Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

A time delay controller for magnetic bearings

The control of systems with unknown dynamics and unpredictable disturbances has raised some challenging problems. This is particularly important when high system performance needs to be guaranteed at all times. Recently, the Time Delay Control has been suggested as an alternative control scheme. The proposed control system does not require an explicit plant model nor does it depend on the estimation of specific plant parameters. Rather, it combines adaptation with past observations to directly estimate the effect of the plant dynamics. A control law is formulated for a class of dynamic systems and a sufficient condition is presented for control systems stability. The derivation is based on the bounded input-bounded output stability approach using L sub infinity function norms. The control scheme is implemented on a five degrees of freedom high speed and high precision magnetic bearing. The control performance is evaluated using step responses, frequency responses, and disturbance rejection properties. The experimental data show an excellent control performance despite the system complexity

Model-Based Iterative Learning Control Applied to an Industrial Robot with Elasticity

In this paper model-based Iterative Learning Control (ILC) is applied to improve the tracking accuracy of an industrial robot with elasticity. The ILC algorithm iteratively updates the reference trajectory for the robot such that the predicted tracking error in the next iteration is minimised. The tracking error is predicted by a model of the closed-loop dynamics of the robot. The model includes the servo resonance frequency, the first resonance frequency caused by elasticity in the mechanism and the variation of both frequencies along the trajectory. Experimental results show that the tracking error of the robot can be reduced, even at frequencies beyond the first elastic resonance frequency

Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems

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Learning control of robot manipulators in the presence of additive disturbances

In this paper, a learning controller for robot manipulators is developed. The controller is proven to yield in a semi-global asymptotic result in the presence of additive input and output disturbances. Lyapunovbased techniques are used to guarantee that the tracking error is asymptotically driven to zero. Numerical simulation results are presented to demonstrate the viability of the proposed learning controller