| 1. |
- Carvalho Bittencourt, André, 1984-
(författare)
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Modeling and Diagnosis of Friction and Wear in Industrial Robots
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- High availability and low operational costs are critical for industrial systems. While industrial equipments are designed to endure several years of uninterrupted operation, their behavior and performance will eventually deteriorate over time. To support service and operation decisions, it is important to devise methods to infer the condition of equipments from available data.The monitoring of industrial robots is an important problem considered in this thesis. The main focus is on the design of methods for the detection of excessive degradations due to wear in a robot joint. Since wear is related to friction, an important idea for the proposed solutions is to analyze the behavior of friction in the joint to infer about wear. Based on a proposed friction model and friction data collected from dedicated experiments, a method is suggested to estimate wear-related effects to friction. As it is shown, the achieved estimates allow for a clear distinction of the wear effects even in the presence of large variations to friction associated to other variables, such as temperature and load.In automated manufacturing, a continuous and repeatable operation of equipments is important to achieve production requirements. Such repetitive behavior of equipments is explored to define a data-driven approach to diagnosis. Considering data collected from a repetitive operation, an abnormality is inferred by comparing nominal against monitored data in the distribution domain. The approach is demonstrated with successful applications for the diagnosis of wear in industrial robots and gear faults in a rotating machine.Because only limited knowledge can be embedded in a fault detection method, it is important to evaluate solutions in scenarios of practical relevance. A simulation based framework is proposed that allows for determination of which variables affect a fault detection method the most and how these variables delimit the effectiveness of the solution. Based on an average performance criterion, an approach is also suggested for a direct comparison of different methods. The ideas are illustrated for the robotics application, revealing properties of the problem and of different fault detection solutions.An important task in fault diagnosis is a correct determination of presence of a condition change. An early and reliable detection of an abnormality is important to support service, giving enough time to perform maintenance and avoid downtime. Data-driven methods are proposed for anomaly detection that only require availability of nominal data and minimal/meaningful specification parameters from the user. Estimates of the detection uncertainties are also possible, supporting higher level service decisions. The approach is illustrated with simulations and real data examples including the robotics application.
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| 2. |
- Evestedt, Niclas
(författare)
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Sampling Based Motion Planning for Heavy Duty Autonomous Vehicles
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The automotive industry is undergoing a revolution where the more traditional mechanical values are replaced by an ever increasing number of Advanced Driver Assistance Systems (ADAS) where advanced algorithms and software development are taking a bigger role. Increased safety, reduced emissions and the possibility of completely new business models are driving the development and most automotive companies have started projects that aim towards fully autonomous vehicles. For industrial applications that provide a closed environment, such as mining facilities, harbors, agriculture and airports, full implementation of the technology is already available with increased productivity, reliability and reduced wear on equipment as a result. However, it also gives the opportunity to create a safer working environment when human drivers can be removed from dangerous working conditions. Regardless of the application an important part of any mobile autonomous system is the motion planning layer. In this thesis sampling-based motion planning algorithms are used to solve several non-holonomic and kinodynamic planning problems for car-like robotic vehicles in different application areas that all present different challenges.First we present an extension to the probabilistic sampling-based Closed-Loop Rapidly exploring Random Tree (CL-RRT) framework that significantly increases the probability of drawing a valid sample for platforms with second order differential constraints. When a tree extension is found infeasible a new acceleration profile that tries to brings the vehicle to a full stop before the collision occurs is calculated. A resimulation of the tree extension with the new acceleration profile is then performed. The framework is tested on a heavy-duty Scania G480 mining truck in a simple constructed scenario.Furthermore, we present two different driver assistance systems for the complicated task of reversing with a truck with a dolly-steered trailer. The first is a manual system where the user can easily construct a kinematically feasible path through a graphical user interface. The second is a fully automatic planner, based on the CL-RRT algorithm where only a start and goal position need to be provided. For both approaches, the internal angles of the trailer configuration are stabilized using a Linear Quadratic (LQ) controller and path following is achieved through a pure-pursuit control law. The systems are demonstrated on a small-scale test vehicle with good results.Finally, we look at the planning problem for an autonomous vehicle in an urban setting with dense traffic for two different time-critical maneuvers, namely, intersection merging and highway merging. In these situations, a social interplay between drivers is often necessary in order to perform a safe merge. To model this interaction a prediction engine is developed and used to predict the future evolution of the complete traffic scene given our own intended trajectory. Real-time capabilities are demonstrated through a series of simulations with varying traffic densities. It is shown, in simulation, that the proposed method is capable of safe merging in much denser traffic compared to a base-line method where a constant velocity model is used for predictions.
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| 3. |
- Ljungberg, Fredrik, 1993-
(författare)
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Estimation of Nonlinear Greybox Models for Marine Applications
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As marine vessels are becoming increasingly autonomous, having accurate simulation models available is turning into an absolute necessity. This holds both for facilitation of development and for achieving satisfactory model-based control. When accurate ship models are sought, it is necessary to account for nonlinear hydrodynamic effects and to deal with environmental disturbances in a correct way. In this thesis, parameter estimators for nonlinear regression models where the regressors are second-order modulus functions are analyzed. This model class is referred to as second-order modulus models and is often used for greybox identification of marine vessels. The primary focus in the thesis is to find consistent estimators and for this an instrumental variable (IV) method is used.First, it is demonstrated that the accuracy of an IV estimator can be improved by conducting experiments where the input signal has a static offset of sufficient amplitude and the instruments are forced to have zero mean. This two-step procedure is shown to give consistent estimators for second-order modulus models in cases where an off-the-shelf applied IV method does not, in particular when measurement uncertainty is taken into account.Moreover, it is shown that the possibility of obtaining consistent parameter estimators for models of this type depends on how process disturbances enter the system and on the amount of prior knowledge about the disturbances’ probability distributions that is available. In cases where the first-order moments are known, the aforementioned approach gives consistent estimators even when disturbances enter the system before the nonlinearity. In order to obtain consistent estimators in cases where the first-order moments are unknown, a framework for estimating the first and second-order moments alongside the model parameters is suggested. The idea is to describe the environmental disturbances as stationary stochastic processes in an inertial frame and to utilize the fact that their effect on a vessel depends on the vessel’s attitude. It is consequently possible to infer information about the environmental disturbances by over time measuring the orientation of a vessel they are affecting. Furthermore, in cases where the process disturbances are of more general character it is shown that supplementary disturbance measurements can be used for achieving consistency.Different scenarios where consistency can be achieved for instrumental variable estimators of second-order modulus models are demonstrated, both in theory and by simulation examples. Finally, estimation results obtained using data from a full-scale marine vessel are presented.
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| 4. |
- Ljungberg, Fredrik, 1993-
(författare)
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Identification of Nonlinear Marine Systems
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As marine vessels are becoming increasingly automated, having accurate simulation models available is turning into an absolute necessity. This holds both for the facilitation of development and for achieving satisfactory model-based control. Such models can be obtained through system identification, and in this thesis, particular emphasis is given to experiment design and parameter estimation, which constitute two central steps in the system identification process. The analysis is carried out for a special class of nonlinear regression models called second-order modulus models, which is a type of model that is often used for describing nonlinear hydrodynamic effects in greybox identification of ships.First, it is demonstrated that the accuracy of an instrumental variable (iv) estimator can be improved by conducting experiments where the input signal has a static offset of sufficient amplitude and the instruments are forced to have zero mean. This two-step procedure is shown to give consistent estimators for second-order modulus models in cases where an off-the-shelf applied iv method does not, in particular when measurement uncertainty is taken into account. Further, it is shown that the possibility of obtaining consistent parameter estimators for models of this type depends on how the process disturbances enter the system and on the amount of prior knowledge that is available about the disturbances’ probability distributions. In cases where the first-order moments are known, the aforementioned approach gives consistent estimators even when disturbances enter the system before the nonlinearity. To obtain consistent estimators in cases where the first-order moments are unknown, a framework for estimating auxiliary nuisance parameters that depend on the disturbances’ first and second-order moments is suggested. This can be done by describing the process disturbances as stationary stochastic processes in an inertial frame and utilizing the fact that their effect on a vessel depends on the vessel’s attitude.After this, the attention is more clearly focused on experiment design, and a systematic approach for choosing the most informative combination of independent sub-experiments out of a predefined set of candidates is proposed. Further, a technique to account for an upcoming subtraction of the instruments’ mean during the experiment design is suggested, and the consequences of various ways of having the mean subtracted are explored. Additionally, it is shown how the dictionary-based method for finding an excitation signal can be combined with a motion-planning framework to obtain a trajectory that is both informative and spatially feasible.The suggested methods are evaluated in experimental work and show promising results on both simulated and real data, the latter from a full-scale marine vessel as well as a small-scale model ship.
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| 5. |
- Wallén, Johanna, 1979-
(författare)
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Estimation-based iterative learning control
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In many applications industrial robots perform the same motion repeatedly. One way of compensating the repetitive part of the error is by using iterative learning control (ILC). The ILC algorithm makes use of the measured errors and iteratively calculates a correction signal that is applied to the system.The main topic of the thesis is to apply an ILC algorithm to a dynamic system where the controlled variable is not measured. A remedy for handling this difficulty is to use additional sensors in combination with signal processing algorithms to obtain estimates of the controlled variable. A framework for analysis of ILC algorithms is proposed for the situation when an ILC algorithm uses an estimate of the controlled variable. This is a relevant research problem in for example industrial robot applications, where normally only the motor angular positions are measured while the control objective is to follow a desired tool path. Additionally, the dynamic model of the flexible robot structure suffers from uncertainties. The behaviour when a system having these difficulties is controlled by an ILC algorithm using measured variables directly is illustrated experimentally, on both a serial and a parallel robot, and in simulations of a flexible two-mass model. It is shown that the correction of the tool-position error is limited by the accuracy of the robot model.The benefits of estimation-based ILC is illustrated for cases when fusing measurements of the robot motor angular positions with measurements from an additional accelerometer mounted on the robot tool to form a tool-position estimate. Estimation-based ILC is studied in simulations on a flexible two-mass model and on a flexible nonlinear two-link robot model, as well as in experiments on a parallel robot. The results show that it is possible to improve the tool performance when a tool-position estimate is used in the ILC algorithm, compared to when the original measurements available are used directly in the algorithm. Furthermore, the resulting performance relies on the quality of the estimate, as expected.In the last part of the thesis, some implementation aspects of ILC are discussed. Since the ILC algorithm involves filtering of signals over finite-time intervals, often using non-causal filters, it is important that the boundary effects of the filtering operations are appropriately handled when implementing the algorithm. It is illustrated by theoretical analysis and in simulations that the method of implementation can have large influence over stability and convergence properties of the algorithm.
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| 6. |
- Carvalho Bittencourt, André
(författare)
-
On Modeling and Diagnosis of Friction and Wear in Industrial Robots
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrial robots are designed to endure several years of uninterrupted operation and therefore are very reliable. However, no amount of design effort can prevent deterioration over time, and equipments will eventually fail. Its impacts can, nevertheless, be considerably reduced if good maintenance/service practices are performed. The current practice for service of industrial robots is based on preventive and corrective policies, with little consideration about the actual condition of the system. In the current scenario, the serviceability of industrial robots can be greatly improved with the use of condition monitoring/diagnosis methods, allowing for condition-based maintenance (cbm).This thesis addresses the design of condition monitoring methods for industrial robots. The main focus is on the monitoring and diagnosis of excessive degradations caused by wear of the mechanical parts. The wear processes may take several years to be of significance, but can evolve rapidly once they start to appear. An early detection of excessive wear levels can therefore allow for cbm, increasing maintainability and availability. Since wear is related to friction, the basic idea pursued is to analyze the friction behavior to infer about wear.To allow this, an extensive study of friction in robot joints is considered in this work. The effects of joint temperature, load and wear changes to static friction in robot a joint are modeled based on empirical observations. It is found that the effects of load and temperature to friction are comparable to those caused by wear. Joint temperature and load are typically not measured, but will always be present in applications. Therefore, diagnosis solutions must be able to cope with them.Different methods are proposed which allow for robust wear monitoring. First, a wear estimator is suggested. Wear estimates are made possible with the use of a test-cycle and a friction model. Second, a method is defined which considers the repetitive behavior found in many applications of industrial robots. The result of the execution of the same task in different instances of time are compared to provide an estimate of how the system changed over the period. Methods are suggested that consider changes in the distribution of data logged from the robot. It is shown through simulations and experiments that robust wear monitoring is made possible with the proposed methods.
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| 7. |
- Moberg, Stig, 1962-
(författare)
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Modeling and Control of Flexible Manipulators
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrial robot manipulators are general-purpose machines used for industrial automation in order to increase productivity, flexibility, and product quality. Other reasons for using industrial robots are cost saving, and elimination of hazardous and unpleasant work. Robot motion control is a key competence for robot manufacturers, and the current development is focused on increasing the robot performance, reducing the robot cost, improving safety, and introducing new functionalities. Therefore, there is a need to continuously improve the mathematical models and control methods in order to fulfil conflicting requirements, such as increased performance of a weight-reduced robot, with lower mechanical stiffness and more complicated vibration modes. One reason for this development of the robot mechanical structure is of course cost-reduction, but other benefits are also obtained, such as lower environmental impact, lower power consumption, improved dexterity, and higher safety.This thesis deals with different aspects of modeling and control of flexible, i.e., elastic, manipulators. For an accurate description of a modern industrial manipulator, this thesis shows that the traditional flexible joint model, described in literature, is not sufficient. An improved model where the elasticity is described by a number of localized multidimensional spring-damper pairs is therefore proposed. This model is called the extended flexible joint model. The main contributions of this work are the design and analysis of identification methods, and of inverse dynamics control methods, for the extended flexible joint model.The proposed identification method is a frequency-domain non-linear gray-box method, which is evaluated by the identification of a modern six-axes robot manipulator. The identified model gives a good description of the global behavior of this robot.The inverse dynamics problem is discussed, and a solution methodology is proposed. This methodology is based on the solution of a differential algebraic equation (DAE). The inverse dynamics solution is then used for feedforward control of both a simulated manipulator and of a real robot manipulator.The last part of this work concerns feedback control. First, a model-based nonlinear feedback control (feedback linearization) is evaluated and compared to a model-based feedforward control algorithm. Finally, two benchmark problems for robust feedback control of a flexible manipulator are presented and some proposed solutions are analyzed.
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| 8. |
- Zimmermann, Stefanie, 1995-
(författare)
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Data-driven Modeling of Robotic Manipulators – Efficiency Aspects
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robotic manipulators are used for industrial automation and play an important role in manufacturing industry. Increasing performance requirements such as high operating speed and motion accuracy conflict with demands on heavy pay-loads and light-weight design with reduced structural stiffness. The motion control system is a key factor for dealing with these requirements, particularly for increasing the robot performance, improving safety and reducing power consumption. Most industrial robot control systems rely on current and angular position measurements from the motors, meaning that the actual controlled variable, that is the position of the robot’s end-effector, needs to be calculated using a model. Therefore, the mathematical model used for motion control must accurately describe the system’s dynamic behavior. Based on physics equations, the model contains unknown parameters that are usually identified from experimental data. This identification is a challenging problem, since the equations are nonlinear in the parameters, the system is highly resonant and experiments can only be done in closed loop with a controller. Assuming a real robot is available for experiments, data-driven identification is common in order to obtain the most accurate description of the real system’s behavior. The method applied in this thesis estimates the dynamic stiffness parameters by matching the model’s frequency response function to the system’s frequency response, which is obtained from measurements done with the closed-loop robot system. The main focus of this thesis are strategies for increasing the process efficiency such that the time it takes to do the experiments is reduced, while the quality of the model is maintained or improved. Two strategies related to experiment design are presented: First, the number of quasi-static robot configurations for data collection is decreased by choosing the most informative configurations from a set of candidates. Second, less data-demanding methods for estimating the system’s frequency response are considered. The effectiveness of the presented approaches is demonstrated both in simulation and with real data. If no robot is available for experiments, e.g. in the development phase, a model must be built based on specification data of components and other information available to the designer, such as CAD data. This thesis contains a modeling approach that derives a high-fidelity robot model of low order (lumped parameter model with few degrees of freedom) by combining results from test-rig measurements of isolated components with carefully reduced finite element models of the robot’s structural parts.
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| 9. |
- Wallén, Johanna, 1979-
(författare)
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On Kinematic Modelling and Iterative Learning Control of Industrial Robots
- 2008
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Good models of industrial robots are necessary in a variety of applications, such as mechanical design, performance simulation, control, diagnosis, supervision and offline programming. This motivates the need for good modelling tools. In the first part of this thesis the forward kinematic modelling of serial industrial robots is studied. The first steps towards a toolbox are implemented in the Maple programming language.A series of possible applications for the toolbox can be mentioned. One example is to estimate the pose of the robot tool using an extended Kalman filter by means of extra sensors mounted on the robot. The kinematic equations and the relations necessary for the extended Kalman filter can be derived in the modelling tool. Iterative learning control, ILC, using an estimate of the tool position can then improve the robot performance.The second part of the thesis is devoted to ILC, which is a control method that is applicable when the robot performs a repetitive movement starting from the same initial conditions every repetition. The algorithm compensates for repetitive errors by adding a correction signal to the reference. Studies where ILC is applied to a real industrial platform is less common in the literature, which motivates the work in this thesis.A first-order ILC filter with iteration-independent operators derived using a heuristic design approach is used, which results in a non-causal algorithm. A simulation study is made, where a flexible two-mass model is used as a simplified linear model of a single robot joint and the ILC algorithm applied is based on motor-angle measurements only. It is shown that when a model error is introduced in the relation between the arm and motor reference angle, it is not necessary that the error on the arm side is reduced as much as the error on the motor side, or in fact reduced at all.In the experiments the ILC algorithm is applied to a large-size commercial industrial robot, performing a circular motion that is relevant for a laser-cutting application. The same ILC design variables are used for all six motors and the learning is stopped after five iterations, which is motivated in practice by experimental results. Performance on the motor side and the corresponding performance on the arm side, using a laser-measurement system, is studied. Even though the result on the motor side is good, it is no guarantee that the errors on the arm side are decreasing. One has to be very careful when dealing with resonant systems when the controlled variable is not directly measured and included in the algorithm. This indicates that the results on the arm side may be improved when an estimate of, for example, the tool position is used in the ILC algorithm.
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| 10. |
- Wernholt, Erik, 1975-
(författare)
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Multivariable Frequency-Domain Identification of Industrial Robots
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Industrirobotar är idag en väsentlig del i tillverkningsindustrin där de bland annat används för att minska kostnader, öka produktivitet och kvalitet och ersätta människor i farliga eller slitsamma uppgifter. Höga krav på noggrannhet och snabbhet hos robotens rörelser innebär också höga krav på de matematiska modeller som ligger till grund för robotens styrsystem. Modellerna används där för att beskriva det komplicerade sambandet mellan robotarmens rörelser och de motorer som orsakar rörelsen. Tillförlitliga modeller är också nödvändiga för exempelvis mekanisk design, simulering av prestanda, diagnos och övervakning.En trend idag är att bygga lättviktsrobotar, vilket innebär att robotens vikt minskas men att den fortfarande kan hantera en lika tung last. Orsaken till detta är främst att minska kostnaden, men också säkerhetsaspekter spelar in. En lättare robotarm ger dock en vekare struktur där elastiska effekter inte längre kan försummas i modellen om man kräver hög prestanda. De elastiska effekterna beskrivs i den matematiska modellen med hjälp av fjädrar och dämpare.Denna avhandling handlar om hur dessa matematiska modeller kan tas fram genom systemidentifiering, vilket är ett viktigt verktyg där mätningar från robotens rörelser används för att bestämma okända parametrar i modellen. Det som mäts är position och moment hos robotens alla motorer. Identifiering av industrirobotar är ett utmanande problem bland annat eftersom robotens beteende varierar beroende på armens position. Den metod som föreslås i avhandlingen innebär att man först identifierar lokala modeller i ett antal positioner. Var och en av dessa beskriver robotens beteende kring en viss arbetspunkt. Sedan anpassas parametrarna i en global modell, som är giltig för alla positioner, så att den så väl som möjligt beskriver det lokala beteendet i de olika positionerna.I avhandlingen analyseras olika metoder för att ta fram lokala modeller. För att få bra resultat krävs att experimenten är omsorgsfullt utformade. För att minska osäkerheten i den globala modellens identifierade parametrar ingår också valet av optimala positioner för experimenten. Olika metoder för att identifiera parametrarna jämförs i avhandlingen och experimentella resultat visar användbarheten av den föreslagna metoden. Den identifierade robotmodellen ger en bra global beskrivning av robotens beteende.Resultatet av forskningen har även gjorts tillgängligt i ett datorverktyg för att noggrant kunna ta fram lokala modeller och identifiera parametrar i dynamiska robotmodeller.
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