Postdecision-State-Based Approximate Dynamic Programming for Robust Predictive Control of Constrained Stochastic Processes

2011 ◽  
Vol 50 (3) ◽  
pp. 1389-1399 ◽  
Author(s):  
Wee Chin Wong ◽  
Jay H. Lee
Keyword(s):  
Dynamic Programming ◽  
Stochastic Processes ◽  
Predictive Control ◽  
Approximate Dynamic Programming ◽  
Robust Predictive Control

scholarly journals Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm

2021 ◽  
Author(s):  
Hossein Nejatbakhsh Esfahani ◽  
Rafal Szlapczynski
Keyword(s):  
Dynamic Programming ◽  
Time Delay ◽  
Control Algorithm ◽  
Approximate Dynamic Programming ◽  
Robust Adaptive Control ◽  
Stochastic Disturbances ◽  
Time Delay Control ◽  
Model Free ◽  
Delay Control ◽  
Robust Adaptive

AbstractThis paper proposes a hybrid robust-adaptive learning-based control scheme based on Approximate Dynamic Programming (ADP) for the tracking control of autonomous ship maneuvering. We adopt a Time-Delay Control (TDC) approach, which is known as a simple, practical, model free and roughly robust strategy, combined with an Actor-Critic Approximate Dynamic Programming (ACADP) algorithm as an adaptive part in the proposed hybrid control algorithm. Based on this integration, Actor-Critic Time-Delay Control (AC-TDC) is proposed. It offers a high-performance robust-adaptive control approach for path following of autonomous ships under deterministic and stochastic disturbances induced by the winds, waves, and ocean currents. Computer simulations have been conducted under two different conditions in terms of the deterministic and stochastic disturbances and all simulation results indicate an acceptable performance in tracking of paths for the proposed control algorithm in comparison with the conventional TDC approach.

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