valuable information. Nearly optimal controllers can obtain an improvement in some aspects not included in the multiobjective optimization problem. For example, in this paper, various aspects are analyzed regarding potentially useful solutions, such as (1) the influence of certain parameters of the simulator; (2) the sample time of the controller; (3) the effect of stack degradation; and (4) the robustness. Therefore, this paper highlights the relevance of this in-depth analysis using the methodology proposed in the design of the multivariable control of the cooling system of a PEM fuel cell. This analysis can modify the final choice of the designer.

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Advantages of using relevant nearly optimal solutions in multi-objective tuning of robust controllers

et al. 2023

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A New Point of View in Multivariable Controller Tuning Under Multiobjective Optimization by Considering Nearly Optimal Solutions

Cited by 10 publications

References 40 publications

Design and Experimental Validation of the Temperature Control of a PEMFC Stack by Applying Multiobjective Optimization

Design and Experimental Validation of the Temperature Control of a PEMFC Stack by Applying Multiobjective Optimization

Multivariable Controller Design for the Cooling System of a PEM Fuel Cell by considering Nearly Optimal Solutions in a Multiobjective Optimization Approach

Advantages of using relevant nearly optimal solutions in multi-objective tuning of robust controllers

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