89 research outputs found

    Impedence Control for Variable Stiffness Mechanisms with Nonlinear Joint Coupling

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    The current discussion on physical human robot interaction and the related safety aspects, but also the interest of neuro-scientists to validate their hypotheses on human motor skills with bio-mimetic robots, led to a recent revival of tendondriven robots. In this paper, the modeling of tendon-driven elastic systems with nonlinear couplings is recapitulated. A control law is developed that takes the desired joint position and stiffness as input. Therefore, desired motor positions are determined that are commanded to an impedance controller. We give a physical interpretation of the controller. More importantly, a static decoupling of the joint motion and the stiffness variation is given. The combination of active (controller) and passive (mechanical) stiffness is investigated. The controller stiffness is designed according to the desired overall stiffness. A damping design of the impedance controller is included in these considerations. The controller performance is evaluated in simulation

    Design, modeling, and control of a variable stiffness elbow joint

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    New technological advances are changing the way robotics are designed for safe and dependable physical human–robot interaction and human-like prosthesis. Outstanding examples are the adoption of soft covers, compliant transmission elements, and motion control laws that allow compliant behavior in the event of collisions while preserving accuracy and performance during motion in free space. In this scenario, there is growing interest in variable stiffness actuators (VSAs). Herein, we present a new design of an anthropomorphic elbow VSA based on an architecture we developed previously. A robust dynamic feedback linearization algorithm is used to achieve simultaneous control of the output link position and stiffness. This actuation system makes use of two compliant transmission elements, characterized by a nonlinear relation between deflection and applied torque. Static feedback control algorithms have been proposed in literature considering purely elastic transmission; however, viscoelasticity is often observed in practice. This phenomenon may harm the performance of static feedback linearization algorithms, particularly in the case of trajectory tracking. To overcome this limitation, we propose a dynamic feedback linearization algorithm that explicitly considers the viscoelasticity of the transmission elements, and validate it through simulations and experimental studies. The results are compared with the static feedback case to showcase the improvement in trajectory tracking, even in the case of parameter uncertainty

    FEEDBACK LINEARIZATION FOR DECOUPLED POSITION/STIFFNESS CONTROL OF BIDIRECTIONAL ANTAGONISTIC DRIVES

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    To ensure safe human-robot interaction impedance robot control has arisen as one of the key challenges in robotics. This paper elaborates control of bidirectional antagonistic drives – qbmove maker pro. Due to its mechanical structure, both position and stiffness of bidirectional antagonistic drives could be controlled independently. To that end, we applied feedback linearization. Feedback linearization based approach initially decouples systems in two linear single-input-single-output subsystems: position subsystem and stiffness subsystem. The paper elaborates preconditions for feedback linearization and its implementation. The paper presents simulation results that prove the concept but points out application issues due to the complex mechanical structure of the bidirectional antagonistic drives

    Естимација крутости и адаптивно управљање код попустљивих робота

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    Although there has been an astonishing increase in the development of nature- inspired robots equipped with compliant features,i.e.soft robots, their full potential has not been exploited yet. One aspect is that the soft robotics research has mainly focused on their position control only, whilest iffness is managed in open loop. Moreover, due to the difficulties of achieving consistent production of the actuation systems for soft articulated robots and the time-varyingnatureoftheirinternalflexibleelements,whicharesubjecttoplasticdeformation overtime,itiscurrentlyachallengetopreciselydeterminethejointstiffness. . In this regard, the thesis puts an emphasis on stiffness estimation and adaptive control for soft articulated robots driven by antagonistic Variable Stiffness Actuators (VSAs) with the aim to impose the desired dynamics of both position and stiffness, which would finally contribute to the overall safety and improved performance of a soft robot. By building upon Unknown Input Observer (UIO) theory, invasive and non-invasive solutions for estimation of stiffness in pneumatic and electro-mechanical actuators are proposed and in the latter case also experimentally validated. Beyond the linearity and scalability advantage, the approaches have an appealing feature that torque and velocity sensors are not needed. Once the stiffness is determined, innovative control approaches are introduced for soft articulated robots comprising an adaptive compensator and a dynamic decoupler. The solutions are able to cope with uncertainties of the robot dynamic model and, when the desired stiffness is constant or slowly-varying, also of the pneumatic actuator. Their verification is performed via simulations and then the pneumatic one is successfully tested on an experimental setup. Finally, the thesis shows via extensive simulations the effectiveness of adaptive technique ap- plied to soft-bodied robots, previously deriving the sufficient and necessary conditions for the controller convergence.Iako se danas izuzetno intenzivno radi na razvoju robota inspirisanih prirodom koje odlikuje elastična struktura, njihov puni potencijal jox uvek nije iskorišćen. Sa jedne strane, istraživanja u oblasti popustljivih robota su uglavnom fokusirana samo na upravljanje njihovom pozicijom, dok se krutost reguliše u otvorenoj sprezi. Pored toga, zbog poteškoća u postiznju konzistentne proizvodnje aktuatora i promenljive prirode njihovih elastičnih elemenata, koji su vremenom podlo_ni plastičnoj deformaciji, trenutno je izazov precizno odrediti krutost zglobova robota. U cilju doprinosa poboljšanja_u performansi i bezbednosti rada popustivih robota, teza prikazuje doprinos proceni krutosti i adaptivnog simultanog upravljanja pozicijom i krutosti antagonističkih aktuatora promenljive krutosti (VSA). Oslanjajući se na teoriju opservera nepoznatih ulaza (UIO), predložena su invazivna i neinvazivna rešenja za procenu krutosti u pneumatskim i elektromehaničkim aktuatorima i eksperimentalno verifikovana u slučaju druge grupe aktuatora. Pored linearnosti i skalabilnosti, ovi pristupi imaju privlaqnu osobinu da senzori momenta i brzine nisu potrebni. Teza predla_e inovativne sisteme upravljanja koji poseduju adaptivni kompenzator i dinamički dekupler. Predložene metode upravljanja demonstriraju mogućnost da kompenzuju nesigurnosti dinamičkog modela robota bez obzira da li je on pogođen električnim ili pneumatskim aktuatorima. Nakon simulacija, razvijeno upravljanje je verifikovano i na pneumatskom robotu. Na kraju teze, obimne simulacije pokazuju efikasnost adaptivne tehnike kada se primeni na robote sa fleksibilnim linkovima, prethodno izvodeći dovoljne i potrebne uslove za konvergenciju kontrolera

    Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes

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    Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a promising alternative as models are trained on real measured data and nonlinearities are inherently taken into account. Our work presents a universal, learning-based approach for position and stiffness control of soft actuators. After introducing a soft pneumatic VSA, the model is learned with input-output data. For this purpose, a test bench was set up which enables automated measurement of the variable joint stiffness. During control, Gaussian processes are used to predict pressures for achieving desired position and stiffness. The feedforward error is on average 11.5% of the total pressure range and is compensated by feedback control. Experiments with the soft actuator show that the learning-based approach allows continuous adjustment of position and stiffness without model knowledge.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

    Output-Based Control of Robots with Variable Stiffness Actuation

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    The output-based control of a redundant robotic manipulator with relevant and adjustable joint stiffness is addressed. The proposed controller is configured as a cascade system that allows the decoupling of the actuators dynamics from the arm dynamics and the consequent reduction of the order of the manipulator dynamic model. Moreover, the proposed controller does not require the knowledge of the whole robot state: only the positions of the actuators and of the joints are necessary. This approach represents a significant simplification with respect to previously proposed state feedback techniques. The problem of controlling simultaneously the position trajectory and the desired stiffness in both the joint and work space is investigated, and the relations between the manipulator redundancy and the selection of both the joint and work space stiffness of the manipulator are discussed. The effectiveness of the proposed approach is verified by simulations of a 3 degrees of freedom planar manipulator

    Numerical solutions for design and dynamic control of compliant robots

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    This work is focused on the development of numerical methods for the design and control of robots, with particular emphasis on joint elasticity. First, a general methodology is presented that is able to solve the problem of computing the inverse dynamics of a serial robot manipulator with an arbitrarily large number of elastic joints in a recursive numerical way. The solution algorithm is a generalized version of the standard Newton-Euler approach. The algorithm is presented with numerous extensions and variants, including the extension to variable-stiffness technologies and control applications. Then, an optimization framework is introduced for the design and analysis of biped walkers characterized by elastic joints, with comparative results demonstrating the scope of application of joint compliance in bipedal walking

    Optimal Torque and Stiffness Control in Compliantly Actuated Robots

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    Abstract — Anthropomorphic robots that aim to approach human performance agility and efficiency are typically highly redundant not only in their kinematics but also in actuation. Variable-impedance actuators, used to drive many of these devices, are capable of modulating torque and passive impedance (stiffness and/or damping) simultaneously and independently. Here, we propose a framework for simultaneous optimisation of torque and impedance (stiffness) profiles in order to optimise task performance, tuned to the complex hardware and incorporating real-world constraints. Simulation and hardware experiments validate the viability of this approach to complex, state dependent constraints and demonstrate task performance benefits of optimal temporal impedance modulation. Index Terms — Variable-stiffness actuation, physical constraints, optimal control

    Natural Motion for Energy Saving in Robotic and Mechatronic Systems

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    Energy saving in robotic and mechatronic systems is becoming an evermore important topic in both industry and academia. One strategy to reduce the energy consumption, especially for cyclic tasks, is exploiting natural motion. We define natural motion as the system response caused by the conversion of potential elastic energy into kinetic energy. This motion can be both a forced response assisted by a motor or a free response. The application of the natural motion concepts allows for energy saving in tasks characterized by repetitive or cyclic motion. This review paper proposes a classification of several approaches to natural motion, starting from the compliant elements and the actuators needed for its implementation. Then several approaches to natural motion are discussed based on the trajectory followed by the system, providing useful information to the researchers dealing with natural motion

    Simultaneous Motion Tracking and Joint Stiffness Control of Bidirectional Antagonistic Variable-Stiffness Actuators

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    Since safe human-robot interaction is naturally linked to compliance in these robots, this requirement presents a challenge for the positioning accuracy. The class of variable- stiffness robots features intrinsically soft contact behavior where the physical stiffness can even be altered during operation. Here we present a control scheme for bidirectional, antagonistic variable-stiffness actuators that achieve high-precision link-side trajectory tracking while simultaneously ensuring compliance during physical contact. Furthermore, the approach enables to regulate the pretension in the antagonism. The theoretical claims are confirmed by formal analyses of passivity during physical interaction and the proof of uniform asymptotic stability of the desired link-side trajectories. Experiments on the forearm joint of the DLR robot David verify the proposed approach
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