Sistemas de Control Automático. Práctica 1 (curso 2010-2011)

Práctica 1 de la asignatura sistemas de control automático. Ajuste y sintonización de controladores.

Sistemas de Control Automático. Práctica 2 (curso 2010-2011)

Práctica 2 de sistemas de control automático. Control de velocidad mediante el autómata CP1L y el variador MX2 de Omron.

Sistemas de Control Automático. Práctica 3 (curso 2010-2011)

Práctica 3 de sistemas de control automático. Control de un PLC mediante tramas Host-Link generadas por un PC.

Sistemas de Control Automático. Tema 1: Introducción (curso 2010-2011)

Presentación tema 1 de sistemas de control automático. Introducción.

Sistemas de Control Automático. Tema 2: Análisis de sistemas (curso 2010-2011)

Presentación tema 2 de sistemas de control automático. Análisis de sistemas.

Sistemas de Control Automático. Tema 3: Espacio de estados (curso 2010-2011)

Presentación tema 2 de sistemas de control automático. Espacio de estados.

Sistemas de Control Automático. Tema 4: Implantación de sistemas de control (curso 2010-2011)

Presentación tema 4 de sistemas de control automático. Implantación de sistemas de control.

Sistemas de Control Automático. Apéndice. Control por computador (curso 2010-2011)

Presentación sistemas de control por computador.

Sistemas de Control Automático. Ejercicios resueltos (curso 2010-2011)

Ejercicios resueltos para la asignatura sistemas de control automático. Control de un motor de corriente continua.

Sistemas de Control Automático. Identificación experimental de sistemas (curso 2010-2011)

Métodos para la obtención del modelo de un sistema de forma experimental.

Sistemas de Control Automático. Guía de la asignatura (curso 2010-2011)

Sistemas de control automático. Guía de la asignatura.

Control framework for dexterous manipulation using dynamic visual servoing and tactile sensors’ feedback

Tactile sensors play an important role in robotics manipulation to perform dexterous and complex tasks. This paper presents a novel control framework to perform dexterous manipulation with multi-fingered robotic hands using feedback data from tactile and visual sensors. This control framework permits the definition of new visual controllers which allow the path tracking of the object motion taking into account both the dynamics model of the robot hand and the grasping force of the fingertips under a hybrid control scheme. In addition, the proposed general method employs optimal control to obtain the desired behaviour in the joint space of the fingers based on an indicated cost function which determines how the control effort is distributed over the joints of the robotic hand. Finally, authors show experimental verifications on a real robotic manipulation system for some of the controllers derived from the control framework.

A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

The current trend in the evolution of sensor systems seeks ways to provide more accuracy and resolution, while at the same time decreasing the size and power consumption. The use of Field Programmable Gate Arrays (FPGAs) provides specific reprogrammable hardware technology that can be properly exploited to obtain a reconfigurable sensor system. This adaptation capability enables the implementation of complex applications using the partial reconfigurability at a very low-power consumption. For highly demanding tasks FPGAs have been favored due to the high efficiency provided by their architectural flexibility (parallelism, on-chip memory, etc.), reconfigurability and superb performance in the development of algorithms. FPGAs have improved the performance of sensor systems and have triggered a clear increase in their use in new fields of application. A new generation of smarter, reconfigurable and lower power consumption sensors is being developed in Spain based on FPGAs. In this paper, a review of these developments is presented, describing as well the FPGA technologies employed by the different research groups and providing an overview of future research within this field.

Dynamic Visual Servoing With Chaos Control for Redundant Robots

This paper presents a new dynamic visual control system for redundant robots with chaos compensation. In order to implement the visual servoing system, a new architecture is proposed that improves the system maintainability and traceability. Furthermore, high performance is obtained as a result of parallel execution of the different tasks that compose the architecture. The control component of the architecture implements a new visual servoing technique for resolving the redundancy at the acceleration level in order to guarantee the correct motion of both end-effector and joints. The controller generates the required torques for the tracking of image trajectories. However, in order to guarantee the applicability of this technique, a repetitive path tracked by the robot-end must produce a periodic joint motion. A chaos controller is integrated in the visual servoing system and the correct performance is observed in low and high velocities. Furthermore, a method to adjust the chaos controller is proposed and validated using a real three-link robot.

Dynamic visual servo control of a 4-axis joint tool to track image trajectories during machining complex shapes

A large part of the new generation of computer numerical control systems has adopted an architecture based on robotic systems. This architecture improves the implementation of many manufacturing processes in terms of flexibility, efficiency, accuracy and velocity. This paper presents a 4-axis robot tool based on a joint structure whose primary use is to perform complex machining shapes in some non-contact processes. A new dynamic visual controller is proposed in order to control the 4-axis joint structure, where image information is used in the control loop to guide the robot tool in the machining task. In addition, this controller eliminates the chaotic joint behavior which appears during tracking of the quasi-repetitive trajectories required in machining processes. Moreover, this robot tool can be coupled to a manipulator robot in order to form a multi-robot platform for complex manufacturing tasks. Therefore, the robot tool could perform a machining task using a piece grasped from the workspace by a manipulator robot. This manipulator robot could be guided by using visual information given by the robot tool, thereby obtaining an intelligent multi-robot platform controlled by only one camera.