Figure 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Controlling soft robots is a significant challenge due to the nonlinear elastic nature of the soft materials that conform their structure. This paper studies the identification and control problems of a novel two-degrees-of-freedom, tendon-actuated, soft robotic arm. A decoupled identification approach is presented; later, a fractional order contro...
Contexts in source publication
Context 1
... aim of this paper is to develop a functional controller for the soft robotic arm shown in Figure 1, developed at the RoboticsLab of the Universidad Carlos III of Madrid (see [18] for details). Its innovative design is under a patenting process [19]. ...
Context 2
... actuation of the soft robotic arm (Figure 1) is achieved by varying the length of each of the three tendons placed longitudinally inside its link. The tendons are routed through the vertices of the triangles and are attached to the free end of the arm and to a winch at the base. ...
Context 3
... are carried out on the real system with the proposed controllers, testing the time response to a pitch step reference of α = 40 • and keeping a constant yaw angle of β = 0 • . Figures 9 and 10 show the ideal time response represented as the dashed red line, the experimental response of the system without load as the blue continuous line and the experimental response with a load of 500 g as the orange continuous line. Tables 5 and 6 show the response metrics of the systems with and without loads for the PI and FOPI controllers, respectively. ...
Context 4
... Experiment: Using Control Specifications φ m = 60 • and ω gc = 5 rad/s Now, a faster performance speed is required and the gain crossover frequency is increased up to ω gc = 5 rad/s. The Bode plots of the open loop systems with the corresponding PI and FOPI controllers are shown in Figure 11 for the PI case and Figure 12 for the FOPI case. ...
Context 5
... Experiment: Using Control Specifications φ m = 60 • and ω gc = 5 rad/s Now, a faster performance speed is required and the gain crossover frequency is increased up to ω gc = 5 rad/s. The Bode plots of the open loop systems with the corresponding PI and FOPI controllers are shown in Figure 11 for the PI case and Figure 12 for the FOPI case. ...
Context 6
... d is the distance from the focus to the origin and nt is the angle of the focal ratio varying by steps of 0.005 radians at a frequency of 50 Hz. Figure 13 shows the time response of the system for the PI and FOPI cases with no load at the tip. It can be seen that both controllers perform correctly and the arm tracks the reference quite accurately. ...
Context 7
... is not due to the control but to the mechanical nature of the arm and its constant curvature, which forces winding and unwinding changes of the tendons when different movement regions are reached in order to correctly track the reference. When the same test is performed with a disc of 500 g placed at the arm tip, the system shows the response of Figure 14. The presence of load makes the PI system unable to track the reference in three of the four movement regions, and very significant oscillations are observed. ...
Context 8
... expected, the PI performance with the 500 g load shows the worst results (more affected by the influence of gravity), whereas the FOPI performance is remarkable. In order to asses the system behavior when a more demanding reference is used, Figure 15 shows the PI and FOPI responses to a step input of α = 40 • and β = 0 • for the 500 g load case. The PI system shows a limit cycle performance, whereas the FOPI response is far from instability. ...
Citations
... However, in the last years, the projects HumaSoft (Diseño y Control de Eslabones Blandos para Robots Humanoides, with reference DPI2016-75330-P, funded by the Spanish Ministry of Economics, Industry and Competitiveness, 2016-2021) and SofIA (Articulación blanda inteligente con capacidades de reconfiguración y modularidad para plataformas robóticas, with reference PID2020-113194GB-I00, funded by the Spanish Ministry of Economics, Industry and Competitiveness, 2021-2024) have been working on the design of new soft limbs that can replace the existing rigid link components. Currently, two soft robot prototypes, an arm [24] and a neck [25], have been developed. The bioinspired soft neck was designed to resemble a human neck, able to mimic frontal (pitch) and lateral (roll) movements. ...
In this paper, a new approach for head camera stabilization of a humanoid robot head is proposed, based on a bio-inspired soft neck. During walking, the sensors located on the humanoid’s head (cameras or inertial measurement units) show disturbances caused by the torso inclination changes inherent to this process. This is currently solved by a software correction of the measurement, or by a mechanical correction by motion cancellation. Instead, we propose a novel mechanical correction, based on strategies observed in different animals, by means of a soft neck, which is used to provide more natural and compliant head movements. Since the neck presents a complex kinematic model and nonlinear behavior due to its soft nature, the approach requires a robust control solution. Two different control approaches are addressed: a classical PID controller and a fractional order controller. For the validation of the control approaches, an extensive set of experiments is performed, including real movements of the humanoid, different head loading conditions or transient disturbances. The results show the superiority of the fractional order control approach, which provides higher robustness and performance.
... These positions do not have a practical application for soft arm manipulation, as they do not provide significant changes in the output, and would complicate the model. The decoupling methodology can be found described in more detail in [213]. ...
... Finalmente, la Sección 5 concluye destacando los principales logros de la investigación y trabajos futuros. El trabajo estará enfocado en crear modelos de cinemática directa e inversa para un brazo robótico blando de dos grados de libertad (DoF) desarrollado por el RoboticsLab de la Universidad Carlos III de Madrid (Nagua et al., 2021;Relaño et al., 2022). ...
... En el método de desacoplamiento, se asume que, al doblarse el cuerpo blando, se producirá una curvatura constante, por lo que se podrá usar la relación L 1 + L 2 + L 3 = 0, donde L i se refiere a la longitud del i-ésimo tendón. Al resolver este sistema de ecuaciones se obtienen las longitudes de los tendones resultantes como función de α y β (losángulos de pitch y yaw, respectivamente), como se muestra en las ecuaciones 1, 2 y 3. La metodología de desacoplamiento se describe con más detalle en (Relaño et al., 2022). ...
La identificación adecuada del sistema es esencial para lograr un buen rendimiento de las tareas y una buena interacción con el entorno mediante la robótica blanda. Aunque se han utilizado técnicas analíticas tradicionales basadas en modelos, a veces resultan insuficientes. Los métodos basados en datos son una alternativa prometedora. Sin embargo, su uso en robótica blanda ha sido limitado y poco explorado. Este trabajo explora el uso de procesos Gaussianos para identificar la cinemática directa e inversa de un brazo robótico blando de dos grados de libertad accionado por tres tendones.
... In each case, the kinematics of the robots are very different and the proposed solution is very focused on the specific problem of each robot. Although these mismatches between the theoretical model and the experimental model can be dealt with from a robust control perspective [96], [97], it is advisable to consider other more efficient modeling techniques for this type of system, in particular data-based techniques, as discussed below. ...
... Some preliminary works like the fractional order robust control proposed in [109] have been found able to solve this problem. Later, that approach was used to propose similar solutions to different platforms, as described in [96], [97], [111], [114], [115] with very good results. Although that strategy can effectively solve the positioning problem, there is still an important work to do to improve the controllers proposed. ...
... The application of fractional calculus to control robotic systems has gained interest in the last few years because it yields controllers with enhanced robustness to external disturbances and parametric uncertainties. FO controllers have been applied to ensure control performances as tip position accuracy and suppression of the residual vibrations: FO adaptive back-stepping control [23], FO PID controller based on Kalman filter estimation [24], FO PID control [25], FO modeling and PI control of soft robotic arms [26], FO optimal control [27] and variable structure system fractional sliding mode controller [28], and a comparison was realized between the sliding mode controller and the fractional sliding mode controller in a non-commensurate order in [29]. The latest review about fractional order modeling and control of flexible and rigid robotic manipulators is presented in [30]. ...
Model design and motion control are considered the cornerstones of the robotic field that allow for achieving performance tasks. This article proposes a new dynamic modeling and control approach for very lightweight mechanical systems carrying payloads. The selection of the model and the design of the control are elaborated on using a fractional order framework under different conditions. The use of fractional order calculus is justified by the better performance that reveals a fractional order model compared to an integer order model of similar complexity. The mechanical structure of very lightweight manipulators has vibrations that impede the accurate positioning of their end effector. Moreover, they have actuators with high friction and sensors to measure the vibrations, which often are strain gauges, that have offset and high-frequency noise. All these mentioned problems might degrade the mechanical system’s performance. Hence, to overcome these inconveniences, two nested-loop controls are examined: an inner loop that controls the motor dynamics and removes the friction effects and an outer loop implemented to eliminate the beam vibrations by adapting the input-state feedback linearization technique. Then, we propose a new fractional order control scheme that (1) removes the strain gauge offset disturbances, (2) reduces the risk of the actuator’s saturation caused by the high-frequency noise of strain gauges and (3) reduces the dynamic effects of huge payload changes. We prove that our fractional controller has enhanced robustness with respect to the above-mentioned problems. Finally, the investigated approach is validated experimentally by applying it to a lightweight robot mounted on an air table.
... Neuron models formed by nonlinear map are called discrete neuron models. [15][16][17][18][19][20][21] The discrete neuron model has the advantage of faster speed of simulations and the ease of understanding complex phenomena. [22][23][24] Map-based discrete neuron models have become most popular in recent years. ...
At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility and local activity, locally active discrete memristors (LADMs) are also suitable for simulating synapses. In this paper, we use a LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.
... En la actualidad, la marcha bípeda es una de lasáreas de investigación más prolíferas y más estudiadas en el campo de los robots humanoides, desde el diseño electromecánico para mejorar la respuesta del robot, hasta el estudio y construcción de partes blandas que aumente la eficiencia y armonía del movimiento Mena et al., 2020;Relaño et al., 2023). Pero ¿cómo se caracteriza una caminata? ...
