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Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability...
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Federated learning-based edge computing for automatic train operation in communication-based train control systems
Automatic train operation (ATO) is a critical component of automatic train control (ATC) systems. The ATO automatically adjusts the speed of trains,...
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Reinforcement learning-based control for waste biorefining processes under uncertainty
Waste biorefining processes face significant challenges related to the variability of feedstocks. The supply and composition of multiple feedstocks...
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Event-based neural learning for quadrotor control
The design of a simple and adaptive flight controller is a real challenge in aerial robotics. A simple flight controller often generates a poor...
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Neural networks-based iterative learning control consensus for periodically time-varying multi-agent systems
In this paper, the problem of adaptive iterative learning based consensus control for periodically time-varying multi-agent systems is studied, in...
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Q-learning Based Adaptive Optimal Control for Linear Quadratic Tracking Problem
This paper describes a Q-learning based algorithm to design the linear quadratic tracker (LQT) for linear time invariant (LTI) continuous-time...
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A learning-based control pipeline for generic motor skills for quadruped robots
Performing diverse motor skills with a universal controller has been a longstanding challenge for legged robots. While motion imitation-based...
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A heterogeneous graph-based semi-supervised learning framework for access control decision-making
For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security....
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Designing an adaptive and deep learning based control framework for modular production systems
In today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s...
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Adaptive fuzzy iterative learning control based neurostimulation system and in-silico evaluation
Closed-loop neural stimulation has been an effective treatment for epilepsy patients. Currently, most closed-loop neural stimulation strategies are...
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SAKMR: Industrial control anomaly detection based on semi-supervised hybrid deep learning
With the advent of Industry 4.0, industrial control systems (ICS) are more and more closely connected with the Internet, leading to a rapid increase...
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A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system
Communication-based train control (CBTC) system is a typical cyber-physical system with open wireless communication that is vulnerable to attacks. To...
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Adaptive urban traffic signal control based on enhanced deep reinforcement learning
One of the focal points in the field of intelligent transportation is the intelligent control of traffic signals (TS), aimed at enhancing the...
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Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning
In this article, the dissolved oxygen (DO) concentration control problem in wastewater treatment process (WWTP) is studied. Unlike existing control...
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Reinforcement learning-based unknown reference tracking control of HMASs with nonidentical communication delays
This paper focuses on the optimal output synchronization control problem of heterogeneous multiagent systems (HMASs) subject to nonidentical...
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Optimal Incremental-containment Control of Two-order Swarm System Based on Reinforcement Learning
In this paper, the optimal incremental-containment control of two-order swarm system based on reinforcement learning (RL) is proposed to avoid the...
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Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model
We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external...
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RELight: a random ensemble reinforcement learning based method for traffic light control
AbstractTraffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional...
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A Learning-based Control Framework for Fast and Accurate Manipulation of a Flexible Object
This paper presents a learning-based control framework for fast (< 1.5 s ) and accurate manipulation of a flexible object, i.e., whip targeting. The...
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Estimator-based dynamic learning from neural control of discrete-time strict-feedback systems
The dynamic learning issue from adaptive neural control for a class of discrete-time strict-feedback nonlinear systems is the main topic of this...
