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Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to "difficult" control problems. Unfortunately, the number of samples required in order to guarantee stringent performance levels may be prohibitively large. This paper introduces bootstrap learning methods and the concept of stopping times to drastically reduce the bound on the number of samples required to achieve a performance level. We then apply these results to obtain more efficient algorithms which probabilistically guarantee stability and robustness levels when designing controllers for uncertain systems. Index Terms-Decidability theory, NP-hard problems, Radamacher bootstrap, robust control, sample complexity, statistical learning.

This study investigates the problem of controller design for systems with uncertain sampling rates. The system is controlled through a communication network. The sampling period, within a given interval, is assumed to be time-varying and a simplified framework for the network-induced delay is considered. The overall system is thus described by an uncertain discrete-time model with time-varying parameters inside a polytope whose vertices are obtained by means of the Cayley–Hamilton theorem. A digital robust controller that minimises an upper bound to the ℋ∞ performance of the closed-loop networked control system (NCS) is determined. The design conditions rely on a particular parameter-dependent Lyapunov function and are expressed as bilinear matrix inequalities (BMIs) in terms of extra matrix variables, which may be explored in the search for a better system behaviour. Numerical examples illustrate the results. [ABSTRACT FROM AUTHOR]

This brief documents experimental results using a deterministic dynamic nonlinear system for load balancing, previously reported by Tang et al. in a cluster of computer nodes used for parallel computations in the presence of time delays and resource constraints. While previous publications by the authors have provided theoretical analysis of this load-balancing strategy using an idealized model, and have documented experiments using a simulated database, experimental results using a complete database for DNA profiles are documented here. Evaluation of the proposed load-balancing strategy using an actual database was critical because of several characteristics of the database that cannot be accurately captured using either a simulation model or database, including the variation in times required for the database to perform search operations, the time-varying and task-dependent computational load the database imposes upon each node of the parallel computer, and the time-varying network traffic imposed by both the communication of database search requests and results, mixed with the traffic generated by the load-balancing strategy. Although the load-balancing strategy can be represented in a relatively straightforward manner using mathematics, its implementation is by necessity an approximation to its mathematical description. The reported experimental results serve to validate the superiority of using the controller based on the anticipated work loads to a controller based on local work loads, which has been predicted with experiments using a simulated database and documented in prior publications. The experiments demonstrate the efficacy of the load-balancing strategy using an anticipated pattern of work loads and provide support for scalability of the approach. [ABSTRACT FROM AUTHOR]

The purpose of this article is to show how the design procedure commonly referred to as integrator backstepping can be used to design globally stable trajectory tracking controllers for Rigid-Link Flexible-Joint (RLFJ) robot manipulators. Three different types of controllers are developed: (1) an exact model knowledge-based controller, (2) an adaptive controller that compensates for parametric uncertainty, and (3) a robust controller that compensates for parametric uncertainty and unknown bounded disturbances. All three controllers are based on previously published work but are presented here in a unifying. [ABSTRACT FROM AUTHOR]