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Constrained Output Iterative Learning Control

Yovchev Kaloyan, Delchev Kamen, Krastev Evgeniy

Archives of Control Sciences

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2020

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Vol. 30, No. 1

157--176

EN

Iterative Learning Control (ILC) is a well-known method for control of systems performing repetitive jobs with high precision. This paper presents Constrained Output ILC (COILC) for non-linear state space constrained systems. In the existing literature there is no general solution for applying ILC to such systems. This novel method is based on the Bounded Error Algorithm (BEA) and resolves the transient growth error problem, which is a major obstacle in applying ILC to non-linear systems. Another advantage of COILC is that this method can be applied to constrained output systems. Unlike other ILC methods the COILC method employs an algorithm that stops the iteration before the occurrence of a violation in any of the state space constraints. This way COILC resolves both the hard constraints in the non-linear state space and the transient growth problem. The convergence of the proposed numerical procedure is proved in this paper. The performance of the method is evaluated through a computer simulation and the obtained results are compared to the BEA method for controlling non-linear systems. The numerical experiments demonstrate that COILC is more computationally effective and provides better overall performance. The robustness and convergence of the method make it suitable for solving constrained state space problems of non-linear systems in robotics.

2

Iterative learning control with sampled-data feedback for robot manipulators

Delchev K., Boiadjiev G., Kawasaki H., Mouri T.

Archives of Control Sciences

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2014

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Vol. 24, no. 3

299--319

EN

This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached.

3

Stiffness control of robot manipulators in the operational space using fuzzy mapping of dynamic functions

Bertol D. W., Barasuol V., Martins N. A., De Pieri E. R.

Control and Cybernetics

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2013

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Vol. 42, no. 3

639--661

EN

In this paper a stiffness control strategy based on the fuzzy mapped nonlinear terms of the robot manipulator dynamic model is proposed. The proposed stiffness controller is evaluated on a research robot manipulator performing a task in the operational space. Tests attempted to achieve fast motion with reasonable accuracy associated with lower computational load compared to the non-fuzzy approach. The stability analysis is presented to conclude about the mapping error influence and to obtain precondition criteria for the gains adjustment to face the trajectory tracking problem. Simulation results that supported the implementation are presented, followed by experiments and results obtained. These tests are conducted on a robot manipulator with SCARA configuration to illustrate the feasibility of this strategy.

4

Neural network parallel force/position control of robot manipulators under environment and robot dynamics uncertainties

Ferguene F., Achour N., Toumi R.

Archives of Control Sciences

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2009

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Vol. 19, no. 1

93-121

EN

The performance of a parallel force/position controller for robot force tracking is affected by the uncertainties in both the robot dynamics and the environment stiffness. This paper aims to improve the controller's robustness by applying the neural network (NN) technique to compensate for the robot dynamics at the input trajectory level and adaptive feed-forward compensation to cope with variations in the contact environment. A NN control technique is applied to a conventional PID force/position parallel control scheme which is composed of a PD action on position loop and a proposed adaptive I (integral) action on the force loop, which allows a complete use of available sensor measurements by operating the control action in a full dimensional space without using selection matrices. Simulation results for a three degrees-of-freedom robot show that highly robust position/force tracking can be achieved in the presence of a full dynamic robot and large environment stiffness uncertainties.

5

3-D objects motion estimation based on Kalman Filter and BSP Tree Models for Robot Stereo Vision

Lippiello V., Siciliano B., Villani L.

Archives of Control Sciences

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2002

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Vol. 12, no. 1/2

71-88

EN

The problem of the real time estimation of the position and orientation of moving objects for position-based visual servoing control of robotic systems is considered in this paper. A computationally efficient algorithm is proposed based on Kalman filtering of the visual measurements of the position of suitable feature points selected on the target objects. The efficiency of the algorithm is improved by adopting a pre-selection technique of the feature points, based on Binary Space Partitioning (BSP) tree geometric models of the target objects, which takes advantage of the Kalman folter prediction capability. Computer simulations are presented to test the performance of the estimation algorithm in the presence of noise, different types of lens geometric distortion, quantization and calibration errors.