7,831 research outputs found
Fuzzy Feedback Scheduling of Resource-Constrained Embedded Control Systems
The quality of control (QoC) of a resource-constrained embedded control
system may be jeopardized in dynamic environments with variable workload. This
gives rise to the increasing demand of co-design of control and scheduling. To
deal with uncertainties in resource availability, a fuzzy feedback scheduling
(FFS) scheme is proposed in this paper. Within the framework of feedback
scheduling, the sampling periods of control loops are dynamically adjusted
using the fuzzy control technique. The feedback scheduler provides QoC
guarantees in dynamic environments through maintaining the CPU utilization at a
desired level. The framework and design methodology of the proposed FFS scheme
are described in detail. A simplified mobile robot target tracking system is
investigated as a case study to demonstrate the effectiveness of the proposed
FFS scheme. The scheme is independent of task execution times, robust to
measurement noises, and easy to implement, while incurring only a small
overhead.Comment: To appear in International Journal of Innovative Computing,
Information and Contro
Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks
Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new
generation of sensor networks. Despite intensive research in wireless sensor
networks (WSNs), limited work has been found in the open literature in the
field of WSANs. In particular, quality-of-service (QoS) management in WSANs
remains an important issue yet to be investigated. As an attempt in this
direction, this paper develops a fuzzy logic control based QoS management
(FLC-QM) scheme for WSANs with constrained resources and in dynamic and
unpredictable environments. Taking advantage of the feedback control
technology, this scheme deals with the impact of unpredictable changes in
traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside
each source sensor node to adapt sampling period to the deadline miss ratio
associated with data transmission from the sensor to the actuator. The deadline
miss ratio is maintained at a pre-determined desired level so that the required
QoS can be achieved. The FLC-QM has the advantages of generality, scalability,
and simplicity. Simulation results show that the FLC-QM can provide WSANs with
QoS support.Comment: 13 pages, 8 figures; Open Access at http://www.mdpi.org/sensor
The Fuzzy Feedback Scheduling of Real-Time Middleware in Cyber-Physical Systems for Robot Control
Cyber-physical systems for robot control integrate the computing units and physical devices, which are real-time systems with periodic events. This work focuses on CPS task scheduling in order to solve the problem of slow response and packet loss caused by the interaction between each service. The two-level fuzzy feedback scheduling scheme is designed to adjust the task priority and period according to the combined effects of the response time and packet loss. Empirical results verify the rationality of the cyber-physical system architecture for robot control and illustrate the feasibility of the fuzzy feedback scheduling method
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
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