Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
On Event Based State Estimation
HSCC '09 Proceedings of the 12th International Conference on Hybrid Systems: Computation and Control
Automatica (Journal of IFAC)
Brief paper: A state-feedback approach to event-based control
Automatica (Journal of IFAC)
Journal of Systems Architecture: the EUROMICRO Journal
The influence of event-based sampling techniques on data transmission and control performance
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
On integration of event-based estimation and robust MPC in a feedback loop
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
QoC elastic scheduling for real-time control systems
Real-Time Systems
Adaptive Sampling for Linear State Estimation
SIAM Journal on Control and Optimization
A comprehensive co-simulation platform for cyber-physical systems
Computer Communications
Model-based periodic event-triggered control for linear systems
Automatica (Journal of IFAC)
Energy-efficient sampling of networked control systems over IEEE 802.15.4 wireless networks
Automatica (Journal of IFAC)
Hi-index | 22.15 |
The standard approach in computer-controlled systems is to sample and control periodically. In certain applications, such as networked control systems or energy-constrained systems, it could be advantageous to instead use event-based control schemes. Aperiodic event-based control of first-order stochastic systems has been investigated in previous work. In any real implementation, however, it is necessary to have a well-defined minimum inter-event time. In this paper, we explore two such sporadic control schemes for first-order linear stochastic systems and compare the achievable performance to both periodic and aperiodic control. The results show that sporadic control can give better performance than periodic control in terms of both reduced process state variance and reduced control action frequency.