1,892 research outputs found
Cooperative Adaptive Control for Cloud-Based Robotics
This paper studies collaboration through the cloud in the context of
cooperative adaptive control for robot manipulators. We first consider the case
of multiple robots manipulating a common object through synchronous centralized
update laws to identify unknown inertial parameters. Through this development,
we introduce a notion of Collective Sufficient Richness, wherein parameter
convergence can be enabled through teamwork in the group. The introduction of
this property and the analysis of stable adaptive controllers that benefit from
it constitute the main new contributions of this work. Building on this
original example, we then consider decentralized update laws, time-varying
network topologies, and the influence of communication delays on this process.
Perhaps surprisingly, these nonidealized networked conditions inherit the same
benefits of convergence being determined through collective effects for the
group. Simple simulations of a planar manipulator identifying an unknown load
are provided to illustrate the central idea and benefits of Collective
Sufficient Richness.Comment: ICRA 201
Stable Gaussian Process based Tracking Control of Lagrangian Systems
High performance tracking control can only be achieved if a good model of the
dynamics is available. However, such a model is often difficult to obtain from
first order physics only. In this paper, we develop a data-driven control law
that ensures closed loop stability of Lagrangian systems. For this purpose, we
use Gaussian Process regression for the feed-forward compensation of the
unknown dynamics of the system. The gains of the feedback part are adapted
based on the uncertainty of the learned model. Thus, the feedback gains are
kept low as long as the learned model describes the true system sufficiently
precisely. We show how to select a suitable gain adaption law that incorporates
the uncertainty of the model to guarantee a globally bounded tracking error. A
simulation with a robot manipulator demonstrates the efficacy of the proposed
control law.Comment: Please cite the conference paper. arXiv admin note: text overlap with
arXiv:1806.0719
On-line Joint Limit Avoidance for Torque Controlled Robots by Joint Space Parametrization
This paper proposes control laws ensuring the stabilization of a time-varying
desired joint trajectory, as well as joint limit avoidance, in the case of
fully-actuated manipulators. The key idea is to perform a parametrization of
the feasible joint space in terms of exogenous states. It follows that the
control of these states allows for joint limit avoidance. One of the main
outcomes of this paper is that position terms in control laws are replaced by
parametrized terms, where joint limits must be avoided. Stability and
convergence of time-varying reference trajectories obtained with the proposed
method are demonstrated to be in the sense of Lyapunov. The introduced control
laws are verified by carrying out experiments on two degrees-of-freedom of the
humanoid robot iCub.Comment: 8 pages, 4 figures. Submitted to the 2016 IEEE-RAS International
Conference on Humanoid Robot
A nonlinear disturbance observer for robotic manipulators
A new nonlinear disturbance observer (NDO) for robotic manipulators is derived in this paper. The global exponential stability of the proposed disturbance observer (DO) is guaranteed by selecting design parameters, which depend on the maximum velocity and physical parameters of robotic manipulators. This new observer overcomes the disadvantages of existing DOs, which are designed or analyzed by linear system techniques. It can be applied in robotic manipulators for various purposes such as friction compensation, independent joint control, sensorless torque control and fault diagnosis. The performance of the proposed observer is demonstrated by the friction estimation and compensation for a two-link robotic manipulator. Both simulation and experimental results show the NDO works well
Control of flexible joint robotic manipulator using tuning functions design
The goal of this thesis is to design the controller for a single arm manipulator having a flexible joint for the tracking problem in two different cases. A controller is designed for a deterministic case wherein the plant parameters are assumed to be known while another is designed for an adaptive case where all the plant parameters are assumed to be unknown. In general the tracking problem is; given a smooth reference trajectory, the end effector has to track the reference while maintaining the stability. It is assumed that only the output of the manipulator, which is the link angle, is available for measurement. Also without loss of generality, the fast dynamics, that is the dynamics of the driver side of the system are neglected for the sake of simplicity; In the first case, the design procedure adopted is called observer backstepping. Since the states of the system are unavailable for measurement, an observer is designed that estimates the system states. These estimates are fed to the controller which in turn produces the control input to the system; The second case employs a design procedure called tuning functions design. In this case, since the plant parameters are unknown, the observer designed in case one cannot be used for determining the state estimates. For this purpose, parameter update laws and filters are designed for estimation of plant parameters. The filters employed are k-filters. The k-filters and the parameter update laws are given as input to the controller, which generates the control input to the system; For both cases, the mathematical models are simulated using Matlab/Simulink, and the results are verified
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