A Sphere Decoding Algorithm for Multistep Sequential Model-Predictive Control

Grimm, Ferdinand; Kolahian, Pouya; Zhang, Zhenbin; Baghdadi, Mehdi

doi:10.1109/tia.2021.3060694

Cited by 24 publications

(6 citation statements)

References 27 publications

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“…The computational process will be presented concisely. Further detail about the SDA can be found in [18], [19]. By using (5), the cost function (10) becomes:…”

Section: Sphere Decoding Algorithm (Sda)mentioning

confidence: 99%

“…Then during the evaluation process, the size of the sphere is decreased until there is only one solution contained in it, and that is the optimal control input sequence. Each time a node is visited, the radius 𝑝 𝑖 is calculated by (17) before evaluating the condition (18). If 𝑝 𝑖 violates condition (18), the node 𝑢 𝑖 will be discarded and its following nodes (from 𝑢 𝑖+1 to 𝑢 3𝑁 ) will be discarded without performing any computation to avoid unnecessary computation.…”

Section: Sphere Decoding Algorithm (Sda)mentioning

confidence: 99%

“…Each time a node is visited, the radius 𝑝 𝑖 is calculated by (17) before evaluating the condition (18). If 𝑝 𝑖 violates condition (18), the node 𝑢 𝑖 will be discarded and its following nodes (from 𝑢 𝑖+1 to 𝑢 3𝑁 ) will be discarded without performing any computation to avoid unnecessary computation. The control input having the smallest radius will be the optimal solution 𝑼(𝑘) = 𝑼 𝑜𝑝𝑡 (𝑘).…”

Section: Sphere Decoding Algorithm (Sda)mentioning

confidence: 99%

See 2 more Smart Citations

Modified multistep model predictive control for three-phase induction motor drive system considering the common-mode voltage minimization

Pho

Cao

Hoan

et al. 2021

IJPEDS

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It is acknowledged that the common-mode voltage may have detrimental effects on an induction motor (IM) drive system if not properly addressed. Therefore, in this paper, a modified multistep model predictive control method for IM drive system considering the common-mode voltage minimization is proposed. This research uses a multi-objective cost function, before applying the Sphere Decoding Algorithm to find the optimal control input. The results show that the proposed control method not only reduces the common-mode voltage significantly but also mitigates the computational burden of the microprocessor without affecting the system performance. The proposed control method is simulated by MATLAB-Simulink for an IM drive system with an 11-level cascaded H-bridge inverter.

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“…The computational process will be presented concisely. Further detail about the SDA can be found in [18], [19]. By using (5), the cost function (10) becomes:…”

Section: Sphere Decoding Algorithm (Sda)mentioning

confidence: 99%

Section: Sphere Decoding Algorithm (Sda)mentioning

confidence: 99%

Section: Sphere Decoding Algorithm (Sda)mentioning

confidence: 99%

See 1 more Smart Citation

Modified multistep model predictive control for three-phase induction motor drive system considering the common-mode voltage minimization

Pho

Cao

Hoan

et al. 2021

IJPEDS

View full text Add to dashboard Cite

show abstract

“…Model predictive control (MPC) is an optimal control algorithm developed for industrial process control. Because its control mechanism has good adaptability to complex industrial processes, it has attracted the extensive attention of scholars and has achieved a lot of research results [1][2]. MPC algorithm uses a dynamic model to predict the future behavior of the system.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based State-Dependent ARX Modeling and Predictive Control of Nonlinear Systems

Kang

Peng

et al. 2023

IEEE Access

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For many practical industrial objects with time-varying operating points, strong nonlinearity, and difficulty in obtaining analytical models, the data-driven identification method is usually used to model such nonlinear systems. However, it is difficult for traditional modeling algorithms to effectively extract the dynamic characteristics of nonlinear systems from data and obtain accurate mathematical models. In this paper, we consider using the deep learning network combined with the state-dependent exogenous variable autoregressive (SD-ARX) model framework to build the nonlinear system model, so as to effectively and accurately learn the space-time characteristics of the nonlinear system from the sample data. Based on the idea, the hybrid models, i.e., the RNN-ARX model, CNN-ARX model, and RNN-CNN-ARX model are built, which use recurrent neural networks (RNN), convolutional neural networks (CNN) and their combination to fit the function-type coefficients of SD-ARX model, respectively. SD-ARX model based on deep learning has the advantages of local linearity and global nonlinearity. Compared with the other two models, the RNN-CNN-ARX model has a stronger ability to extract the multidimensional spatiotemporal dynamic characteristics of nonlinear systems, because it combines the advantages of RNN in mining temporal features and CNN in extracting spatial features. According to the structural characteristics of these models, three model-based predictive control (MPC) strategies are designed, i.e., RNN-ARX-MPC, CNN-ARX-MPC, and RNN-CNN-ARX-MPC. The real-time control comparative experiment on an actual multiwater-tank object shows that the proposed modeling and MPC method is feasible and effective for the modeling and predictive control of the nonlinear system.

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“…Model predictive control (MPC) is a well-developed control strategy that takes into account the nonlinear and strongly coupled characteristics of industrial systems. [4,5] A feedforward unbiased strategy predictive control method proposed by Li et al [6] improves the tracking performance of the controller and achieves an unbiased control target. Experimental comparisons with proportional integral derivative (PID) control and MPC strategies further attest to its superiority.…”

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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A Sphere Decoding Algorithm for Multistep Sequential Model-Predictive Control

Cited by 24 publications

References 27 publications

Modified multistep model predictive control for three-phase induction motor drive system considering the common-mode voltage minimization

Modified multistep model predictive control for three-phase induction motor drive system considering the common-mode voltage minimization

Deep Learning-Based State-Dependent ARX Modeling and Predictive Control of Nonlinear Systems

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

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