Data-driven virtual reference feedback tuning and reinforcement Q-learning for model-free position control of an aerodynamic system

2016 ◽  
Author(s):  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup ◽  
Raul-Cristian Roman
Keyword(s):  
Position Control ◽  
Data Driven ◽  
Virtual Reference ◽  
Q Learning ◽  
Model Free ◽  
Free Position

scholarly journals Data-driven Control of the Activated Sludge Process: IMC plus Feedforward Approach

2016 ◽  
Vol 11 (4) ◽  
pp. 522 ◽  
Author(s):  
Jose David Rojas ◽  
Orlando Arrieta ◽  
Montse Meneses ◽  
Ramon Vilanova
Keyword(s):  
Activated Sludge ◽  
Substrate Concentration ◽  
Internal Model ◽  
Internal Model Control ◽  
Data Driven ◽  
Activated Sludge Process ◽  
Virtual Reference ◽  
Model Free ◽  
Model Control ◽  
Feedforward Controller

<p>In the work presented in this paper, data-driven control is used to tune an Internal Model Control. Despite the fact that it may be contradictory to apply a model-free method to a model-based controller, this methodology has been successfully applied to a Activated Sludge Process (ASP) based wastewater treatment. In addition a feedforward controller over the influent substrate concentration was also computed using the virtual reference feedback tuning and applied to the same wastewater process to see the effect over the dissolved oxygen and the substrate concentration at the effluent.</p>

scholarly journals Open Loop Position Control of Soft Continuum Arm Using Deep Reinforcement Learning

2018 ◽  
Author(s):  
Sreeshankar Satheeshbabu ◽  
Naveen Kumar Uppalapati ◽  
Girish Chowdhary ◽  
Girish Krishnan
Keyword(s):  
Reinforcement Learning ◽  
Position Control ◽  
Numerical Models ◽  
Control Policy ◽  
Open Loop ◽  
External Loading ◽  
Q Learning ◽  
Model Free ◽  
Experience Replay ◽  
The Continuum

Soft robots undergo large nonlinear spatial deformations due to both inherent actuation and external loading. The physics underlying these deformations is complex, and often requires intricate analytical and numerical models. The complexity of these models may render traditional model based control difficult and unsuitable. Model-free methods offer an alternative for analyzing the behavior of such complex systems without the need for elaborate modeling techniques.In this paper, we present a model-free approach for open loop position control of a soft spatial continuum arm, based on deep reinforcement learning. The continuum arm is pneumatically actuated and attains a spatial workspace by a combination ofunidirectional bending and bidirectional torsional deformation. We use Deep-Q Learning with experience replay to train the system in simulation. The efficacy and robustness of the control policy obtained from the system is validated both in simulation and on the continuum arm prototype for varying external loading conditions

Data-driven model-free slip control of anti-lock braking systems using reinforcement Q-learning

2018 ◽  
Vol 275 ◽  
pp. 317-329 ◽  
Author(s):  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup
Keyword(s):  
Data Driven ◽  
Slip Control ◽  
Q Learning ◽  
Model Free

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning

2018 ◽  
Vol 73 ◽  
pp. 227-238 ◽  
Author(s):  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup ◽  
Raul-Cristian Roman
Keyword(s):  
Data Driven ◽  
Model Reference ◽  
Q Learning ◽  
Model Free ◽  
Model Reference Control

A Hybrid Model and Model-Free Position Control for a Reconfigurable Manipulator

2019 ◽  
Vol 24 (2) ◽  
pp. 785-795 ◽  
Author(s):  
Gaofeng Li ◽  
Dezhen Song ◽  
Shan Xu ◽  
Lei Sun ◽  
Jingtai Liu
Keyword(s):  
Hybrid Model ◽  
Position Control ◽  
Model Free ◽  
Free Position
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