A Computationally Fast Iterative Dynamic Programming Method for Optimal Control of Loosely Coupled Dynamical Systems with Different Time Scales
Paper in proceedings, 2017

Iterative dynamic programming is a powerful method that is often used to solve finite-dimensional nonlinear constrained global optimal control problems. However, multi-dimensional problems are often computationally complex, and in some cases an infeasible result is generated despite the existence of a feasible solution. A new iterative multi-pass method is presented that reduces the execution time of multi-dimensional, loosely-coupled, dynamic programming problems, where some state variables exhibit dynamic behavior with time scales significantly smaller than the others. One potential application is the optimal control of a hybrid electrical vehicle, where the computational burden can be reduced by a factor on the order of 100 -- 10000. Furthermore, new regularization terms are introduced that typically improve the likelihood of generating a feasible optimal trajectory. Though the regularization terms may generate suboptimal solutions in the interim, with successive iterations the generated solution typically asymptotically approaches the true optimal solution.

Nonlinear control

Efficiency enhancement

Optimal control

Dynamic programming

Global optimization

Bang-bang control

Author

Jonathan Lock

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Tomas McKelvey

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IFAC-PapersOnLine

24058963 (eISSN)

1 5953-5960

Driving Forces

Sustainable development

Areas of Advance

Transport

Energy

Subject Categories

Computational Mathematics

Control Engineering

DOI

10.1016/j.ifacol.2017.08.1498

More information

Created

10/7/2017