| 1. |
- 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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| 2. |
- 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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| 3. |
- Axelsson, Patrik, 1985-, et al.
(författare)
-
H∞-Controller Design Methods Applied to One Joint of a Flexible Industrial Manipulator
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Control of a flexible joint of an industrial manipulator using H∞ design methods is presented. The considered design methods are i) mixed-H∞ design, and ii) H∞ loop shaping design. Two different controller configurations are examined: one uses only the actuator position, while the other uses the actuator position and the acceleration of end-effector. The four resulting controllers are compared to a standard PID controller where only the actuator position is measured. The choices of the weighting functions are discussed in details. For the loop shaping design method, the acceleration measurement is required to improve the performance compared to the PID controller. For the mixed-H∞ method it is enough to have only the actuator position to get an improved performance. Model order reduction of the controllers is briefly discussed, which is important for implementation of the controllers in the robot control system.
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| 4. |
- Axelsson, Patrik, 1985-, et al.
(författare)
-
H∞ Synthesis Method for Control of Non-linear Flexible Joint Models
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- An H∞ synthesis method for control of a flexible joint, with non-linear spring characteristic, is proposed. The first step of the synthesis method is to extend the joint model with an uncertainty description of the stiffness parameter. In the second step, a non-linear optimisation problem, based on nominal performance and robust stability requirements, has to be solved. Using the Lyapunov shaping paradigm and a change of variables, the non-linear optimisation problem can be rewritten as a convex, yet conservative, LMI problem. The method is motivated by the assumption that the joint operates in a specific stiffness region of the non-linear spring most of the time, hence the performance requirements are only valid in that region. However, the controller must stabilise the system in all stiffness regions. The method is validated in simulations on a non-linear flexible joint model originating from an industrial robot.
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