Undiscounted Control Policy Generation for Continuous-Valued Optimal Control by Approximate Dynamic Programming
Preprint, 2021

We present a numerical method for generating the state-feedback control policy associated with general undiscounted, constant-setpoint, infinite-horizon, nonlinear optimal control problems with continuous state variables. The method is based on approximate dynamic programming, and is closely related to approximate policy iteration. Existing methods typically terminate based on the convergence of the control policy and either require a discounted problem formulation or demand the cost function to lie in a specific subclass of functions. The presented method extends on existing termination criteria by requiring both the control policy and the resulting system state to converge, allowing for use with undiscounted cost functions that are bounded and continuous. This paper defines the numerical method, derives the relevant underlying mathematical properties, and validates the numerical method with representative examples. A MATLAB implementation with the shown examples is freely available.

Undiscounted infinite-horizon

Control policy

Approximate dynamic programming

Optimal control

Author

Jonathan Lock

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Tomas McKelvey

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Subject Categories

Energy Engineering

Computational Mathematics

Control Engineering

Related datasets

UCPADP Matlab implementation [dataset]

URI: https://gitlab.com/lerneaen_hydra/ucpadp

More information

Latest update

4/27/2021