2,367 research outputs found

    Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments

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    This paper presents two novel control methodologies for the cooperative manipulation of an object by N robotic agents. Firstly, we design an adaptive control protocol which employs quaternion feedback for the object orientation to avoid potential representation singularities. Secondly, we propose a control protocol that guarantees predefined transient and steady-state performance for the object trajectory. Both methodologies are decentralized, since the agents calculate their own signals without communicating with each other, as well as robust to external disturbances and model uncertainties. Moreover, we consider that the grasping points are rigid, and avoid the need for force/torque measurements. Load distribution is also included via a grasp matrix pseudo-inverse to account for potential differences in the agents' power capabilities. Finally, simulation and experimental results with two robotic arms verify the theoretical findings

    Robot Control for Task Performance and Enhanced Safety under Impact

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    A control law combining motion performance quality and low stiffness reaction to unintended contacts is proposed in this work. It achieves prescribed performance evolution of the position error under disturbances up to a level related to model uncertainties and responds compliantly and with low stiffness to significant disturbances arising from impact forces. The controller employs a velocity reference signal in a model-based control law utilizing a non-linear time-dependent term, which embeds prescribed performance specifications and vanishes in case of significant disturbances. Simulation results with a three degrees of freedom (DOF) robot illustrate the motion performance and self-regulation of the output stiffness achieved by this controller under an external force, and highlights its advantages with respect to constant and switched impedance schemes. Experiments with a KUKA LWR4+ demonstrate its performance under impact with a human while following a desired trajectory

    Task-space dynamic control of underwater robots

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    This thesis is concerned with the control aspects for underwater tasks performed by marine robots. The mathematical models of an underwater vehicle and an underwater vehicle with an onboard manipulator are discussed together with their associated properties. The task-space regulation problem for an underwater vehicle is addressed where the desired target is commonly specified as a point. A new control technique is proposed where the multiple targets are defined as sub-regions. A fuzzy technique is used to handle these multiple sub-region criteria effectively. Due to the unknown gravitational and buoyancy forces, an adaptive term is adopted in the proposed controller. An extension to a region boundary-based control law is then proposed for an underwater vehicle to illustrate the flexibility of the region reaching concept. In this novel controller, a desired target is defined as a boundary instead of a point or region. For a mapping of the uncertain restoring forces, a least-squares estimation algorithm and the inverse Jacobian matrix are utilised in the adaptive control law. To realise a new tracking control concept for a kinematically redundant robot, subregion tracking control schemes with a sub-tasks objective are developed for a UVMS. In this concept, the desired objective is specified as a moving sub-region instead of a trajectory. In addition, due to the system being kinematically redundant, the controller also enables the use of self-motion of the system to perform sub-tasks (drag minimisation, obstacle avoidance, manipulability and avoidance of mechanical joint limits)

    Robust prescribed trajectory tracking control of a robot manipulator using adaptive finite-time sliding mode and extreme learning machine method

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    This study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications
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