| 1. |
- Wallén Axehill, Johanna, et al.
(författare)
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Estimation-based ILC applied to a parallel kinematic robot
- 2014
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Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 33, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Estimation-based iterative learning control (ILC) is applied to a parallel kinematic manipulator known as the Gantry-Tau parallel robot. The system represents a control problem where measurements of the controlled variables are not available. The main idea is to use estimates of the controlled variables in the ILC algorithm, and in the paper this approach is evaluated experimentally on the Gantry-Tau robot. The experimental results show that an ILC algorithm using estimates of the tool position gives a considerable improvement of the control performance. The tool position estimate is obtained by fusing measurements of the actuator angular positions with measurements of the tool path acceleration using a complementary filter. (C) 2014 Elsevier Ltd. All rights reserved.
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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. |
- Hendeby, Gustaf, 1978-, et al.
(författare)
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A Platform for Teaching Sensor Fusion Using a Smartphone
- 2017
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Ingår i: International journal of engineering education. - : Tempus Publications. - 0949-149X. ; 33:2B, s. 781-789
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Tidskriftsartikel (refereegranskat)abstract
- A platform for sensor fusion consisting of a standard smartphone equipped with the specially developed Sensor Fusion appis presented. The platform enables real-time streaming of data over WiFi to a computer where signal processingalgorithms, e.g., the Kalman filter, can be developed and executed in a Matlab framework. The platform is an excellenttool for educational purposes and enables learning activities where methods based on advanced theory can be implementedand evaluated at low cost. The article describes the app and a laboratory exercise developed around these new technologicalpossibilities. The laboratory session is part of a course in sensor fusion, a signal processing continuation course focused onmultiple sensor signal applications, where the goal is to give the students hands on experience of the subject. This is done byestimating the orientation of the smartphone, which can be easily visualized and also compared to the built-in filters in thesmartphone. The filter can accept any combination of sensor data from accelerometers, gyroscopes, and magnetometers toexemplify their importance. This way different tunings and tricks of important methods are easily demonstrated andevaluated on-line. The presented framework facilitates this in a way previously impossible.
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| 4. |
- Zimmermann, Stefanie Antonia, et al.
(författare)
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Improving experiment design for frequency-domain identification of industrial robots
- 2022
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Ingår i: IFAC-PapersOnLine. - : ELSEVIER. - 2405-8963. ; , s. 475-480
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Konferensbidrag (refereegranskat)abstract
- For accurate control of industrial robots, a fast and easy-to-use method to estimate the model parameters based on experimental data is desired. This publication is about optimal experiment design in terms of short experiment times and an accurate parameter estimate. An optimization problem that is based on information matrices is solved for finding the optimal robot configurations for the identification experiment. A simulation study shows that the experiment time can be reduced significantly and the accuracy of the parameter estimate can be increased if experiments are conducted only in the optimal manipulator configurations. Furthermore, it is shown that a realistic estimate of the uncertainty in the frequency response function is crucial for successful experiment design.
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| 5. |
- Adib Yaghmaie, Farnaz, et al.
(författare)
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A New Result on Robust Adaptive Dynamic Programming for Uncertain Partially Linear Systems
- 2019
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Ingår i: 2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781728113982 - 9781728113999 ; , s. 7480-7485
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present a new result on robust adaptive dynamic programming for the Linear Quadratic Regulation (LQR) problem, where the linear system is subject to unmatched uncertainty. We assume that the states of the linear system are fully measurable and the matched uncertainty models unmeasurable states with an unspecified dimension. We use the small-gain theorem to give a sufficient condition such that the generated policies in each iteration of on-policy and off-policy routines guarantee robust stability of the overall uncertain system. The sufficient condition can be used to design the weighting matrices in the LQR problem. We use a simulation example to demonstrate the result.
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| 6. |
- Adib Yaghmaie, Farnaz, et al.
(författare)
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Output regulation of unknown linear systems using average cost reinforcement learning
- 2019
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Ingår i: Automatica. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0005-1098 .- 1873-2836. ; 110
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we introduce an optimal average cost learning framework to solve output regulation problem for linear systems with unknown dynamics. Our optimal framework aims to design the controller to achieve output tracking and disturbance rejection while minimizing the average cost. We derive the Hamilton-Jacobi-Bellman (HJB) equation for the optimal average cost problem and develop a reinforcement algorithm to solve it. Our proposed algorithm is an off-policy routine which learns the optimal average cost solution completely model-free. We rigorously analyze the convergence of the proposed algorithm. Compared to previous approaches for optimal tracking controller design, we elevate the need for judicious selection of the discounting factor and the proposed algorithm can be implemented completely model-free. We support our theoretical results with a simulation example. (C) 2019 Elsevier Ltd. All rights reserved.
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| 7. |
- Amirijoo, Mehdi, 1978-, et al.
(författare)
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Enhancing Feedback Control Scheduling Performance by On-line Quantification and Suppression of Measurement Disturbance
- 2005
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Ingår i: Proceedings of the 11th IEEE Real-Time and Embedded Technology and Applications Symposium. - 0769523021 ; , s. 2-11
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Konferensbidrag (refereegranskat)abstract
- In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the system dynamics. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantize the disturbance present in the measured variable as a function of the sampling period and we propose a measurement disturbance suppressive control structure. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability and adaptation.
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| 8. |
- Amirijoo, Mehdi, et al.
(författare)
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Experimental Evaluation of Linear Time-Invariant Models for Feedback Performance Control in Real-Time Systems
- 2007
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Ingår i: Real-time systems. - : Springer Science+Business Media B.V.. - 0922-6443 .- 1573-1383. ; 35:3, s. 209-238
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Tidskriftsartikel (refereegranskat)abstract
- In recent years a new class of soft real-time applications operating in unpredictable environments has emerged. Typical for these applications is that neither the resource requirements nor the arrival rates of service requests are known or available a priori. It has been shown that feedback control is very effective to support the specified performance of dynamic systems that are both resource insufficient and exhibit unpredictable workloads. To efficiently use feedback control scheduling it is necessary to have a model that adequately describes the behavior of the system. In this paper we experimentally evaluate the accuracy of four linear time-invariant models used in the design of feedback controllers. We introduce a model (DYN) that captures additional system dynamics, which a previously published model (STA) fails to include. The accuracy of the models are evaluated by validating the models with regard to measured data from the controlled system and through a set of experiments where we evaluate the performance of a set of feedback control schedulers tuned using these models. From our evaluations we conclude that second order models (e.g., DYN) are more accurate than first order models (e.g. STA). Further we show that controllers tuned using second order models perform better than controllers tuned using first order models.
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| 9. |
- Amirijoo, Mehdi, et al.
(författare)
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Quantifying and Suppressing the Measurement Disturbance in Feedback Controlled Real-Time Systems
- 2008
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Ingår i: Real-time systems. - : Springer. - 0922-6443 .- 1573-1383. ; 40:1, s. 44-76
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Tidskriftsartikel (refereegranskat)abstract
- In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the dynamics of the system. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantify the disturbance present in the measured variable as a function of the data set size and the sampling period, and we propose a feedback control structure that suppresses the measurement disturbance. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability.
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| 10. |
- Andersson, Magnus, et al.
(författare)
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A Simulation and Animation Tool for Studying Multivariable Control
- 2002
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Ingår i: Proceedings of the 15th IFAC World Congress. - 9783902661746 ; , s. 1432-1432
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Konferensbidrag (refereegranskat)abstract
- A simulation and animation tool for education in multivariable control is presented. The purpose of the tool is to support studies of various aspects of multivariable dynamical systems and design of multivariable feedback control systems. Different ways to use this kind of tool in control education are also presented and discussed.
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