Control reconfiguration

About: Control reconfiguration is a research topic. Over the lifetime, 22423 publications have been published within this topic receiving 334217 citations.

Reconfiguration and module replacement in Argus: theory and practice

Abstract: Early work on reconfiguration of distributed applications was carried out as part of the Argus project at MIT. In this paper, we review that work, focusing on correctness conditions and on alternative implementations, and discuss lessons learnt, not only about the reconfiguration system, but also about language design for supporting reconfigurable systems.

Dynamic interconnection of reconfigurable modules on reconfigurable devices

Abstract: This article presents two approaches to solving the problem of communication between components dynamically placed at runtime on a reconfigurable device. The first is a circuit-routing approach designed for existing FPGAs. This approach uses the reconfigurable multiple bus (RMB). The second, network-based approach targets devices with unlimited reconfiguration capability such as coarse-grained reconfigurable devices. We introduce the dynamic network on chip (DyNoC) as a viable communication infrastructure for communication on dynamically reconfigurable devices. For prototyping the DyNoC on FPGAs, we design and implement an unrestricted communication model for a columnwise-reconfigurable chip. For the DyNoC, as well as for the RMB on chip (RMBoC), we provide algorithms and implementation results from real-life problems.

Dynamic reconfiguration: Basic building blocks for autonomic computing on IBM pSeries servers

Abstract: A logical partition in an IBM pSeriesTM symmetric multiprocessor (SMP) system is a subset of the hardware of the SMP that can host an operating system (OS) instance. Dynamic reconfiguration (DR) on these logically partitioned servers enables the movement of hardware resources (such as processors, memory, and I/O slots) from one logical partition to another without requiring reboots. This capability also enables an autonomic agent to monitor usage of the partitions and automatically move hardware resources to a needy OS instance nondisruptively. Today, as SMPs and nonuniform memory access (NUMA) systems become larger and larger, the ability to run several instances of an operating system(s) on a given hardware system, so that each OS instance plus its subsystems scale or perform well, has the advantage of an optimal aggregate performance, which can translate into cost savings for customers. Though static partitioning provides a solution to this overall performance optimization problem, DR enables an improved solution by providing the capability to dynamically move hardware resources to a needy OS instance in a timely fashion to match workload demands. Hence, DR capabilities serve as key building blocks for workload managers to provide self-optimizing and self-configuring features. Besides dynamic resource balancing, DR also enables Dynamic Capacity Upgrade on Demand, and self-healing features such as Dynamic CPU Sparing, a winning solution for users in this age of rapid growth in Web servers on the Internet.

A Novel Stochastic Framework Based on Cloud Theory and $\theta $ -Modified Bat Algorithm to Solve the Distribution Feeder Reconfiguration

Abstract: Distribution feeder reconfiguration (DFR) is a precious operation strategy that can improve the system from different aspects including total cost, reliability, and power quality. Nevertheless, the high complexity of the new smart grids has resulted in much uncertainty in the DFR problem that necessities the use of a sufficient stochastic framework to deal with them. In this way, this paper proposes a new stochastic framework based on cloud theory to account the uncertainties associated with multiobjective DFR problem from the reliability point of view. Cloud theory is constructed based on fuzzy theory and probability idea. In comparison with the Monte Carlo simulation method, cloud models can give more information on the uncertainties associated with the problem. This special aspect of cloud models makes it possible to integrate the fuzziness and randomness of qualitative concepts through the cloud drops and then transforms them to the quantitative model. In order to solve the proposed problem, a fast and powerful optimization technique is required. To deal with this issue, a new optimization algorithm designated as ${\theta }$ -bat algorithm is proposed in this paper. The feasibility and satisfying performance of the proposed method are examined on the 32-bus and 69-bus IEEE distribution test system.

Robust Control Allocation for Spacecraft Attitude Tracking Under Actuator Faults

Abstract: This brief addresses attitude tracking problems for an over-actuated spacecraft in the presence of actuator faults, imprecise fault estimation, and external disturbances. First, a model reference adaptive control technique is used to design a high-level controller to produce the three-axis virtual control torque. Then, taking fault estimation uncertainties into account, a robust control allocation (RobCA) strategy is proposed to redistribute virtual control signals to the remaining actuators when an actuator fault occurs. The RobCA is formulated as a min–max optimization problem, which deals with actuator faults directly without reconfiguring the controller and ensures some robustness of system performances. Finally, simulation results are provided to show the effectiveness of the overall control strategy.