Cesáreo Raimúndez

Brief paper: Passive position error correction in Internet-based teleoperation

During the last two decades, important advances have been made in the field of bilateral teleoperation. Different techniques for performing stable teleoperation in non-ideal conditions have been developed, especially in a passivity framework. Until recently, however, no robust solutions for addressing this problem with variable delays and other drawbacks of packet-switched networks have been developed. The requirement of maintaining passivity in these circumstances degrades performance, due to the loss of energy that it involves. In this paper an arrangement is proposed which is capable of eliminating position errors, while maintaining passivity of an Internet-like channel. The behaviour of this new controller is studied by Lyapunov analysis, compared to previous methods, and validated through numerical simulations.

Adaptive tracking in mobile robots with input-output linearization

This paper proposes a neural network adaptive controller augmentation to a computed torque proportional plus derivative (PD) controller, to guide a nonholonomic mobile robot during trajectory tracking. Path following is done defining a point to follow (look-ahead control) and using input-output linearization. A dynamic inversion neural network controller is responsible for tracking error reduction and adaptation to unmodeled external perturbations. The adaptive controller is implemented through a hidden layer feed-forward neural network and has its weights realtime updated. The stability of the whole system is analyzed using Lyapunov theory, and the control errors are proved to be ultimately bounded. Simulation results are also presented, which demonstrate the good performance of the proposed controller for trajectory tracking under external perturbations.This paper proposes a neural network adaptive controller augmentation to a computed torque proportional plus derivative (PD) controller, to guide a nonholonomic mobile robot during trajectory tracking. Path following is done defining a point to follow (look-ahead control) and using input-output linearization. A dynamic inversion neural network controller is responsible for tracking error reduction and adaptation to unmodeled external perturbations. The adaptive controller is implemented through a hidden layer feed-forward neural network and has its weights realtime updated. The stability of the whole system is analyzed using Lyapunov theory, and the control errors are proved to be ultimately bounded. Simulation results are also presented, which demonstrate the good performance of the proposed controller for trajectory tracking under external perturbations.

1tabilizing an inverted spherical pendulum using a scale quad-rotor

In this paper, we develop a control strategy that allows to balance an inverted pendulum, using a quad-rotor as controller device. The pendulum is supported through a spherical link at the top of the quad-rotor. The feasibility of this experiment is due to the great flexibility of the quad-rotor reaction, having a wide and rich dynamic range. The ultimate aim of this work is that the procedure can be used in scenarios such as those arising in collaborative work environments.

Transporting Hanging Loads Using a Scale Quad-Rotor

In this paper, we develop a control strategy that allows hanging load delivery, using a quad-rotor as controller device. The pendular load is hanging from a rope attached to a quad-rotor. The feasibility of this experiment is due to the great flexibility of the quad-rotor maneuverability, possessing a wide and rich dynamic range. The ultimate aim of this work is to show that the procedure can be used in scenarios such as those arising in collaborative work environments.

Adaptive Tracking in Mobile Robots With Input-Output Linearization

This paper proposes a neural network adaptive controller augmentation to a computed torque proportional plus derivative (PD) controller, to guide a nonholonomic mobile robot during trajectory tracking. Path following is done defining a point to follow (look-ahead control) and using input-output linearization. A dynamic inversion neural network controller is responsible for tracking error reduction and adaptation to unmodeled external perturbations. The adaptive controller is implemented through a hidden layer feed-forward neural network and has its weights realtime updated. The stability of the whole system is analyzed using Lyapunov theory, and the control errors are proved to be ultimately bounded. Simulation results are also presented, which demonstrate the good performance of the proposed controller for trajectory tracking under external perturbations.This paper proposes a neural network adaptive controller augmentation to a computed torque proportional plus derivative (PD) controller, to guide a nonholonomic mobile robot during trajectory tracking. Path following is done defining a point to follow (look-ahead control) and using input-output linearization. A dynamic inversion neural network controller is responsible for tracking error reduction and adaptation to unmodeled external perturbations. The adaptive controller is implemented through a hidden layer feed-forward neural network and has its weights realtime updated. The stability of the whole system is analyzed using Lyapunov theory, and the control errors are proved to be ultimately bounded. Simulation results are also presented, which demonstrate the good performance of the proposed controller for trajectory tracking under external perturbations.

Tracking in scale quad-rotors through adaptive augmentation

This paper illustrates the application of an adaptive flight control architecture to a scale quad-rotor. For autonomous vertical takeoff and landing flight, it is common to separate the control problem into an inner fast loop that controls attitude and an outer slow loop that controls the trajectory tracking. In this paper, we augment a conventional proportional and derivative controller conceived mainly for hovering, with an adaptive element using a real-time tuning single hidden layer neural network in a inner–outer loop combined architecture to account for model inversion error cancelation, issued in the feedback linearization process. The results shown in simulations reveal the superior performance of the augmented controller in tracking maneuvers.

Damping Injection by Reset Control

This paper presents a method for using reset control as an alternative way of obtaining dissipation for a class of port-Hamiltonian systems. One advantage of this approach is the simplicity of its implementation, which requires only a velocity observer. Another advantage is its robustness to modeling uncertainties, since it can be calculated independently of the plant structure. A gantry crane is selected as case study, yielding simulation and experimental results that show the good performance of this technique.

Identification of induction motor parameters using evolutive strategies

Abstract This paper applies Evolutive Strategies (ES) to the problem of parameter identification for induction motors. The lumped model parameteres are estimated using startup transient data. A three-phase balanced induction motor is assumed. Measurements of the stator currents and voltages are required for the identification procedure, but no measurements from the motor shaft are needed. The method generates time histories for the rotor induction. shaft rotation and rotor currents, comparing those results with the measured data. The parameteres are associated to the best fitting between data and simulations.