Subspace Predictive Control Tuning with Multiobjetive Optimization

Maciel, Victor Rafael Bezerra; Lima, Rafael B. C.

doi:10.1109/tla.2022.9693560

Cited by 1 publication

(1 citation statement)

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“…A hardware‐in‐the‐loop experiment demonstrated that the proposed strategy could efficiently achieve the set goals. Maciel et al [ 13 ] proposed a data‐driven subspace predictive control method and defined its performance index as the minimization of the error between the closed‐loop response and the desired reference trajectory. The optimal parameters of the controller were found by solving a multiobjective optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Research on the Optimal Output Power of Proton‐Exchange Membrane Fuel Cell Based on Improved Model Predictive Control Strategy

Qin,

Qi,

Sun

et al. 2023

Energy Tech

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To achieve optimal power output of proton‐exchange membrane fuel cell (PEMFC) under load variation and noise disturbance, this article proposes a recursive subspace model predictive control based on Laguerre function (RSMPCL) strategy. First, a fractional PEMFC identification model is established, and an optimal power setter is introduced to provide an optimal power value corresponding to the current load. Second, during the predictive process, a subspace predictor is updated in real time where a Householder transformer is applied to decrease the computation time of the frequent QR decomposition, and a variable forgetting factor is introduced to enhance the sensitivity and accuracy of predictive control. In the control process, a Laguerre function is employed to transform the problem of computing the input increment at the next step into calculating the coefficients of the input variables. This transformation reduces the computation time of the control quantities. Finally, load and disturbance tests conducted on a dSPACE semiphysical simulation platform indicate that compared with constrained subspace model predictive control, the proposed RSMPCL strategy can help the fuel cells achieve the optimal power point with control accuracy, robustness, and calculation speed.

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Section: Introductionmentioning

confidence: 99%