Model Free Deep Deterministic Policy Gradient Controller for Setpoint Tracking of Non-Minimum Phase Systems

Fatemeh Tavakkoli, Pouria Sarhadi, Benoit Clement, Wasif Naeem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep Reinforcement Learning (DRL) techniques have received significant attention in control and decision-making algorithms. Most applications involve complex decision-making systems, justified by the algorithms' computational power and cost. While model-based versions are emerging, model-free DRL approaches are intriguing for their independence from models, yet they remain relatively less explored in terms of performance, particularly in applied control. This study conducts a thorough performance analysis comparing the data-driven DRL paradigm with a classical state feedback controller, both designed based on the same cost (reward) function of the linear quadratic regulator (LQR) problem. Twelve additional performance criteria are introduced to assess the controllers' performance, independent of the LQR problem for which they are designed. Two Deep Deterministic Policy Gradient (DDPG)-based controllers are developed, leveraging DDPG's widespread reputation. These controllers are aimed at addressing a challenging setpoint tracking problem in a Non-Minimum Phase (NMP) system. The performance and robustness of the controllers are assessed in the presence of operational challenges, including disturbance, noise, initial conditions, and model uncertainties. The findings suggest that the DDPG controller demonstrates promising behavior under rigorous test conditions. Nevertheless, further improvements are necessary for the DDPG controller to outperform classical methods in all criteria. While DRL algorithms may excel in complex environments owing to the flexibility in the reward function definition, this paper offers practical insights and a comparison framework specifically designed to evaluate these algorithms within the context of control engineering.
Original languageEnglish
Title of host publication2024 UKACC 14th International Conference on Control, CONTROL 2024
Place of PublicationWinchester, UK
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages163-168
Number of pages6
ISBN (Electronic)979-8-3503-7426-1
DOIs
Publication statusAccepted/In press - 1 Nov 2024
EventCONTROL 2024: 14th United Kingdom Automatic Control Council (UKACC) International Conference on Control - University of Southampton, Southampton, United Kingdom
Duration: 10 Apr 202412 Apr 2024
Conference number: 14
https://control2024.uk/

Publication series

Name2024 UKACC 14th International Conference on Control, CONTROL 2024

Conference

ConferenceCONTROL 2024: 14th United Kingdom Automatic Control Council (UKACC) International Conference on Control
Abbreviated titleCONTROL 2024
Country/TerritoryUnited Kingdom
CitySouthampton
Period10/04/2412/04/24
Internet address

Keywords

  • Training
  • State feedback
  • Uncertainty
  • Costs
  • Computational modeling
  • Decision making
  • Noise

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