V. Etxebarria

Brief Robust neural control for robotic manipulators

A robust neural control scheme for mechanical manipulators is presented. The design basically consists of an adaptive neural controller which implements a feedback linearization control law for a generic manipulator with unknown parameters, and a sliding-mode control which robustifies the design and compensates for the neural approximation errors. It is proved that the resulting closed-loop system is stable and that the trajectory-tracking control objective is achieved. Some simulation results are also provided to evaluate the design.

Energy-based approach to sliding composite adaptive control for rigid robots with finite error convergence time

In this paper a terminal sliding-mode adaptive control scheme for robotic manipulators designed following an energybased approach is presented. The control comprises two basic terms: a composite adaptive term which implements a feedback law for compensating the modelled dynamics and a non-linear sliding-mode term for overcoming the unmodelled dynamics and perturbations. The resulting closed-loop system is proved to be stable and it is also shown that the trajectory-tracking error converges to zero in finite time. Moreover, an upper bound of this error convergence time is calculated. Finally, the design is evaluated by means of simulations.

Neural network-based micropositioning control of smart shape memory alloy actuators

Shape memory alloys (SMA) are a special kind of smart materials whose dimensions change because of a temperature-dependent structural phase transition. This property can be used to generate motion or force in electromechanical devices and micromachines. However, their highly nonlinear hysteretical stimulus-response characteristic fundamentally limits the accuracy of SMA actuators. The purpose of this work is to design nonlinear control methods suitable for SMA-based positioning applications. To account for the hysteresis effects, inverse hysteresis models are inserted in proportional integral with antiwindup control loops. The inverse hysteresis models are obtained both using a linear phase shift approximation and by training neural networks using experimental data. It is found that neural networks are excellent tools perfectly capable of learning the hysteresis effects. Several control strategies, with and without compensation, are experimented on a laboratory SMA actuator and it is found that neural networks successfully improve the closed-loop response, leading to position accuracies close to the micron.

Control of a Lightweight Flexible Robotic Arm Using Sliding Modes

This paper presents a robust control scheme for flexible link robotic manipulators, which is based on considering the flexible mechanical structure as a system with slow (rigid) and fast (flexible) modes that can be controlled separately. The rigid dynamics is controlled by means of a robust sliding-mode approach with well-established stability properties while an LQR optimal design is adopted for the flexible dynamics. Experimental results show that this composite approach achieves good closed loop tracking properties both for the rigid and the flexible dynamics.

Adaptive control based on special compensation methods for time-varying systems subject to bounded disturbances

An adaptive controller based on some compensation methods that preserve stability for fast time-varying plants in the presence of bounded output disturbances and unmodelled dynamics is presented. No minimum phase assumption is required by the adaptive scheme to guarantee closed-loop stability, although the unstable plant zeros must be transmitted to the closed-loop system to avoid unstable cancellations. The unknown plant parameters are estimated by a normalized algorithm with dead zone. The design is completed with a modified control law which adds an internal impulse to the system whenever the controllability of the estimated model is lost. This strategy confers a self-excitation capability to the system, so that it makes unnecessary the presence of external persistently exciting signals. As a result, the estimated models are sufficiently controllable by the underlying control law based on the special compensation methods, and global stability is ensured. The performance of the given scheme is evaluated...

ROBUST ADAPTIVE CONTROL FOR ROBOT MANIPULATORS WITH UNMODELLED DYNAMICS

In this paper, a robust adaptive control scheme for mechanical manipulators is presented. The design basically consists, on the one hand, of an adaptive controller that implements a feedback linearization control law which compensates the modeled dynamics, and, on the other hand, of an adaptive sliding-mode control law that overcomes the unmodelled dynamics and noise. It is also proved that the resulting closed-loop system is stable and that the trajectory tracking control objective is asymptotically achieved. Finally, some simulation results are also provided to evaluate the design.

Robust sliding composite adaptive control for mechanical manipulators with finite error convergence time

In this paper a new robust adaptive control scheme for mechanical manipulators is presented. The design basically consists of, on the one hand, a composite adaptive controller which implements a feedback law that compensates the modelled dynamics and, on the other hand, a nonlinear sliding mode control law that overcomes the unmodelled dynamics and noise. It is proved that the resulting closed-loop system is stable and that the trajectory-tracking error converges to zero in finite time. Moreover, an upper bound of this error convergence time is calculated. Finally, the design is evaluated by means of simulations.

Ferromagnetic shape memory alloys for positioning with nanometric resolution

Ferromagnetic shape memory alloys are promising active elements for actuators. Our work centered on achieving and maintaining an intermediate fixed deformation so that they can be used as precision positioning actuators. For this purpose, a custom actuator was built using a single crystal of NiMnGa. The results show that these alloys can be controlled within less than 5 nm for both precision and accuracy, a result comparable to piezoelectric ceramics. Interestingly, the defect structure plays a fundamental role in achieving such performance. The stochastically distributed defects determine a progressive diminution of the magnetic field strength required to achieve the control.

ESS-Bilbao light-ion linear accelerator and neutron source: design and applications

The baseline design for the ESS-Bilbao light-ion linear accelerator and neutron source has been completed and the normal conducting section of the linac is at present under construction. The machine has been designed to be compliant with ESS specifications following the international guidelines of such project as described in Ref. [1]. The new accelerator facility in Bilbao will serve as a base for support of activities on accelerator physics carried out in Spain and southern Europe in the frame of different ongoing international collaborations. Also, a number of applications have been envisaged in the new Bilbao facility for the outgoing light ion beams as well as from fast neutrons produced by low-energy neutron-capture targets, which are briefly described.

COMBINED PD-H∞ APPROACH TO CONTROL OF FLEXIBLE LINK MANIPULATORS USING ONLY DIRECTLY MEASURABLE VARIABLES

A control scheme for flexible link manipulators, which makes use only of directly measurable variables and thus is well suited for practical implementations, is presented. The scheme is based on a combined PD-H X design that takes into account the actual coupling between the rigid and flexible dynamics of these kinds of manipulators and needs feedback only from directly available sensor readings. This contrasts with other approaches reported in the literature that require full-state feedback or even feedback of the rigid unperturbed variables, which simply cannot be measured. It is shown that the proposed design, in spite of its ease of implementation, maintains good closed-loop tracking properties.