| 1. |
- Axelsson, Patrik, 1985-
(författare)
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A Simulation Study on the Arm Estimation of a Joint Flexible 2 DOF Robot Arm
- 2009
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The main task for an industrial robot is to move the tool into specific positions. It is therefore necessary to have an accurate knowledge about the tool position. This report desrcibes a simulation study where an accelerometer attached to the robot tool is used. The acceleration and measured motor angles are used with an Extended Kalman Filter (EKF) to estimate the tool position. The work has been focused on a robot with two degrees of freedom. Simulations have been performed with different kind of errors and on different paths. The EKF uses covariance matrices of the process noise and measurement noise which are unknown. An optimization problem has therefore been proposed and solved to get covariance matrices that give good estimations.
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| 2. |
- Axelsson, Patrik, 1985-, et al.
(författare)
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Bayesian Methods for Estimating Tool Position of an Industrial Manipulator
- 2012
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Ingår i: Proceedings of Reglermöte 2012.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- State estimation of a flexible industrial manipulator is presented using experimental data. The problem is formulated in a Bayesian framework where the extended Kalman filter and particle filter are used. The filters use the joint positions on the motor side of the gearboxes as well as the acceleration at the end-effector as measurements and estimates the corresponding joint angles on the arm side of the gearboxes. The techniques are verified on a state of the art industrial robot, and it is shown that the use of the acceleration at the end-effector improves the estimates significantly.
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| 3. |
- Axelsson, Patrik, 1985-, et al.
(författare)
-
Bayesian State Estimation of a Flexible Industrial Robot
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. 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 technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.
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| 4. |
- Axelsson, Patrik, 1985-, et al.
(författare)
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Bayesian State Estimation of a Flexible Industrial Robot
- 2012
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Ingår i: Control Engineering Practice. - : Elsevier. - 0967-0661 .- 1873-6939. ; 20:11, s. 1220-1228
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Tidskriftsartikel (refereegranskat)abstract
- A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. 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 technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.
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| 5. |
- Axelsson, Patrik, 1985-, et al.
(författare)
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Controllability Aspects for Iterative Learning Control
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper discusses the aspects of controllability in the iteration domain for systems that are controlled using iterative learning control (ILC). The focus is on controllability for a proposed state space model in the iteration domain and it relates to an assumption often used to prove convergence of ILC algorithms. It is shown that instead of investigating controllability it is more suitable to use the concept of target path controllability (TPC), where it is investigated if a system can follow a trajectory instead of the ability to control the system to an arbitrary point in the state space. Finally, a simulation study is performed to show how the ILC algorithm can be designed using the LQ-method, if the state space model in the iteration domain is output controllable. The LQ-method is compared to the standard norm-optimal ILC algorithm, where it is shown that the control error can be reduced significantly using the LQ-method compared to the norm-optimal approach.
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| 6. |
- Axelsson, Patrik, 1985-, et al.
(författare)
-
Discrete-time Solutions to the Continuous-time Differential Lyapunov Equation With Applications to Kalman Filtering
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Prediction and filtering of continuous-time stochastic processes require a solver of a continuous-time differential Lyapunov equation (CDLE). Even though this can be recast into an ordinary differential equation (ODE), where standard solvers can be applied, the dominating approach in Kalman filter applications is to discretize the system and then apply the discrete-time difference Lyapunov equation (DDLE). To avoid problems with stability and poor accuracy, oversampling is often used. This contribution analyzes over-sampling strategies, and proposes a low-complexity analytical solution that does not involve oversampling. The results are illustrated on Kalman filtering problems in both linear and nonlinear systems.
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| 7. |
- Axelsson, Patrik, 1985-, et al.
(författare)
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Discrete-time Solutions to the Continuous-time Differential Lyapunov Equation With Applications to Kalman Filtering
- 2015
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE Press. - 0018-9286 .- 1558-2523. ; 60:3, s. 632-643
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Tidskriftsartikel (refereegranskat)abstract
- Prediction and filtering of continuous-time stochastic processes often require a solver of a continuous-time differential Lyapunov equation (CDLE), for example the time update in the Kalman filter. Even though this can be recast into an ordinary differential equation (ODE), where standard solvers can be applied, the dominating approach in Kalman filter applications is to discretize the system and then apply the discrete-time difference Lyapunov equation (DDLE). To avoid problems with stability and poor accuracy, oversampling is often used. This contribution analyzes over-sampling strategies, and proposes a novel low-complexity analytical solution that does not involve oversampling. The results are illustrated on Kalman filtering problems in both linear and nonlinear systems.
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| 8. |
- Axelsson, Patrik, 1985-, et al.
(författare)
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Estimation-based ILC using Particle Filter with Application to Industrial Manipulators
- 2013
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Ingår i: Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). ; , s. 1740-1745
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Konferensbidrag (refereegranskat)abstract
- An estimation-based iterative learning control (ILC) algorithm is applied to a realistic industrial manipulator model. By measuring the acceleration of the end-effector, the arm angular position accuracy is improved when the measurements are fused with motor angle observations. The estimation problem is formulated in a Bayesian estimation framework where three solutions are proposed: one using the extended Kalman filter (EKF), one using the unscented Kalman filter (UKF), and one using the particle filter (PF). The estimates are used in an ILC method to improve the accuracy for following a given reference trajectory. Since the ILC algorithm is repetitive no computational restrictions on the methods apply explicitly. In an extensive Monte Carlo simulation study it is shown that the PF method outperforms the other methods and that the ILC control law is substantially improved using the PF estimate.
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| 9. |
- Axelsson, Patrik, 1985-, et al.
(författare)
-
Estimation-based Norm-optimal Iterative Learning Control
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The iterative learning control (ILC) method improvesperformance of systems that repeat the same task several times. In this paper the standard norm-optimal ILC control law for linear systems is extended to an estimation-based ILC algorithm where the controlled variables are not directly available as measurements. The proposed ILC algorithm is proven to be stable and gives monotonic convergence of the error. The estimation-based part of the algorithm uses Bayesian estimation techniques such as the Kalman filter. 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. It is further shown that for linear time-invariant systems the ILC design is independent of the estimation method. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ILC algorithm. It is also discussed how the Kullback-Leibler divergence can be used if linearisation cannot be performed. Finally, the proposed solution for non-linear systems is applied and verified in a simulation study with a simplified model of an industrial manipulator system.
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| 10. |
- Axelsson, Patrik, 1985-, et al.
(författare)
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Estimation-based Norm-optimal Iterative Learning Control
- 2014
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Ingår i: Systems & control letters (Print). - : Elsevier. - 0167-6911 .- 1872-7956. ; 73, s. 76-80
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Tidskriftsartikel (refereegranskat)abstract
- 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. A separation lemma is presented, stating that if a stationary Kalman filter is used for linear time-invariant systems then the ilc design is independent of the dynamics in the Kalman filter. Furthermore, the objective function in the optimisation problem is modified to incorporate the full probability density function of the error. Utilising the Kullback–Leibler divergence leads to an automatic and intuitive way of tuning the ilc algorithm. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ilc algorithm. Stability and convergence properties for the proposed scheme are also derived.
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