259 research outputs found
Review of dynamic positioning control in maritime microgrid systems
For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper
Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization
Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far
more limited in their flight envelope as compared to experienced human pilots,
thereby restricting the conditions UAVs can operate in and the types of
missions they can accomplish autonomously. This paper proposes a deep
reinforcement learning (DRL) controller to handle the nonlinear attitude
control problem, enabling extended flight envelopes for fixed-wing UAVs. A
proof-of-concept controller using the proximal policy optimization (PPO)
algorithm is developed, and is shown to be capable of stabilizing a fixed-wing
UAV from a large set of initial conditions to reference roll, pitch and
airspeed values. The training process is outlined and key factors for its
progression rate are considered, with the most important factor found to be
limiting the number of variables in the observation vector, and including
values for several previous time steps for these variables. The trained
reinforcement learning (RL) controller is compared to a
proportional-integral-derivative (PID) controller, and is found to converge in
more cases than the PID controller, with comparable performance. Furthermore,
the RL controller is shown to generalize well to unseen disturbances in the
form of wind and turbulence, even in severe disturbance conditions.Comment: 11 pages, 3 figures, 2019 International Conference on Unmanned
Aircraft Systems (ICUAS
Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms
This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences
Development of Robust Control Strategies for Autonomous Underwater Vehicles
The resources of the energy and chemical balance in the ocean sustain mankind in many ways. Therefore, ocean exploration is an essential task that is accomplished by deploying Underwater Vehicles. An Underwater Vehicle with autonomy feature for its navigation and control is called
Autonomous Underwater Vehicle (AUV). Among the task handled by an AUV, accurately positioning itself at a desired position with respect to the reference objects is called set-point control. Similarly, tracking of the reference trajectory is also another important task. Battery recharging of AUV, positioning with respect to underwater structure, cable, seabed, tracking of reference trajectory with desired accuracy and speed to avoid collision with the guiding vehicle in the last phase of docking are some significant applications where an AUV needs to perform
the above tasks. Parametric uncertainties in AUV dynamics and actuator torque limitation necessitate to design robust control algorithms to achieve motion control objectives in the face of uncertainties. Sliding Mode Controller (SMC), H / μ synthesis, model based PID group controllers are some of the robust controllers which have been applied to AUV. But SMC suffers from less efficient tuning of its switching gains due to model parameters and noisy estimated acceleration states appearing in its control law. In addition, demand of high control effort due to high frequency chattering is another drawback of SMC. Furthermore, real-time implementation of H / μ synthesis controller based on its stability study is restricted due to use of linearly approximated dynamic model of an AUV, which hinders achieving robustness. Moreover, model based PID group controllers suffer from implementation complexities and exhibit poor transient and steady-state performances under parametric uncertainties. On the other hand model free
Linear PID (LPID) has inherent problem of narrow convergence region, i.e.it can not ensure convergence of large initial error to zero. Additionally, it suffers from integrator-wind-up and subsequent saturation of actuator during the occurrence of large initial error. But LPID controller
has inherent capability to cope up with the uncertainties. In view of addressing the above said problem, this work proposes wind-up free Nonlinear PID with Bounded Integral (BI) and Bounded Derivative (BD) for set-point control and combination of continuous SMC with Nonlinear PID with BI and BD namely SM-N-PID with BI and BD for trajectory tracking.
Nonlinear functions are used for all P,I and D controllers (for both of set-point and tracking control) in addition to use of nonlinear tan hyperbolic function in SMC(for tracking only) such that torque demand from the controller can be kept within a limit. A direct Lyapunov analysis is pursued to prove stable motion of AUV. The efficacies of the proposed controllers are compared with other two controllers namely PD and N-PID without BI and BD for set-point control and PD plus Feedforward Compensation (FC) and SM-NPID without BI and BD for tracking control.
Multiple AUVs cooperatively performing a mission offers several advantages over a single AUV in a non-cooperative manner; such as reliability and increased work efficiency, etc. Bandwidth limitation in acoustic medium possess challenges in designing cooperative motion control
algorithm for multiple AUVs owing to the necessity of communication of sensors and actuator signals among AUVs. In literature, undirected graph based approach is used for control design under communication constraints and thus it is not suitable for large number of AUVs participating in a cooperative motion plan. Formation control is a popular cooperative motion control paradigm. This thesis models the formation as a minimally persistent directed graph and
proposes control schemes for maintaining the distance constraints during the course of motion of entire formation. For formation control each AUV uses Sliding Mode Nonlinear PID controller with Bounded Integrator and Bounded Derivative. Direct Lyapunov stability analysis in the
framework of input-to-state stability ensures the stable motion of formation while maintaining the desired distance constraints among the AUVs
Dynamic modelling and control of a flexible manoeuvring system.
In this research a twin rotor multi-input multi-output system (TRMS), which is a
laboratory platform with 2 degrees of freedom (DOF) is considered. Although, the
TRMS does not fly, it has a striking similarity with a helicopter, such as system
nonlinearities and cross-coupled modes. Therefore, the TRMS can be perceived as
an unconventional and complex "air vehicle" that poses formidable challenges in
modelling, control design and analysis, and implementation. These issues constitute
the scope of this research.
Linear and nonlinear models for the vertical movement of the TRMS are
obtained via system identification techniques using black-box modelling. The
approach yields input-output models without a priori defined model structure or
specific parameter settings reflecting any physical attributes of the system. Firstly,
linear parametric models, characterising the TRMS in its hovering operation mode,
are obtained using the potential of recursive least squares (RLS) estimation and
genetic algorithms (GAs). Further, a nonlinear model using multi-layer perceptron
(MLP) neural networks (NNs) is obtained. Such a high fidelity nonlinear model is
often required for nonlinear system simulation studies and is commonly employed in
the aerospace industry. Both time and frequency domain analyses are utilised to
investigate and develop confidence in the models obtained. The frequency domain
verification method is a useful tool in the validation of extracted parametric models.
It allows high-fidelity verification of dynamic characteristics over a frequency range
of interest. The resulting models are utilized in designing controllers for low
frequency vibration suppression, development of suitable feedback control laws for
set-point tracking, and design of augmented feedforward and feedback control
schemes for both vibration suppression and set-point tracking performance. The
modelling approaches presented here are shown to be suitable for modelling
complex new generation air vehicles, whose flight mechanics are not well
understood.
Modelling of the TRMS revealed the presence of resonance modes, which are
responsible for inducing unwanted vibrations in the system. Command shaping
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control strategies are developed to reduce motion and uneven mass induced
vibrations, produced by the main rotor during the vertical movement around the
lateral axis of the TRMS rig. 2-impulse, 3-impulse and 4-impulse sequence input
shapers and Iow-pass and band-stop digital filters are developed to shape the
command signals such that the resonance modes are not overly excited. The
effectiveness of this concept is then demonstrated in both simulation and real-time
experimental environments in terms of level of vibration reduction using power
spectral density profiles of the system response.
Combinations of intelligent and conventional techniques are commonly used
the control of complex dynamic systems. Such hybrid schemes have proved to be
efficient and can overcome the deficiencies of conventional and intelligent
controllers alone. The current study is confined to the development of two forms of
hybrid control schemes that combine fuzzy control and conventional PID
compensator for input tracking performance. The two hybrid control strategies
comprising conventional PO control plus PlO compensator and PO-type fuzzy
control plus PlO compensator are developed and implemented for set-point tracking
control of the vertical movement of the TRMS rig. It is observed that the hybrid
control schemes are superior to other feedback control strategies namely, PlO
compensator, pure PO-type and PI-type fuzzy controllers in terms of time domain
system behaviour.
This research also witnesses investigations into the development of an
augmented feedforward and feedback control scheme (AFFCS) for the control of
rigid body motion and vibration suppression of the TRMS. The main goal of this
framework is to satisfy performance objectives in terms of robust command tracking,
fast system response and minimum residual vibration. The developed control
strategies have been designed and implemented within both simulation and real-time
environments of the TRMS rig. The employed control strategies are shown to
demonstrate acceptable performances. The obtained results show that much
improved tracking is achieved on positive and negative cycles of the reference
signal, as compared to that without any control action. The system performance with
the feedback controller is significantly improved when the feedforward control
component is added. This leads to the conclusion that augmenting feedback control
with feedforward method can lead to more practical and accurate control of flexible
systems such as the TRMS
PREDICTION OF RESPIRATORY MOTION
Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier
Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review
Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area
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