TR2020-103

Inexact Adjoint-based SQP Algorithm for Real-Time Stochastic Nonlinear MPC


    •  Quirynen, R., Feng, X., Di Cairano, S., "Inexact Adjoint-based SQP Algorithm for Real-Time Stochastic Nonlinear MPC", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-103 PDF
      • @inproceedings{Quirynen2020jul2,
      • author = {Quirynen, Rien and Feng, Xuhui and Di Cairano, Stefano},
      • title = {Inexact Adjoint-based SQP Algorithm for Real-Time Stochastic Nonlinear MPC},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-103}
      • }
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  • Research Areas:

    Control, Optimization

This paper presents a real-time algorithm for stochastic nonlinear model predictive control (NMPC). The optimal control problem (OCP) involves a linearization based covariance matrix propagation to formulate the probabilistic chance constraints. Our proposed solution approach uses a tailored Jacobian approximation in combination with an adjoint-based sequential quadratic programming (SQP) method. The resulting algorithm allows the numerical elimination of the covariance matrices from the SQP subproblem, while ensuring Newton-type local convergence properties and preserving the block-sparse problem structure. It allows a considerable reduction of the computational complexity and preserves the positive definiteness of the covariance matrices at each iteration, unlike an exact Jacobian-based implementation. The realtime feasibility and closed-loop control performance of the proposed algorithm are illustrated on a case study of an autonomous driving application subject to external disturbances.