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Robotics is increasing its presence in the machine tool sector. One interesting application for robot assisted machining involves a robot locally increasing the stiffness of a thin walled part to suppress regenerative vibrations and minimize part deformations during machining. Simulating the dynamics improvement achieved when coupling the robot and the part is of high concern, in order to guarantee the appropriate performance of the assisted machining. Receptance Coupling Substructure Analysis (RCSA) technique for High Speed Machining (HSM) dynamics simulation has been expanded to derive the frequency response of the assembled system composed by the coupling of the thin walled part and the robot.

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The control of flexible link parallel manipulators is still an open area of research, endpoint trajectory tracking being one of the main challenges in this type of robot. The flexibility and deformations of the limbs make the estimation of the Tool Centre Point (TCP) position a challenging one. Authors have proposed different approaches to estimate this deformation and deduce the location of the TCP. However, most of these approaches require expensive measurement systems or the use of high computational cost integration methods. This work presents a novel approach based on a virtual sensor which can not only precisely estimate the deformation of the flexible links in control applications (less than 2% error), but also its derivatives (less than 6% error in velocity and 13% error in acceleration) according to simulation results. The validity of the proposed Virtual Sensor is tested in a Delta Robot, where the position of the TCP is estimated based on the Virtual Sensor measurements with less than a 0.03% of error in comparison with the flexible approach developed in ADAMS Multibody Software.

The aerospace industry still relies on manual processes for finish applications, which can be a tedious task. In recent years, robotic automation has gained interest due to its flexibility and adaptability to provide solutions to this issue. However, these processes are difficult to automate, as the material removal rate can vary due to changes in the process variables. This work proposes an approach for automatically modeling the material removal process based on experimental data in a robotic belt grinding application. The methodology concerns the measurement of the removed mass of a test part during a finishing process using an automatic precision measurement system. Then, experimental models are used to develop a control algorithm for continuous material removal that maintains a uniform finishing process by regulating the robot’s feed rate. Next, the results for various experimental material removal models under different process conditions are presented, showing the process parameter’s influence on the removal capacity. Finally, the proposed control algorithm is validated, achieving a constant material removal rate.

Este artículo presenta una aplicación de seguimiento de trayectoria para vehículos mediante Control Predictivo basado en Modelo (MPC) con un modelo Linealmente Variable en el Tiempo (LTV) con estabilidad garantizada. El sistema de control considera tanto el error lateral como el error de orientación respecto de la trayectoria de referencia para garantizar un correcto seguimiento de la trayectoria bajo ciertos criterios de confort. Además, también se consideran restricciones estrictas en la señal de control, en la variación de la señal de control y en el error lateral de seguimiento de la trayectoria, así como se tienen en cuenta consideraciones de estabilidad. Se exponen los resultados para diferentes tipos de trazados, concluyendo con un circuito y para un rango muy amplio de velocidades.