Solving the Constrained Problem in Model Predictive Control Based Motion Cueing Algorithm with a Neural Network Approach - Archive ouverte HAL
Communication Dans Un Congrès Année : 2018

Solving the Constrained Problem in Model Predictive Control Based Motion Cueing Algorithm with a Neural Network Approach

Résumé

Because of the critical timing requirement, one major issue regarding model predictive control-based motion cueing algorithms is the calculation of real-time optimal solutions. In this paper, a continuous-time recurrent neural network-based gradient method is applied to compute the optimal control action in real time for an MPCbased MCA.We demonstrate that by implementing a saturation function for the constraints in the decision variables and a regulation for the energy function in the network, a constrained optimization problem can be solved without using any penalty function. Simulation results are included to compare the proposed approach and substantiate the applicability of recurrent neural networks as a quadratic programming solver. A comparison with another QP solver shows that our method can find an optimal solution much faster and with the same precision.
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Dates et versions

hal-01940772 , version 1 (19-12-2018)

Identifiants

  • HAL Id : hal-01940772 , version 1

Citer

Carolina Rengifo, Jean-Rémy Chardonnet, Damien Paillot, Hakim Mohellebi, Andras Kemeny. Solving the Constrained Problem in Model Predictive Control Based Motion Cueing Algorithm with a Neural Network Approach. Driving Simulation Conference 2018 Europe VR, Sep 2018, Antibes, France. pp.63-69. ⟨hal-01940772⟩
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