Sequential hierarchical least-squares programming for prioritized non-linear optimal control

Kai Pfeiffer*, Abderrahmane Kheddar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We present a sequential hierarchical least-squares programming solver with trust-region and hierarchical step-filter with application to prioritized discrete non-linear optimal control. It is based on a hierarchical step-filter which resolves each priority level of a non-linear hierarchical least-squares programming via a globally convergent sequential quadratic programming step-filter. Leveraging a condition on the trust-region or the filter initialization, our hierarchical step-filter maintains this global convergence property. The hierarchical least-squares programming sub-problems are solved via a sparse reduced Hessian based interior point method. It leverages an efficient implementation of the turnback algorithm for the computation of nullspace bases for banded matrices. We propose a nullspace trust region adaptation method embedded within the sub-problem solver towards a comprehensive hierarchical step-filter. We demonstrate the computational efficiency of the hierarchical solver on typical test functions like the Rosenbrock and Himmelblau's functions, inverse kinematics problems and prioritized discrete non-linear optimal control.

Original languageEnglish
Pages (from-to)1104-1142
Number of pages39
JournalOptimization Methods and Software
Volume39
Issue number5
DOIs
Publication statusPublished - 2024
Externally publishedYes

Keywords

  • 49M15
  • 49M37
  • 65F50
  • 90C29
  • 90C55
  • Numerical optimization
  • discrete optimal control
  • filter methods
  • hierarchical non-linear least-squares programming
  • lexicographical optimization
  • multi objective optimization
  • sparse nullspace

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