Abstract Mechatronic systems with nonlinear dynamics are met in motion transmission applications for vehicles and robots. In this article, the control problem for the nonlinear dynamics of mechatronic motion transmission systems is solved with the use of a flatness‐based control approach which is implemented in successive loops. The state‐space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system, and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. In this chain of subsystems, the state variables of the subsequent ( )‐th subsystem become virtual control inputs for the preceding ‐th subsystem and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding subsystems. The whole control method is implemented in successive loops, and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in the following two case studies: (a) control of a permanent magnet linear synchronous motor (PMLSM)‐actuated vehicle's clutch and (ii) control of a multi‐Degrees of Freedom (multi‐DOF) flexible joint robot.
The work proposed here tries to introduce a project-based learning methodology for the subject Sensing Systems of the master’s degree in Automation and Robotics at the University of Alicante. The heterogeneous conditions of the students and the combined face-to-face and online teaching requirements made the teachers change the current teaching model based only on theoretical classes and individual practical exercises, to a more flexible schedule following a Project-based active learning scheme. One of the problems that the teachers found in this subject and the master’s degree in general is the difference in knowledge between the students in the different areas that the subject cover. This is due to the heterogeneity of students who come from different degrees from various universities and even countries. Also, in this course, the teachers must deal with the situation that the incoming students graduated in the Degree in Robotics Engineering (University of Alicante) already have most of the knowledge that this and other subjects cover. This project-based learning is therefore intended to promote self-teaching to allow the most advanced students to get a deep teaching experience while the others learn the basics of the subject. The other great challenge the teachers must deal with is the adaptation to the current face-to-face and online combined teaching classes. As the proposed projects must be carried out using the robotics laboratory hardware, a carefully designed schedule for sharing the resources was made, with special attention to the current cleaning requirements. All these factors make us think that project-based learning will allow all students to acquire the appropriate skills. To evaluate the advantages and disadvantages of this proposal two surveys were carried out, one for the students and another for the current and previous teachers of the subject. The results show how this strategy got good results even for the students with no prior knowledge of the subject area. © 2021 University of Minho. All rights reserved.
Este trabajo muestra como se realiza la ensenanza de robotica mediante un robot modular y los resultados educativos obtenidos en el Master Universitario en Automatica y Robotica de la Escuela Politecnica Superior de la Universidad de Alicante. En el articulo se describen los resultados obtenidos con el uso de este robot modular tanto en competencias genericas como especificas, en las ensenanzas de electronica, control y programacion del Master. En este articulo se exponen los objetivos de aprendizaje para cada uno de ellos, su aplicacion a la ensenanza y los resultados educativos obtenidos. En los resultados del estudio, cabe destacar que el alumno ha mostrado mayor interes y ha fomentado su aprendizaje autonomo. Para ello, el robot modular se construyo con herramientas para fomentar este tipo de ensenanza y aprendizaje, tales como comunicaciones interactivas para monitorizar, cambiar y adaptar diversos parametros de control y potencia del robot.
En este articulo se describe un laboratorio remoto empleado en el aprendizaje practico de la asignatura Sistemas de Control Automatico, que se imparte en el Master Universitario en Automatica y Robotica de la Universidad de Alicante. La aplicacion desarrollada permite a los estudiantes practicar a distancia diferentes conceptos teoricos utilizando un modelo hardware de un proceso industrial real consistente en un sistema de bombeo. En el articulo se describe las caracteristicas mas importantes de este laboratorio remoto, destacando su capacidad para realizar la evaluacion automatica del estudiante. La aplicacion propone un conjunto de experiencias practicas que los alumnos deben resolver haciendo uso del laboratorio remoto. Ademas, la aplicacion ofrece una retroalimentacion que guia al estudiante en los conceptos para mejorar en su aprendizaje. Esta informacion puede ser utilizada por los estudiantes para llevar a cabo un auto-aprendizaje. El documento concluye con un estudio que describe el impacto educativo acerca del uso de esta herramienta en el aprendizaje de los estudiantes.
Este trabajo ha sido parcialmente financiado por OMRON tras la concesion del premio OMRON de “Iniciacion a la investigacion e innovacion en automatica” durante la convocatoria de 2003.
In this work, we evaluate the relationship between human manipulability indices obtained from motion sensing cameras and a variety of muscular factors extracted from surface electromyography (sEMG) signals from the upper limb during specific movements that include the shoulder, elbow and wrist joints. The results show specific links between upper limb movements and manipulability, revealing that extreme poses show less manipulability, i.e., when the arms are fully extended or fully flexed. However, there is not a clear correlation between the sEMG signals' average activity and manipulability factors, which suggests that muscular activity is, at least, only indirectly related to human pose singularities. A possible means to infer these correlations, if any, would be the use of advanced deep learning techniques. We also analyze a set of EMG metrics that give insights into how muscular effort is distributed during the exercises. This set of metrics could be used to obtain good indicators for the quantitative evaluation of sequences of movements according to the milestones of a rehabilitation therapy or to plan more ergonomic and bearable movement phases in a working task.
Free-floating space robotic manipulators (FSRMs) are robotic arms mounted on space platforms, such as spacecraft or satellites which are used for the repair of space vehicles or the removal of noncooperating targets such as inactive material remaining in orbit. In this paper, a novel nonlinear optimal control method is applied to the dynamic model of FSRMs. First, the state-space model of a 3-DOF free-floating space robot is formulated and its differential flatness properties are proven. This model undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the free-floating space robot a stabilizing optimal (H-infinity) feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem under model uncertainty and external perturbations. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed nonlinear optimal control approach achieves fast and accurate tracking of setpoints under moderate variations of the control inputs and a minimum dispersion of energy by the actuators of the free-floating space robot.
The article proposes a nonlinear H-infinity (optimal) control approach to the problem of control of closed-chain robotic mechanisms. The dynamic model of the closed-chain robotic mechanism undergoes approximate linearization, round a local operating point. This local equilibrium is re-calculated at each iteration of the control program and consists of the present value of the state vector of the robotic mechanism and of the last value of the control input that was exerted on it. The linearization is based on Taylor series expansion and the computation of the associated Jacobian matrices. The modelling error due to truncation of higher order terms from this expansion is compensated by the robustness of the control scheme. Next, an H-infinity feedback controller is designed. The feedback gain is computed after solving an algebraic Riccati equation at each iteration of the control algorithm. The control scheme provides solution to a mini-max differential game in which the disturbances and modelling errors try to maximize a quadratic cost functional, while the control input tries to minimize it. Through Lyapunov stability analysis it is proven that the control loop satisfies an H-infinity tracking performance criterion, which signifies elevated robustness to model uncertainty and external perturbations. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven.
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.
