Laura Ferranti, Ye Pu, Colin N. Jones, and Tamas Keviczky
55th IEEE Conference on Decision and Control
Publication year: 2016

Abstract

This paper focuses on the design of an asynchronous dual solver suitable for embedded model predictive control (MPC) applications. The proposed solver relies on a state-of-the-art variance reduction (VR) scheme, previously used in the context of stochastic proximal gradient methods, and on the alternating minimization algorithm (AMA). The resultant algorithm, a stochastic AMA with VR, shows geometric convergence (in the expectation) to a suboptimal solution of the MPC problem and, compared to other state-of-the-art dual asynchronous algorithms, allows to tune the probability of the asynchronous updates to improve the quality of the estimates. We apply the proposed algorithm to a specific class of splitting methods, i.e., the decomposition along the length of the prediction horizon, and provide preliminary numerical results on a practical application, the longitudinal control of an Airbus passenger aircraft.

 

Keywords

  • Model predictive control;
  • Optimization;
  • Alternating minimization algorithm;
  • Stochastic proximal gradient;
  • Variance reduction;
  • Flight control.