114 research outputs found
Guidance and control of an autonomous underwater vehicle
Merged with duplicate record 10026.1/856 on 07.03.2017 by CS (TIS)A cooperative project between the Universities of Plymouth and Cranfield was aimed
at designing and developing an autonomous underwater vehicle named Hammerhead.
The work presented herein is to formulate an advance guidance and control system
and to implement it in the Hammerhead. This involves the description of Hammerhead
hardware from a control system perspective. In addition to the control system,
an intelligent navigation scheme and a state of the art vision system is also developed.
However, the development of these submodules is out of the scope of this thesis.
To model an underwater vehicle, the traditional way is to acquire painstaking mathematical
models based on laws of physics and then simplify and linearise the models to
some operating point. One of the principal novelties of this research is the use of system
identification techniques on actual vehicle data obtained from full scale in water
experiments. Two new guidance mechanisms have also been formulated for cruising
type vehicles. The first is a modification of the proportional navigation guidance for
missiles whilst the other is a hybrid law which is a combination of several guidance
strategies employed during different phases of the Right.
In addition to the modelling process and guidance systems, a number of robust control
methodologies have been conceived for Hammerhead. A discrete time linear
quadratic Gaussian with loop transfer recovery based autopilot is formulated and integrated
with the conventional and more advance guidance laws proposed. A model
predictive controller (MPC) has also been devised which is constructed using artificial
intelligence techniques such as genetic algorithms (GA) and fuzzy logic. A GA
is employed as an online optimization routine whilst fuzzy logic has been exploited
as an objective function in an MPC framework. The GA-MPC autopilot has been
implemented in Hammerhead in real time and results demonstrate excellent robustness
despite the presence of disturbances and ever present modelling uncertainty. To
the author's knowledge, this is the first successful application of a GA in real time
optimization for controller tuning in the marine sector and thus the thesis makes an
extremely novel and useful contribution to control system design in general. The
controllers are also integrated with the proposed guidance laws and is also considered
to be an invaluable contribution to knowledge. Moreover, the autopilots are used in
conjunction with a vision based altitude information sensor and simulation results
demonstrate the efficacy of the controllers to cope with uncertain altitude demands.J&S MARINE LTD., QINETIQ,
SUBSEA 7 AND SOUTH WEST WATER PL
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means
of improving the autonomy of marine vehicles over the last few years. This
article surveys the recent ML approaches utilised for ship collision avoidance
(COLAV) and mission planning. Following an overview of the ever-expanding ML
exploitation for maritime vehicles, key topics in the mission planning of ships
are outlined. Notable papers with direct and indirect applications to the COLAV
subject are technically reviewed and compared. Critiques, challenges, and
future directions are also identified. The outcome clearly demonstrates the
thriving research in this field, even though commercial marine ships
incorporating machine intelligence able to perform autonomously under all
operating conditions are still a long way off
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning
Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. Following an overview of the ever-expanding ML exploitation for maritime vehicles, key topics in the mission planning of ships are outlined. Notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ships incorporating machine intelligence able to perform autonomously under all operating conditions are still a long way off.Peer reviewe
An Adaptive fuzzy-PD inertial control strategy for a DFIG wind turbine for frequency support
With increasing levels of wind generation in power systems, guaranteeing continuous power and system’s safety is essential. Frequency control is critical which requires a supplementary inertial control strategy. Since wind power generation depends directly on wind conditions, this creates an immense challenge for a conventional inertial controller with parameters suitable for all power grid operations and wind speed conditions. Therefore, tuning the controller gains is absolutely critical for an integrated conventional/renewable power system. Here, a fuzzy-logic adaptive inertial controller scheme for online tuning of the proportional-derivative-type (PD) inertial controller parameters is proposed. The proposed controller adapts the control parameters of the supplementary inertial control of the doubly fed induction generator (DFIG) wind turbine so that with any disturbance such as load changes, the active power output can be controlled to mitigate the frequency deviation. Simulation results indicate that the proposed adaptive controller demonstrates a more consistent and robust response to load changes compared to a conventional controller with fixed parameters
Greetings from the conference chairs
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record
Model Free Deep Deterministic Policy Gradient Controller for Setpoint Tracking of Non-minimum Phase Systems
Deep Reinforcement Learning (DRL) techniques have received significant
attention in control and decision-making algorithms. Most applications involve
complex decision-making systems, justified by the algorithms' computational
power and cost. While model-based versions are emerging, model-free DRL
approaches are intriguing for their independence from models, yet they remain
relatively less explored in terms of performance, particularly in applied
control. This study conducts a thorough performance analysis comparing the
data-driven DRL paradigm with a classical state feedback controller, both
designed based on the same cost (reward) function of the linear quadratic
regulator (LQR) problem. Twelve additional performance criteria are introduced
to assess the controllers' performance, independent of the LQR problem for
which they are designed. Two Deep Deterministic Policy Gradient (DDPG)-based
controllers are developed, leveraging DDPG's widespread reputation. These
controllers are aimed at addressing a challenging setpoint tracking problem in
a Non-Minimum Phase (NMP) system. The performance and robustness of the
controllers are assessed in the presence of operational challenges, including
disturbance, noise, initial conditions, and model uncertainties. The findings
suggest that the DDPG controller demonstrates promising behavior under rigorous
test conditions. Nevertheless, further improvements are necessary for the DDPG
controller to outperform classical methods in all criteria. While DRL
algorithms may excel in complex environments owing to the flexibility in the
reward function definition, this paper offers practical insights and a
comparison framework specifically designed to evaluate these algorithms within
the context of control engineering
Reinforcement Learning-Enhanced Control Barrier Functions for Robot Manipulators
In this paper we present the implementation of a Control Barrier Function
(CBF) using a quadratic program (QP) formulation that provides obstacle
avoidance for a robotic manipulator arm system. CBF is a control technique that
has emerged and developed over the past decade and has been extensively
explored in the literature on its mathematical foundations, proof of set
invariance and potential applications for a variety of safety-critical control
systems. In this work we will look at the design of CBF for the robotic
manipulator obstacle avoidance, discuss the selection of the CBF parameters and
present a Reinforcement Learning (RL) scheme to assist with finding parameters
values that provide the most efficient trajectory to successfully avoid
different sized obstacles. We then create a data-set across a range of
scenarios used to train a Neural-Network (NN) model that can be used within the
control scheme to allow the system to efficiently adapt to different obstacle
scenarios. Computer simulations (based on Matlab/Simulink) demonstrate the
effectiveness of the proposed algorithm
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