Multiobjective strict dissipativity via a weighted sum approach

Grüne, Lars; Krügel, Lisa; Müller, Matthias A.

doi:10.1016/j.sysconle.2022.105396

Cited by 2 publications

(9 citation statements)

References 18 publications

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“…For an overview see, for example, [17] and the references cited therein. Recent work studies MOCP in view of MPC solution methods, using dissipativity arguments to ensure MPC-stability in a multicriteria setting [12,14].…”

Section: Single-and Multiobjective Optimal Control Problemmentioning

confidence: 99%

Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation

et al. 2023

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Dielectric elastomers are a promising technology for wave energy harvesting. An optimal system operation can allow maximizing the extracted energy and, simultaneously, reducing wear that would lead to a reduction in the wave harvester lifetime. We pursue a model‐based optimization approach to identify optimal controls for wave energy harvesters based on dielectric elastomers. First, a direct method is used for time‐discretization of the dielectric elastomer wave energy harvester in the optimal control problem. The two conflicting objectives are considered in a multiobjective optimization framework. Considering a periodic, sinusoidal wave excitation, the optimal solution shows turnpike properties for the optimal periodic mode of operation. However, since real wave motion is neither monochromatic nor predictable on longer time horizons, further extensions are pursued. First, we introduce a stochastic wave excitation. Second, an iterative model‐predictive control scheme is designed. Due to multiple objectives, the control scheme has to include an automated adaption of the corresponding priorities. Here, we propose and evaluate a heuristic rule‐based adaption in order to maintain the damage below target levels. The approach presented here might be used in the future to guarantee for autonomous operation of farms of wave energy harvesters.

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Section: Single-and Multiobjective Optimal Control Problemmentioning

confidence: 99%

Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation

et al. 2023

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“…To this end, the compromise solution is computed. Consider the utopia-tracking cost function in (11), the compromise solution is defined as…”

Section: Definitionmentioning

confidence: 99%

“…Clearly, the cost function (11) evaluates the normalized distance between L(x, u) and L * s in the cost space, by which the multi-objective control problem was transformed into a utopiatracking control problem. The compromise solution (x c s , u c s ) is a Pareto optimal solution of ( 8), and L c s is a point along the Pareto front, which is the closest one to the utopia point.…”

Section: Definitionmentioning

confidence: 99%

“…Therefore, considering the multiple conflicting criteria in (4), (x c s , u c s ) is the optimal steady operating condition of the system (1). The control goal of the MoMPC controller here consists in making the disturbed system robustly stabilized around (x c s , u c s ) by minimizing the normalized utopia-tracking cost function (11).…”

Section: Definitionmentioning

confidence: 99%

“…The weighted sum approach transforms the multiple objective functions into a single one by combining them with weighted coefficients, which has been a widely accepted technique to cope with the multi-objective problem in MoMPC because of its simplicity in understanding and implementing [10,11]. However, the selection and tuning of weighting coefficients need to be completed by tedious trial and error, which will be extremely difficult, especially for complicated non-linear dynamic systems [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust multi‐objective model predictive control for constrained nonlinear systems with disturbances

Wu,

Xue,

Liu

2023

IET Control Theory & Appl

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This work develops a new robust multi‐objective model predictive control (MoMPC) strategy for constrained non‐linear systems with bounded disturbances. The multiple objectives are always contradictory, and the presence of disturbances may result in the violation of state constraints and instability of the controlled system. Firstly, the conflict between multiple objectives is reconciled by minimizing the distance of the cost function vector to the vector of independently minimised objectives obtained by solving a set of steady‐state optimisation problems. Then the contractive state set, which takes into account the effect of disturbances on system states, is constructed to guarantee the robust satisfaction of state constraints. Finally, a stability constraint updated online by solving an auxiliary optimization problem is established to ensure the robust stability of the system under MoMPC. An isothermal chemical reactor system is employed to verify the effectiveness of the controller proposed here.

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Multiobjective strict dissipativity via a weighted sum approach

Cited by 2 publications

References 18 publications

Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation

Optimal operation of dielectric elastomer wave energy converters under harmonic and stochastic excitation

Robust multi‐objective model predictive control for constrained nonlinear systems with disturbances

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