Adaptive controller based on barrier Lyapunov function for a composite Cartesian-delta robotic device for precise time-varying position tracking.

This study presents the design of an adaptive event-driven controller for solving the trajectory tracking problem of a composite robotic device made up of a three-dimensional Cartesian and a parallel Delta robot. The proposed composite device has a mathematical model satisfying a standard Lagrangian structure affected by modeling uncertainties and external perturbations. The adaptive gain of the controller is considered to enforce the convergence of the tracking error while the state bounds are satisfied. The barrier Lyapunov function addresses the preconceived state constraints for both robotic devices by designing a time-varying gain that guarantees the ultimate boundedness of the tracking error under the effect of external perturbations. The event-driven approach considers that the Cartesian robot is moving into a predefined invariant zone near to the origin. In contrast, the delta robot can complete the tracking problem once the end-effector is inside the given zone. The suggested controller was evaluated using a virtual representation of the composite robotic device showing better tracking performance (while the restrictions are satisfied) than the performances obtained with the traditional linear state feedback controllers. Analyzing the mean square error and its integral led to confirming the benefits of using the adaptive barrier control.

Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control.

Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due to the system's imperfection (i.e., unpredictable duty cycle) and influences the closed-loop performance. The methodology is executed offline; thus, it highlights the applicability of collected BDC motor measurements in time series analysis. The proposed method updates the PID controller gains based on the Simulink ™ controller tuning toolbox. The contribution of this approach is shown in a comparative study that indicates an opportunity to use time series analysis to forecast DC motor parameters, to re-tune PID controller gains, and to obtain similar performance under the same perturbation conditions. The research demonstrates the practical applicability of the proposed method for fine-tuning/re-tuning controllers in real-time. The results show the inclusion of the time series analysis to recalculate controller gains as an alternative for adaptive control.

Energetic optimization of an autonomous mobile socially assistive robot for autism spectrum disorder.

The usage of socially assistive robots for autism therapies has increased in recent years. This novel therapeutic tool allows the specialist to keep track of the improvement in socially assistive tasks for autistic children, who hypothetically prefer object-based over human interactions. These kinds of tools also allow the collection of new information to early diagnose neurodevelopment disabilities. This work presents the integration of an output feedback adaptive controller for trajectory tracking and energetic autonomy of a mobile socially assistive robot for autism spectrum disorder under an event-driven control scheme. The proposed implementation integrates facial expression and emotion recognition algorithms to detect the emotions and identities of users (providing robustness to the algorithm since it automatically generates the missing input parameters, which allows it to complete the recognition) to detonate a set of adequate trajectories. The algorithmic implementation for the proposed socially assistive robot is presented and implemented in the Linux-based Robot Operating System. It is considered that the optimization of energetic consumption of the proposal is the main contribution of this work, as it will allow therapists to extend and adapt sessions with autistic children. The experiment that validates the energetic optimization of the proposed integration of an event-driven control scheme is presented.

Adaptive Control in Passive rehabilitation routines using ELLTIO / Control Adaptable en rutinas de rehabilitación pasiva utilizando ELLTIO

ABSTRACT: The Exoskeleton for Lower Limb Training with Instrumented Orthosis (ELLTIO) is a mechatronic device that can be used to assist in passive kinesitherapy to increase human muscles strength and resistance [1]. This paper presents an alternative for passive rehabilitation process using an exoskeleton for knee and ankle. The main idea is assist a pro fessional physiotherapist in the design and performance of exercises routines for his patients using the prototype. The knee and ankle joint's movements are recorded and storage during the exercises to propose a similar computer generated trajectories which the exoskeleton on should follow. An adaptive controller is implemented to track the trajectories and adapt the user parameters.

RESUMEN: El exoesqueleto para el entrenamiento de miembros inferiores con órtesis instrumentada (ELLTIO) por sus siglas en ingles "Exoskeleton for Lower Limb Training with Instrumented Orthosis" es un dispositivo mecatrónico que se puede utilizar para ayudar en la fisioterapia pasiva para aumentar la fuerza y resistencia de los músculos humanos. En este trabajo se presenta una alternativa para el proceso de rehabilitación pasiva utilizando un exoesqueleto de rodilla y tobillo. La idea principal es ayudar a un fisioterapeuta profesional en el diseño y ejecución de rutinas de ejercicios para sus pacientes utilizando el prototipo. Los movimientos de la articulación de la rodilla y el tobillo se registran y se almacenan durante los ejercicios para proponer trayectorias similares generadas por computadora que el exoesqueleto debe seguir. Se implementa un controlador adaptativo para rastrear las trayectorias y adaptar los parámetros del usuario.