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Stability and Convergence of a Randomized Model Predictive Control Strategy

Daniël Veldman, Alexandra Borkowski, Enrique Zuazua. Stability and Convergence of a Randomized Model Predictive Control Strategy (2022)

Abstract. This paper is concerned with a combination of Random Batch Methods (RBMs) and Model Predictive Control (MPC) called RBM-MPC. In RBM-MPC, the RBM is used to speed up the solution of the finite horizon optimal control problems that need to be solved in MPC. We analyze our algorithm in the linear quadratic setting and obtain explicit error estimates that characterize the stability and convergence of the proposed method. The obtained estimates are validated in numerical experiments that also demonstrate the effectiveness of RBM-MPC.

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arxiv: 2211.05463

Last updated on November 23, 2022

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Last Publications

Control of neural transport for normalizing flows

A Two-Stage Numerical Approach for the Sparse Initial Source Identification of a Diffusion-Advection Equation

Gaussian Beam ansatz for finite difference wave equations

Long-time convergence of a nonlocal Burgers’ equation towards the local N-wave

Optimal design of sensors via geometric criteria

  • Control of neural transport for normalizing flows
  • A Two-Stage Numerical Approach for the Sparse Initial Source Identification of a Diffusion-Advection Equation
  • Gaussian Beam ansatz for finite difference wave equations
  • Optimal design of sensors via geometric criteria
  • Eigenvalue bounds for the Gramian operator of the heat equation
  • Control of neural transport for normalizing flows
  • A Two-Stage Numerical Approach for the Sparse Initial Source Identification of a Diffusion-Advection Equation
  • Gaussian Beam ansatz for finite difference wave equations
  • Optimal design of sensors via geometric criteria
  • Eigenvalue bounds for the Gramian operator of the heat equation
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