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Editorial: Advanced learning control in physical interaction tasks [PDF]
Frontiers in Robotics and AI, 2023 Chao Zeng +3 more
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Editorial: Advancements in neural learning control for enhanced multi-robot coordination [PDF]
Frontiers in Robotics and AIShude He +5 more
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State of the Art of Adaptive Dynamic Programming and Reinforcement Learning
CAAI Artificial Intelligence Research, 2022 This article introduces the state-of-the-art development of adaptive dynamic programming and reinforcement learning (ADPRL). First, algorithms in reinforcement learning (RL) are introduced and their roots in dynamic programming are illustrated.
Derong Liu, Mingming Ha, Shan Xue
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Learning meaningful controls for fluids [PDF]
ACM Transactions on Graphics, 2021 While modern fluid simulation methods achieve high-quality simulation results, it is still a big challenge to interpret and control motion from visual quantities, such as the advected marker density. These visual quantities play an important role in user interactions: Being familiar and meaningful to humans, these quantities have a strong correlation ...
Mengyu Chu +4 more
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Modeling and Control Design of a Contact-Based, Electrostatically Actuated Rotating Sphere
Actuators, 2022 The performance of micromirrors in terms of their maximum deflection is often limited due to mechanical constraints in the design. To increase the range of achievable deflection angles, we present a novel concept in which a free-lying sphere with a flat ...
Michael Olbrich +3 more
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Learning Control of Redundant DOF Robots by Optimization of Parameterized Control Space [PDF]
Modeling, Identification and Control, 1988 A framework for the learning control of robots based on a parameterized control space is doscussed. Emphasis is put on how to utilize stored motion knowledge.
Erling Lunde, Jens G. Balchen
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Discerning Discretization for Unmanned Underwater Vehicles DC Motor Control
Journal of Marine Science and Engineering, 2023 Discretization is the process of converting a continuous function or model or equation into discrete steps. In this work, learning and adaptive techniques are implemented to control DC motors that are used for actuating control surfaces of unmanned ...
Jovan Menezes, Timothy Sands
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An Adaptive Voltage Control Using Local Voltage Profile Mode and Similarity Ranking
Frontiers in Energy Research, 2022 Emergency voltage control provides a real-time online response to maintain the long-term voltage stability of a power system. Searching for an optimal emergency control solution is a hard combinatorial optimization problem because of the highly dynamic ...
Haomin Ma +4 more
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Control of a Quadrotor With Reinforcement Learning [PDF]
IEEE Robotics and Automation Letters, 2017 In this paper, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques. With reinforcement learning, a common network can be trained to directly map state to actuator command making any predefined control structure obsolete for training. Moreover, we present a new learning algorithm which differs
Hwangbo, Jemin +3 more
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Energies, 2021 In this paper, a novel Virtual State-feedback Reference Feedback Tuning (VSFRT) and Approximate Iterative Value Iteration Reinforcement Learning (AI-VIRL) are applied for learning linear reference model output (LRMO) tracking control of observable ...
Mircea-Bogdan Radac +1 more
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