| 1. |
- Axelsson, Patrik, 1985-
(författare)
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Sensor Fusion and Control Applied to Industrial Manipulators
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- One of the main tasks for an industrial robot is to move the end-effector in a predefined path with a specified velocity and acceleration. Different applications have different requirements of the performance. For some applications it is essential that the tracking error is extremely small, whereas other applications require a time optimal tracking. Independent of the application, the controller is a crucial part of the robot system. The most common controller configuration uses only measurements of the motor angular positions and velocities, instead of the position and velocity of the end-effector. The development of new cost optimised robots has introduced unwanted flexibilities in the joints and the links. The consequence is that it is no longer possible to get the desired performance and robustness by only measuring the motor angular positions. This thesis investigates if it is possible to estimate the end-effector position using Bayesian estimation methods for state estimation, here represented by the extended Kalman filter and the particle filter. The arm-side information is provided by an accelerometer mounted at the end-effector. The measurements consist of the motor angular positions and the acceleration of the end-effector. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The methods are also verified in experiments on an ABB IRB4600 robot, where the dynamic performance of the position for the end-effector is significantly improved. There is no significant difference in performance between the different methods. Instead, execution time, model complexities and implementation issues have to be considered when choosing the method. The estimation performance depends strongly on the tuning of the filters and the accuracy of the models that are used. Therefore, a method for estimating the process noise covariance matrix is proposed. Moreover, sampling methods are analysed and a low-complexity analytical solution for the continuous-time update in the Kalman filter, that does not involve oversampling, is proposed. The thesis also investigates two types of control problems. First, the norm-optimal iterative learning control (ILC) algorithm for linear systems is extended to an estimation-based norm-optimal ILC algorithm where the controlled variables are not directly available as measurements. The algorithm can also be applied to non-linear systems. The objective function in the optimisation problem is modified to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. Second, H∞ controllers are designed and analysed on a linear four-mass flexible joint model. It is shown that the control performance can be increased, without adding new measurements, compared to previous controllers. Measuring the end-effector acceleration increases the control performance even more. A non-linear model has to be used to describe the behaviour of a real flexible joint. An H∞-synthesis method for control of a flexible joint, with non-linear spring characteristic, is therefore proposed.
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| 2. |
- 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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| 3. |
- Haghshenas, Hamed, 1990-
(författare)
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Time-Optimal Cooperative Path Tracking for Multi-Robot Systems
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robotic systems are nowadays the key technology in a wide variety of applications. The increasing demand for performance of robotic systems is often met by employing a team of cooperating robots for a specific task.When the task carried out by the robots involves manipulation of an object, the multi-robot system is said to perform a cooperative manipulation task.Cooperative manipulation is an important capability for extending the domain of robotic applications.This thesis studies the time-optimal path tracking problem for a cooperative manipulation scenario where an object is rigidly grasped by multiple manipulators. The goal is to move the object along a predefined geometric path in minimum time while satisfying the imposed constraints on the motion. First, it is shown that the time-optimal path tracking problem for cooperative manipulators can be cast as a convex optimization problem. A fundamental property of convex optimization problems is that any locally optimal solution is also a globally optimal one. Furthermore, by recognizing and formulating a problem as a convex optimization problem, it can be solved very reliably and efficiently using interior-point or other methods for convex optimization.These results are presented in two separate studies. In the first one which is a preliminary study, the manipulation setup is a particular setup comprised of two planar manipulators and a bar. Furthermore, the load distribution among the manipulators is considered to be equal. The second study extends the results in the preliminary study to a general scenario with $N$ generic manipulators and an object with a desired orientation during the motion. Here, the load distribution among the manipulators is determined via a generic pseudo-inverse of the grasp matrix that can be chosen by the user.The freedom in the choice of the pseudo-inverse allows to consider different load distributions which can be exploited to account for the potential differences in the capabilities of the manipulators.The second part of this thesis is devoted to finding load distributions that are free of internal forces. A drawback of using multiple manipulators in a cooperative manipulation task is that internal forces can be introduced.Internal forces are forces exerted by the end-effectors at the grasping points that do not contribute to the motion of the manipulated object. While a certain amount of such forces can be useful in some cases, in general they must be avoided to prevent object damage and unnecessary effort of the manipulators.This thesis proposes a new approach to obtain internal force-free load distributions.The proposed approach results in a new pseudo-inverse of the grasp matrix parameterized by coefficients that have the meaning of the inertial parameters of some parts of the object. The freedom in the choice of the parameters of the pseudo-inverse allows to assign different loads to the manipulators. This can be exploited to account for the differences in the power capabilities of the manipulators.The results are further explored for scenarios where the object is three-dimensional and convex and has uniform mass density. Finally, the proposed pseudo-inverse is combined with the results in the first part of the thesis to solve the problem of time-optimal cooperative path tracking subject to zero internal forces during the motion.
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| 4. |
- 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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| 5. |
- Hedberg, Erik, 1986-
(författare)
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Control, Models and Industrial Manipulators
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The two topics at the heart of this thesis are how to improve control of industrial manipulators and how to reason about the role of models in automatic control.On industrial manipulators, two case studies are presented. The first investigates estimation with inertial sensors, and the second compares control by feedback linearization to control based on gain-scheduling.The contributions on the second topic illustrate the close connection between control and estimation in different ways. A conceptual model of control is introduced, which can be used to emphasize the role of models as well as the human aspect of control engineering. Some observations are made regarding block-diagram reformulations that illustrate the relation between models, control and inversion. Finally, a suggestion for how the internal model principle, internal model control, disturbance observers and Youla-Kucera parametrization can be introduced in a unified way is presented.
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| 6. |
- Sundström, Christofer
(författare)
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Vehicle Level Diagnosis for Hybrid Powertrains
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There are possibilities to reduce the fuel consumption in trucks using hybrid technology. New components are added when hybridizing a vehicle, and these need to be monitored due to safety and legislative demands. Diagnosis aspects due to hybridization of the powertrain are investigated using a model of a long haulage truck. Such aspects are for example that there are more mode switches in the hybrid powertrain compared to a conventional vehicle, and there is a freedom in choosing operating points of the components in the powertrain via the energy management and still fulfill the torque request of the driver.To investigate the influence of energy management and sensor configuration on the performance of the diagnosis system, three diagnosis systems on vehicle level are designed and implemented. The systems are based on different sensor configurations; one with a fairly typical sensor configuration, one with the same number of sensors but in model sense placed more closely to the components to be monitored, and one with the minimal number of sensors to ideally achieve full fault isolability. It is found that there is a connection between the design of the energy management and the diagnosis systems, and that this connection is of special relevance when the model used in the diagnosis is valid only for some operating modes of the powertrain.In consistency based diagnosis it is investigated if there exists a solution to a set of equations with analytical redundancy, where the redundancy is obtained using measurements. The selection of sets of equations to be included in the diagnosis and how and in what order the unknown variables are to be computed affect the diagnosis performance. A simplified vehicle model is used to exemplify how an algebraic loop can be avoided for one computational sequence of the unknowns, but can not be avoided for a different computational sequence given the same overdetermined set of model equations. A vehicle level diagnosis system is designed using a systematic method to obtain unique residuals and that no signal is differentiated. The performance of the designed system is evaluated in a simulation study, and compared to a diagnosis system based on the same sets of equations, but where the residual generators are selected ad hoc. The results of the comparison are positive, which reinforces the idea of considering the properties of the residual generators in a systematic way.A diagnosis system using a map based model of the electric machine is designed. The benefits of using map based models are that it is easy to construct the models if measurements are available, and that such models in general are accurate. As a consequence of the structure of the model, full fault isolability is not possible to achieve using only the model for fault free behavior of the machine. To achieve full fault isolability, fault models are added to the diagnosis system using a model with a different model structure. The system isolates the faults, even though the induced faults are small in the simulation study, and the size of the faults are accurately estimated using observers.
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| 7. |
- 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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| 8. |
- Wullt, Bernhard, et al.
(författare)
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Neural motion planning in dynamic environments
- 2023
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Ingår i: IFAC-PapersOnLine. - : Elsevier. ; , s. 10126-10131
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Konferensbidrag (refereegranskat)abstract
- Motion planning is a mature field within robotics with many successful solutions. Despite this, current state-of-the-art planners are still computationally heavy. To address this, recent work have employed ideas from machine learning, which have drastically reduced the computational cost once a planner has been trained. It is mainly static environments that have been studied in this way. We continue along the same research direction but expand the problem to include dynamic environments, hence increasing the difficulty of the problem. Analogously to previous work, we use imitation learning, where a planning policy is learnt from an expert planner in a supervised manner. Our main contribution is a planner mimicking an expert that considers the future movement of all the obstacles in the environment, which is key in order to learn a successful policy in dynamic environments. We illustrate this by evaluating our approach in a dynamic environment and by comparing our planner with a conventional planner that re-plans at every iteration, which is a common approach in dynamic motion planning. We observe that our approach yields a higher success rate, while also taking less time and accumulating less distance to reach the goal.
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| 9. |
- Östring, Måns
(författare)
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Identification, Diagnosis, and Control of a Flexible Robot Arm
- 2002
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The most important factors in manufacturing are quality, cost, and productivity. The trend is towards lighter robots with increased mechanical flexibilities, and therefore there is a need to include the flexibilities in the robot models to obtain good performance of the robot. The core theme in this thesis is modeling and identification of the physical parameters of an ABB IRB 1400 industrial robot. The approximation made is that the robot arm can be described using a finite number of masses connected by springs and dampers. It has been found that a three-mass model gives a reasonably good description of the robot when moving around axis one. The physical parameters of this model are identified using off-line and on-line algorithms. The algorithms are based on prediction error methods. For the on-line identication the Matlab System Identifiation Toolbox is used. For the on-line identication the algorithm used is a modified version of a recursive prediction error method to cope with continuous time models. The models are then used in diagnosis and control. Two ways of doing diagnosis using on-line identification are investigated. Estimating some of the physical parameters of the robot arm recursively makes it possible to monitor important aspects of the system such as friction and load. LQG control of the flexible robot arm is also studied with the aim of good disturbance rejection. Aspects that have been studied are unstable regulators and the use of accelerometers.
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