Enrico Anderlini
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View article: Nonlinear Model Predictive Control of Heaving Wave Energy Converter with Nonlinear Froude–Krylov Forces
Nonlinear Model Predictive Control of Heaving Wave Energy Converter with Nonlinear Froude–Krylov Forces Open
Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. …
View article: Passive Fault-Tolerant Augmented Neural Lyapunov Control: A method to synthesise control functions for marine vehicles affected by actuators faults
Passive Fault-Tolerant Augmented Neural Lyapunov Control: A method to synthesise control functions for marine vehicles affected by actuators faults Open
Closed-loop stability of control systems can be undermined by actuator faults. Redundant actuator sets and Fault-Tolerant Control (FTC) strategies can be exploited to enhance system resiliency to loss of actuator efficiency, complete failu…
View article: Radiation Force Modeling for a Wave Energy Converter Array
Radiation Force Modeling for a Wave Energy Converter Array Open
The motivation and focus of this work is to generate passive transfer function matrices that model the radiation forces for an array of WECs. Multivariable control design is often based on linear time-invariant (LTI) systems such as state-…
View article: Augmented Neural Lyapunov Control
Augmented Neural Lyapunov Control Open
Machine learning-based methodologies have recently been adapted to solve control problems. The Neural Lyapunov Control (NLC) method is one such example. This approach combines Artificial Neural Networks (ANNs) with Satisfiability Modulo Th…
View article: Machine learning in sustainable ship design and operation: A review
Machine learning in sustainable ship design and operation: A review Open
The shipping industry faces a large challenge as it needs to significantly lower the amounts of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has been achieved during the design stage and, after building…
View article: Leader Follower Formation Control for Underwater Transportation Using Multiple Autonomous Underwater Vehicles
Leader Follower Formation Control for Underwater Transportation Using Multiple Autonomous Underwater Vehicles Open
The successful ability to conduct underwater transportation using multiple autonomous underwater vehicles (AUVs) is important for the commercial sector to undertake precise underwater installations on large modules, whilst for the military…
View article: Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation
Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation Open
Grid-connected microgrids consisting of renewable energy sources, battery storage, and load require an appropriate energy management system that controls the battery operation. Traditionally, the operation of the battery is optimised using…
View article: Sliding Mode Control of a Nonlinear Wave Energy Converter Model
Sliding Mode Control of a Nonlinear Wave Energy Converter Model Open
The most accurate wave energy converter models for heaving point absorbers include nonlinearities, which increase as resonance is achieved to maximize the energy capture. Over the power production spectrum and within the physical limits of…
View article: Robust trajectory tracking control for unmanned surface vessels under motion constraints and environmental disturbances
Robust trajectory tracking control for unmanned surface vessels under motion constraints and environmental disturbances Open
To achieve a fully autonomous navigation for unmanned surface vessels (USVs), a robust control capability is essential. The control of USVs in complex maritime environments is rather challenging as numerous system uncertainties and environ…
View article: An Intelligent Energy Management Framework for Hybrid-Electric Propulsion Systems Using Deep Reinforcement Learning
An Intelligent Energy Management Framework for Hybrid-Electric Propulsion Systems Using Deep Reinforcement Learning Open
Hybrid-electric propulsion systems powered by clean energy derived from renewable sources offer a promising approach to decarbonise the world's transportation systems. Effective energy management systems are critical for such systems to ac…
View article: Unsupervised anomaly detection for underwater gliders using generative adversarial networks
Unsupervised anomaly detection for underwater gliders using generative adversarial networks Open
An effective anomaly detection system is critical for marine autonomous systems operating in complex and dynamic marine environments to reduce operational costs and achieve concurrent large-scale fleet deployments. However, developing an a…
View article: Hydrodynamic Modelling for a Transportation System of Two Unmanned Underwater Vehicles: Semi-Empirical, Numerical and Experimental Analyses
Hydrodynamic Modelling for a Transportation System of Two Unmanned Underwater Vehicles: Semi-Empirical, Numerical and Experimental Analyses Open
Underwater transportation is an essential approach for scientific exploration, maritime construction and military operations. Determining the hydrodynamic coefficients for a complex underwater transportation system comprising multiple vehi…
View article: Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks
Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks Open
Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogen…
View article: Collision Avoidance of External Obstacles for an Underwater Transportation System
Collision Avoidance of External Obstacles for an Underwater Transportation System Open
Transportation using multiple autonomous vehicles with detection avoidance capability is useful for military applications. It is important for such systems to avoid collisions with underwater obstacles in an effective way, while keeping tr…
View article: Electric Water Heaters Management via Reinforcement Learning With Time-Delay in Isolated Microgrids
Electric Water Heaters Management via Reinforcement Learning With Time-Delay in Isolated Microgrids Open
Isolated microgrids powered by renewable energy sources, battery storage, and backup diesel generators need appropriate demand response to utilize available energy and reduce diesel consumption efficiently. However, real-time demand-side m…
View article: Towards Real-Time Reinforcement Learning Control of a Wave Energy Converter
Towards Real-Time Reinforcement Learning Control of a Wave Energy Converter Open
The levellised cost of energy of wave energy converters (WECs) is not competitive with fossil fuel-powered stations yet. To improve the feasibility of wave energy, it is necessary to develop effective control strategies that maximise energ…
View article: Hydrodynamic Modelling of An Oscillating Wave Surge Converter Including Power Take-Off
Hydrodynamic Modelling of An Oscillating Wave Surge Converter Including Power Take-Off Open
To estimate the response of wave energy converters to different sea environments accurately is an ongoing challenge for researchers and industry, considering that there has to be a balance between guaranteeing their integrity whilst extrac…
View article: Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning
Docking Control of an Autonomous Underwater Vehicle Using Reinforcement Learning Open
To achieve persistent systems in the future, autonomous underwater vehicles (AUVs) will need to autonomously dock onto a charging station. Here, reinforcement learning strategies were applied for the first time to control the docking of an…
View article: Development of a Simulation Platform for Underwater Transportation using Two Hovering Autonomous Underwater Vehicles
Development of a Simulation Platform for Underwater Transportation using Two Hovering Autonomous Underwater Vehicles Open
This paper considers two HAUVs undertaking underwater transportation of a spherical payload via cylindrical manipulators.The rigid body connection method of transportation is explored.In this analysis, the nonlinear coupled dynamic model i…
View article: Centralized Control System Design for Underwater Transportation using two Hovering Autonomous Underwater Vehicles (HAUVs)
Centralized Control System Design for Underwater Transportation using two Hovering Autonomous Underwater Vehicles (HAUVs) Open
In this paper, a centralized control system is designed for the two HAUVs undertaking underwater transportation of a spherical payload via cylindrical manipulators. First, the nonlinear coupled dynamic model is developed considering the ri…
View article: Reactive control of a two-body point absorber using reinforcement learning
Reactive control of a two-body point absorber using reinforcement learning Open
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.
View article: Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration
Control of a Realistic Wave Energy Converter Model Using Least-Squares Policy Iteration Open
Published
View article: Control of wave energy converters using machine learning strategies
Control of wave energy converters using machine learning strategies Open
Wave energy converters are devices that are designed to extract power from ocean
\nwaves. Existing wave energy converter technologies are not financially viable yet. Control
\nsystems have been identified as one of the areas that can contr…
View article: Control of a Point Absorber Using Reinforcement Learning
Control of a Point Absorber Using Reinforcement Learning Open
This work presents the application of reinforcement learning for the optimal resistive control of a point absorber. The model-free Q-learning algorithm is selected in order to maximise energy absorption in each sea state. Step changes are …