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

Multi-hop Diffusion LMS for Energy-constrained Distributed Estimation

We propose a multi-hop diffusion strategy for a sensor network to perform distributed least mean-squares (LMS) estimation under local and network-wide energy constraints. At each iteration of the strategy, each node can combine intermediate parameter estimates from nodes other than its physical neighbors via a multi-hop relay path. We propose a rule to select combination weights for the multi-hop neighbors, which can balance between the transient and the steady-state network mean-square deviations (MSDs). We study two classes of networks: simple networks with a unique transmission path from one node to another, and arbitrary networks utilizing diffusion consultations over at most two hops. We propose a method to optimize each node's information neighborhood subject to local energy budgets and a network-wide energy budget for each diffusion iteration. This optimization requires the network topology, and the noise and data variance profiles of each node, and is performed offline before the diffusion process. In addition, we develop a fully distributed and adaptive algorithm that approximately optimizes the information neighborhood of each node with only local energy budget constraints in the case where diffusion consultations are performed over at most a predefined number of hops. Numerical results suggest that our proposed multi-hop diffusion strategy achieves the same steady-state MSD as the existing one-hop adapt-then-combine diffusion algorithm but with a lower energy budget.Comment: 14 pages, 12 figures. Submitted for publicatio

Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-Agent Systems with Nonidentical Dimensions

A novel feedback distributed adaptive control strategy based on radial basis neural network (RBFNN) is proposed for the consensus control of a class of leaderless heterogeneous nonlinear multi-agent systems with the same and different dimensions. The distributed control, which consists of a sequence of comparable matrices or vectors, can make that all the states of each agent to attain consensus dynamic behaviors are defined with similar parameters of each agent with nonidentical dimensions. The coupling weight adaptation laws and the feedback management of neural network weights ensure that all signals in the closed-loop system are uniformly ultimately bounded. Finally, two simulation examples are carried out to validate the effectiveness of the suggested control design strategy

Conflict in Organizations

{Excerpt} Michael Cohen, James March, and Johan Olsen9 have developed an influential, agent-based representation of organizational decision-making processes. They submit that organizations are—at least in part and part of the time—distinguished by three general properties: (i) problematic preferences, (ii) unclear technology, and (iii) fluid participation. Citing, “Although organizations can often be viewed conveniently as vehicles for solving well-defined problems or structures within which conflict is resolved through bargaining, they also provide sets of procedures through which participants arrive at an interpretation of what they are doing and what they have done while in the process of doing it. From this point of view, an organization is a collection of choices looking for problems, issues and feelings looking for decision situations in which they might be aired, solutions looking for issues to which they might be the answer, and decision makers looking for work.” Decision opportunities characterized by problematic preferences, unclear technology, and fluid participation, viz., ambiguous stimuli, generate three possible outcomes, each driven by the energy it requires within the confines of organizational structure. These outcomes, whose meaning changes over time, are resolution, oversight, and flight. Significantly, resolution of problems as a style for making decisions is not the most common; in its place, decision making by flight or oversight is the feature. Is it any wonder then that the relatively complicated intermeshing of elements does not enable organizations to resolve problems as often as their mandates demand