2,367 research outputs found
Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments
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
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
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)
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Reinforcement learning for human-robot shared control
This paper aims at proposing a general framework of shared control for human-robot interaction. Human dynamics are considered in analysis of the coupled human-robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof. Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations. Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human-robot shared control system, without the requirement of the knowledge of humans and robots dynamics
Robust prescribed trajectory tracking control of a robot manipulator using adaptive finite-time sliding mode and extreme learning machine method
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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