1,721,020 research outputs found
Nonlinear State and Parameter Estimation for Hopper Dredgers
No full textA Trailing Suction Hopper Dredger (TSHD) is a ship that excavates sediments from the sea bottom while sailing. In situ material is excavated with a special tool called the Drag-Head, then it is hydraulically transported through a pipe to the hopper where it is temporarily stored. After the dredging is completed the collected material is transported and discharged at a specified location. The efficiency of this process is highly dependent on the detailed knowledge of the excavated soil. The optimization of dredging operations is of vital importance for future improvement in efficiency, accuracy and from the viewpoint of labor saving. The automated onboard systems that have been developed to optimize the dredging performance require knowledge of several uncertain soil-dependent parameters. These cannot be directly measured but have to be estimated online from the available measurements. Such estimation is a challenging task due to lack of sufficient sensors, severe nonlinearities in models, and time-varying nature of the parameters of interest. In this thesis we focus on two of the most important TSHD-related models. These are: I. Drag-Head Model - describing the excavation process, II. Hopper Model - describing the sedimentation process occurring inside the hopper. They contain several uncertain soil-dependent parameters that need to be estimated. These are: I. horizontal cutting force coefficient kch (Drag-Head Model ), II. ratio kvh between the horizontal and vertical cutting forces (Drag-Head Model ), III. in situ permeability ksi (Drag-Head Model ), IV. average grain diameter dm (Hopper Model ). Both processes, together with the corresponding estimation problems, are discussed in detail in Chapter 2. The highly uncertain and time-varying nature of the soil-dependent parameters and the nonlinear dynamics of the models used to describe dredging process make the estimation a challenging task. The algorithms that are capable of tackling these type of problems are Nonlinear Bayesian Filters (NBF). In Chapter 3 we review several types of NBF, namely: I. parametric filters based on the Taylor series expansion (EKF, IEKF), II. parametric filters based on statistical approximations (UKF, GHF, CDF), III. parametric filters based on Gaussian Sum approximations (GSF), IV. nonparametric filters based on the importance sampling (BPF), V. nonparametric filters based on the mean-field control-oriented approach (FPF). In Chapter 4 we investigate the applicability of these nonlinear filters to the estimation problems that originate from the Drag-Head Model. The problems are: the Cutting Estimation Problem and the Cutting and Jetting Estimation Problem. The Cutting Estimation Problem applies for any cutting excavation tool whereas the Cutting and Jetting Estimation Problem is applicable only for tools equipped with cutting and jetting components. The former problem considers estimation of the ratio kvh between cutting forces and the horizontal cutting force coefficient kch, the latter problem deals with the estimation of the horizontal cutting force coefficient kch and the in situ permeability ksi. To solve the aforementioned estimation problems one needs to handle time-varying delay in the measurement of incoming density ?i, which is discussed separately. It is concluded that among the tested methods the best solution to the Cutting Estimation Problem is provided by the CDF and, in case of large uncertainty in the initial states, by the GSF. To solve the Cutting and Jetting Estimation Problem it is crucial to exploit the correlation between the horizontal cutting force coefficient kch and the in situ permeability ksi. This is done by a cascaded filter, which uses the PF to obtain an estimate of ksi, which will be further filtered by a Steady State Identification (SSI) filter, and finally by the BF to produce a final estimate of kch. In Chapter 5 we develop a novel class of nonlinear particle filters: the Saturated Particle Filter (SPF) that is used to solve the Hopper Estimation Problem. The SPF is a general method designed for Saturated Stochastic Dynamical Systems (SSDS), which are severely nonlinear systems often used in modeling real-life problems. They are characterized by a constrained probability distribution exhibiting singularity on the boundary of the saturation region. Such singularities make it difficult to estimate the states or the parameters of SSDSs by standard nonlinear filters. Our new method exploits the specific structure of the SSDS in order to design an importance sampling distribution that accounts for the most recent measurements in the prediction step of the filtering algorithm. Chapter 6 deals with the asymptotic properties of the SPF. We establish the conditions under which the SPF converges to the optimal theoretical filter. The convergence of our method is closely related to the appropriate resampling scheme. This led to the development of the improved Saturated Particle Filter (iSPF) which combines the importance sampling of the SPF with a novel resampling algorithm. In Chapter 7 the iSPF together with other nonparametric methods from Chapter 3 are used to estimate the average grain diameter dm, which solves the Hopper Estimation Problem. Because the sedimentation process is naturally divided into three regimes, to find the most efficient filtering method we considered each mode separately. We conclude that: I. for the No-Overflow loading phase the best estimate of dm is obtained by the FPF, II. for the Overflow loading phases with weak erosion, the recommended filtering method is the Reduced-Order PF, III. for the Overflow loading phases with strong erosion, the best estimation performance is achieved by the Reduced-Order PF when the excavated soil is fine and the Hybrid SPF when the excavated soil is coarse. The final solution to the Hopper Estimation Problem is obtained by integrating the filters designed for separate modes into a global estimator. Chapter 8 concludes the thesis.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
Soft Computing Methods in Flight Control System Design
No full textMechanical Maritime and Materials Engineerin
Online Model Learning Algorithms for Actor-Critic Control
No full textClassical control theory requires a model to be derived for a system, before any control design can take place. This can be a hard, time-consuming process if the system is complex. Moreover, there is no way of escaping modelling errors. As an alternative approach, there is the possibility of having the system learn a controller by itself while it is in operation or offline. Reinforcement learning (RL) is such a framework in which an agent (or controller) optimises its behaviour by interacting with its environment. For continuous state and action spaces, the use of function approximators is a necessity and a commonly used type of RL algorithms for these continuous spaces is the actor-critic algorithm, in which two independent function approximators take the role of the policy (the actor) and the value function (the critic). A main challenge in RL is to use the information gathered during the interaction as efficiently as possible, such that an optimal policy may be reached in a short amount of time. The majority of RL algorithms at each time step measure the state, choose an action corresponding to this state, measure the next state, the corresponding reward and update a value function (and possibly a separate policy). As such, the only source of information used for learning at each time step is the last transition sample. This thesis proposes novel actor-critic methods that aim to shorten the learning time by using every transition sample collected during learning to learn a model of the system online. It also explores the possibility of speeding up learning by providing the agent with explicit knowledge of the reward function.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
Simulation of an Artificial Respiratory System: Choosing a New Actuator for Implementation in a Lung Simulator
No full textIt is suspected that instability problems in the current generation of lung simulators are caused by its actuator, a brushless DC motor, in combination with the system configuration. The hypothesis is that these problems can be resolved by replacing the actuator with a backdrivable actuator (that is, an actuator that responds well to external force) in a new system. In this BSc Thesis this hypothesis is researched. The backdrivable actuator (in this particular case, a Voice Coil actuator) in a new system can overcome the instability problems.Electrical EngineeringElectrical Engineering, Mathematics and Computer Scienc
Featherweight Camera stabilisatie systeem: Gebruiksonderzoek, ontwerp en prototype
No full textDe professionele paraglider en fotograaf Leo Westerkamp is instaat de kijker dichter bij de natuur en sport te brengen dan ieder ander log en niet wendbaar apparaat. Echter door bochten, windvlagen, luchtdrukverschillen en trillingen van de motor ondervinden de beelden een ernstig stabiliteitsprobleem. Vandaar dan ook dat Featherweight de opdracht heeft gekregen om de camerabeelden zodanig te stabiliseren dat de beelden te verkopen zijn. Tijdens het ontwerpproces is er hoofdzakelijk rekening gehouden met de mogelijkheid de camerastabilisator lichtgewicht en makkelijk bestuurbaar te maken. Aan de andere kant mocht hierbij niet worden ingeleverd op de kwaliteit van de beelden. Ontwerp Om te voldoen aan de wensen van Westerkamp heeft Featherweight gekozen voor de Featherweight Professional. De Featherweight Professional bestaat uit een opzet waar de camera gestabiliseerd wordt door twee gekoppelde dc motoren. Westerkamp heeft zelf de controle om de camera ergens anders op te richten via een duimcontroller, en via de aangeraden LCD-bril is hij instaat zelf real-time te kijken waar hij de camera op heeft gefocust. De motoren worden aangestuurd door een laptop, wiens data verkregen wordt door een microcontroller (Arduino-Mega) en de positie van de duimcontroller. De Arduino-Mega is er om de data van de acceleratiemeter en gyrometer om te zetten naar de afwijkende hoeken die ontstaan zijn door stabilisatie problemen. Implementatie De data van de acceleratiemeter en gyrometer wordt via een I2C protocol naar de Arduino-Mega gestuurd. Hier wordt door gebruik te maken van een versimpelde Kalman filter de verkregen data omgezet naar het aantal graden dat de camera afwijkt van het punt waar hij op gestabiliseerd moet worden. De Arduino-Mega stuurt het op zijn beurt door naar de laptop met de RS-232 interface, waar het wordt verwerkt met MATLAB. Bij MATLAB ligt de nadruk op: - De hoeken aanpassen door de data van de duimcontroller en de Arduino Mega te combineren; - De hoeken begrenzen; - De hoeken en hoeksnelheden via een adaptieve PD regelaar om te zetten naar het gewenste koppel, waar de twee RX-64 motoren mee aangestuurd worden. Daarnaast worden de huidige beelden waar de camera opgericht staat, weergegeven via een LCD-scherm. Evaluatie Bij de evaluatie kwam naar voren dat veel van de eisen uit het programma van eisen voldaan werden, voor zover deze geïmplementeerd waren in het prototype. Het systeem is licht en compact genoeg en stabiliseert de grootste verstoringen uit het beeld. Daarnaast is het systeem makkelijk te bedienen en niet hinderlijk voor de piloot. Aanbevelingen Er wordt aanbevolen, voordat de Featherweight Professional in ontwikkeling wordt genomen, nader onderzoek te doen naar de gebruikerswensen, toepassen van een normale Kalman filter op de sensordata, en het omlaag brengen van de verwerkingstijd. Ook wordt er aangeraden om mogelijk over te stappen naar een gimbal constructie, het toepassen van een lock-functie zodat de camera altijd het gewenste object volgt, en het mogelijk toepassen van een softwarematige stabilisatie. Om Featherweight Professional op een bredere markt te verkopen, moet er onderzoek gedaan worden naar mogelijke klanten. Dit onderzoek kan gedaan worden door een gebruikersonderzoek uit te voeren op een testgroep.Bachelor EindprojectElectrical EngineeringElectrical Engineering, Mathematics and Computer Scienc
Knowledge Discovery and Pavement Performance: Intelligent Data Mining
No full textThe main goal of the study was to discover knowledge from data about asphalt road pavement problems to achieve a better understanding of the behavior of them and via this understanding improve pavement quality and enhance its lifespan. Four pavement problems were chosen to be investigated; raveling of Porous Asphalt Concrete (PAC), cracking of Dense Asphalt Concrete (DAC), rutting of dense asphalt concrete, and determination of the stiffness of Cement Treated Bases (CTBs). At the moment, almost 75% of the Dutch motorways network has a PAC top layer. Raveling is the most dominant type of damage of PAC top layers. The DAC top layers which are mainly applied to the secondary roads in the Netherlands are the most commonly used top layers worldwide. The two main damage types of this top layer are cracking and rutting. Determination of the stiffness of the cement treated base layer stiffness is not an easy task. Therefore, a tool which can accurately calculate the stiffness of such base layers is desirable. Concerning data, the SHRP-NL databases provided the data for the three surface damages, being ravelling of PAC, cracking and rutting of DAC. The data for climate and traffic were obtained from databases of the Royal Dutch Meteorological Institute (KNMI), the Ministry of Transport and Water Management, and different provinces of the Netherlands. The data for the stiffness of CTBs was simulated using the multilayer linear-elastic computer program BISAR. During preparation of the data, the determination of outliers was a challenging task. Due to the low number of data points available for raveling, cracking, and rutting (in one case around 70 data points), an extensive variable selection was performed using eight different methods: decision trees, genetic polynomial, artificial neural network, rough set theory, correlation based variable selection with bidirectional and genetic search, wrappers of neural network with genetic search, and relief ranking filter. For development of models (data mining) from the mentioned data, four machine learning based techniques were employed. Two were prediction techniques; artificial neural networks and support vector machines. The other two were rule based techniques; decision trees and rough set theory. This study resulted in 20 intelligent models for the mentioned four problemsCivil Engineering and Geoscience
Distributed Estimation and Control for Robotic Networks
No full textMobile robots that communicate and cooperate to achieve a common task have been the subject of an increasing research interest in recent years. These possibly heterogeneous groups of robots communicate locally via a communication network and therefore are usually referred to as robotic networks. Their potential applications are diverse and encompass monitoring, exploration, search and rescue, and disaster relief. From a research standpoint, in this thesis we consider specific aspects related to the foundations of robotic network algorithmic development: distributed estimation, control, and optimization. The word “distributed” refers to situations in which the cooperating robots have a limited, local knowledge of the environment and of the group, as opposed to a “centralized” scenario, where all the robots have access to the complete information. The typical challenge in distributed systems is to achieve similar results (in terms of performance of the estimation, control, or optimization task) with respect to a centralized system without extensive communication among the cooperating robots. In this thesis we develop effective distributed estimation, control, and optimization algorithms tailored to the distributed nature of robotic networks. These algorithms strive for limiting the local communication among the mobile robots, in order to be applicable in practical situations. In particular, we focus on issues related to nonlinearities of the dynamical model of the robots and their sensors, to the connectivity of the communication graph through which the robots interact, and to fast feasible solutions for the common (estimation or control) objective.DCSCMechanical, Maritime and Materials Engineerin
Reinforcement Learning on autonomous humanoid robots
No full textService robots have the potential to be of great value in households, health care and other labor intensive environments. However, these environments are typically unique, not very structured and frequently changing, which makes it difficult to make service robots robust and versatile through manual programming. Having robots learn to solve tasks autonomously through interaction with the real world forms an attractive alternative. With Reinforcement Learning (RL), a system can learn to perform tasks by receiving only coarse feedback on its actions: desired behavior is reinforced by positive rewards, undesired behavior is punished by negative rewards. In this research, a bipedal walking robot named Leo was designed and built specifically to study the application of RL to real robots. Robot Leo is able to learn two basic motor control tasks: placing a foot on a step of stairs, and walking. To learn to walk, Leo receives a positive reward for moving its foot forward, and negative rewards for falling and for spending time and energy. This process takes about 5 hours of practice in simulation, as well as thousands of falls. On the real prototype, the learning time was shortened by first letting the robot observe a hand coded, sub-optimal controller, which it was quickly able to mimic and even improve in a matter of hours. Algorithmic improvements are proposed to address complications of RL on real robots, such as time delays in the control loop and large disturbances such as a sudden push. To reduce the continuous risk of damage due to the trial-and-error nature of RL, a modular approach is proposed through which the robot can coarsely but quickly learn about the risk of its behavior and learn the actual task more safely and in more detail.BioMechanical EngineeringMechanical, Maritime and Materials Engineerin
Model-Based Control of Drinking-Water Treatment Plants
No full textThe drinking water in the Netherlands is of high quality and the production cost is low. This is the result of extensive research in the past decades to innovate and optimise the treatment processes. The processes are monitored and operated by motivated and skilled operators and process technologists, which leads to an operator-dependent, subjective, variable and possibly suboptimal operation of the treatment plants. Furthermore, the extensive automation of the treatment plants reduces the possible operator attention to the individual process units. The use of mathematical process models might solve these problems. This thesis focuses on the application of models in model-based monitoring, optimisation and control of drinking-water treatment plants, with the Weesperkarspel treatment plant of Waternet as a case study. To shift the operation of drinking water treatment plants from experience driven to knowledge based, a model-based approach is shown to be effective. Models are successfully used in plant analysis and basic control design, resulting in the successful implementation of new basic control for the softening reactors at the Weesperkarspel plant. Model-based monitoring schemes abstract relevant information from the large amount of data and the schemes estimate the current state of the processes. Model-based control uses the monitored process state to dynamically optimise the treatment without introducing new disturbances in the treatment plant. Model-based optimisation gives the process technologist the possibility to improve treatment operation without disrupting the full-scale plant.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
Full Color High Definition Fused Filament Fabrication
No full textAlthough Full Color (FC) High Definition (HD) Additive Manufacturing (AM) machines are available on the market today, a FC HD Fused Filament Fabrication (FFF) printer has yet to enter the competitive FFF market. Leapfrog, a FFF printer manufacturer, holds a patent that allows the application of coating on 3D printed FFF filament after it has been deposited. In this thesis work a prototype has been realized that serves as proof of the FC HD FFF concept and forms the basis of the commercial product which will be launched in the near future. Using inkjet technology high development costs can be avoided if existing coating devices are used in the machine. The challenges are to achieve a maximum outer surface coating of the 3D printed object and minimize the time it takes to coat a 3D printed object. This time is increased dramatically by applying a coating layer after every 3D printed layer compared to no coating. Using the prototype machine several coating methods have been explored of which the most promising method is perpendicular coating. Here, after 3D printing a layer, droplets are jetted onto the outward facing surface of the object. The total coating time is minimized by grouping contours which can be coated in one swath and sorting the order in which groups are coated. The grouping is done by formulating and solving a bin packing problem. Mixed Integer Linear Programming (MILP) and First Fit Decreasing (FFD) are compared and used to solve the problem, achieving more than 80% time reduction compared to no grouping on test models. A minimal movement time between all the groups is found by solving a Traveling Salesman Problem (TSP). The state of the art solver Concorde (CC) is compared to a Nearest Neighbor (NN) heuristic. A movement time reduction of 55% up to 85% is achieved compared to the standard 3D printing order on the test models. CC gives a maximum improvement of 2.3% over NN. The perpendicular coating method will be used to coat the filament. A significant total print time reduction is possible by grouping. MILP can have a hard time finding a solution for certain data sets, therefore FFD is preferred. For movement time reduction, a 2.3% performance increase by CC generally means that printing times are reduced by seconds. This is negligible considering 3D printing a layer generally takes minutes making NN a suitable heuristic for solving the TSP.Mechanical, Maritime and Materials EngineeringDelft Center for Systems and Control (DCSC
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