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Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System
The fast response and low inertia characteristics of converter-based generation (CBG) lead to a new stability issue that limits renewable energy...
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An improved teaching learning based optimization method to enrich the flight control of a helicopter system
A helicopter is a multivariable, nonlinear, higher order and strongly coupled system. The helicopter dynamics is subjected to unknown external...
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Collaborative Learning in Control Systems Using a Revised Team-Based Approach
Teaching control systems courses solely through lectures are only sometimes effective in preparing students for practical applications and...
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Deep learning based vessel arrivals monitoring via autoregressive statistical control charts
This paper introduces a methodology for monitoring the vessel arrival process, a critical factor in enhancing maritime operational efficiency. This...
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Better value estimation in Q-learning-based multi-agent reinforcement learning
In many real-life scenarios, multiple agents necessitate cooperation to accomplish tasks. Benefiting from the significant success of deep learning,...
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Structural optimization of multistage depressurization sleeve of axial flow control valve based on Stacking integrated learning
Due to the requirements of the working environment, the marine axial flow control valve needs to reduce the noise as much as possible while ensuring...
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Effectiveness of AI-assisted game-based learning on science learning outcomes, intrinsic motivation, cognitive load, and learning behavior
This study aimed to investigate the effectiveness of using AI-assisted game-based learning on science learning outcomes, intrinsic motivation,...
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5 Further Topics on Learning-based Control
Modeling human control strategy (HCS) refers to a model of a human expert’s control action in response to system real-time feedback. That is, we aim...
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Out-of-the-box parameter control for evolutionary and swarm-based algorithms with distributed reinforcement learning
Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their...
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A IPMSM Current Control Method Based on Reinforcement Learning
The control problem of Interior Permanent Magnet Synchronous Motor (IPMSM) under external disturbance has always been a difficult problem in the...
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Access Control Method for EV Charging Stations Based on State Aggregation and Q-Learning
This paper presents intelligent access control for a charging station and a framework for dynamically and adaptively managing charging requests from...
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Learning-based Adaptive Optimal Impedance Control to Enhance Physical Human-robot Interaction Performance
This paper presents a framework of adaptive optimal impedance control to enhance physical human-robot interaction (pHRI) performance. The overall...
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Machine learning based nonlinear adaptive optimal control of capacitive micro-actuator subjected to electrostatic field
Since controlling of parallel-plate micro-actuators and improving their speed and precision is an essential factor for tracking of high frequencies...
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Learning-based control approaches for service robots on cloth manipulation and dressing assistance: a comprehensive review
BackgroundService robots are defined as reprogrammable, sensor-based mechatronic devices that perform useful services in an autonomous or...
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Trajectory Tracking Control for Under-Actuated Hovercraft Using Differential Flatness and Reinforcement Learning-Based Active Disturbance Rejection Control
This paper proposes a scheme of trajectory tracking control for the hovercraft. Since the model of the hovercraft is under-actuated, nonlinear, and...
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Learning vision based autonomous lateral vehicle control without supervision
Supervised deep learning methods using image data as input have shown promising results in the context of vehicle control. However, these supervised...
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Traffic signal control using a cooperative EWMA-based multi-agent reinforcement learning
In contemporary urban, traffic signal control is still enormously difficult. Multi-agent reinforcement learning (MARL) is a promising ways to solve...
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Learning-Based Parameter Optimization for a Class of Orbital Tracking Control Laws
This paper presents a learning algorithm for tuning the parameters of a family of stabilizing nonlinear controllers for orbital tracking, in order to...
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Deep Learning Based Attack Detection for Microgrid Control
This chapter presents a deep learningDeep learning based multi-label attack detection approach for the distributed control Distributed control in AC...
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Adaptive industrial control data analysis based on deep learning
The fault detection and diagnosis of industrial production process is of great significance to the reliability and safety of modern industrial...
