17,629 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
Second-Order Consensus of Networked Mechanical Systems With Communication Delays
In this paper, we consider the second-order consensus problem for networked
mechanical systems subjected to nonuniform communication delays, and the
mechanical systems are assumed to interact on a general directed topology. We
propose an adaptive controller plus a distributed velocity observer to realize
the objective of second-order consensus. It is shown that both the positions
and velocities of the mechanical agents synchronize, and furthermore, the
velocities of the mechanical agents converge to the scaled weighted average
value of their initial ones. We further demonstrate that the proposed
second-order consensus scheme can be used to solve the leader-follower
synchronization problem with a constant-velocity leader and under constant
communication delays. Simulation results are provided to illustrate the
performance of the proposed adaptive controllers.Comment: 16 pages, 5 figures, submitted to IEEE Transactions on Automatic
Contro
Feedback and time are essential for the optimal control of computing systems
The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems
- …