Constrained and Stabilizing Stacked Adaptive Dynamic Programming and a Comparison with Model Predictive Control

Lukas Beckenbach, Pavel Osinenko, Thomas Gohrt, Stefan Streif

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Model predictive control (MPC) is in many applications the de facto approach to optimal control. It typically provides an optimal input (sequence) for a finite-horizon of given running costs. Another approach, called dynamic programming (DP), is based on the Hamilton-Jacobi-Bellman formalism and usually seeks optimal inputs over an infinite horizon of running costs. Unlike MPC, DP is much less computationally tractable and typically requires state space discretization which leads to the so-called curse of dimensionality. Adaptive dynamic programming (ADP), an approach based on reinforcement learning, seeks to address the difficulties of DP by introducing approximation models for the optimal cost function and control policies. In a variant of ADP called stacked ADP (sADP), control policies are optimized over a finite stack of value function approximants, thus making it somewhat similar to MPC. First, similarities and differences between a variant of ADP and MPC are discussed. Second, MPC stability results are transferred to ADP and state and input constraints are considered. The work is concluded by a case study.

Original languageEnglish
Title of host publication2018 European Control Conference, ECC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1349-1354
Number of pages6
ISBN (Electronic)9783952426982
DOIs
Publication statusPublished - 27 Nov 2018
Externally publishedYes
Event16th European Control Conference, ECC 2018 - Limassol, Cyprus
Duration: 12 Jun 201815 Jun 2018

Publication series

Name2018 European Control Conference, ECC 2018

Conference

Conference16th European Control Conference, ECC 2018
Country/TerritoryCyprus
CityLimassol
Period12/06/1815/06/18

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