4 research outputs found
Stabilizing Scheduling Policies for Networked Control Systems
This paper deals with the problem of allocating communication resources for
Networked Control Systems (NCSs). We consider an NCS consisting of a set of
discrete-time LTI plants whose stabilizing feedback loops are closed through a
shared communication channel. Due to a limited communication capacity of the
channel, not all plants can exchange information with their controllers at any
instant of time. We propose a method to find periodic scheduling policies under
which global asymptotic stability of each plant in the NCS is preserved. The
individual plants are represented as switched systems, and the NCS is expressed
as a weighted directed graph. We construct stabilizing scheduling policies by
employing cycles on the underlying weighted directed graph of the NCS that
satisfy appropriate contractivity conditions. We also discuss algorithmic
design of these cycles
Transmission Scheduling in Wireless Networked Control for Industrial IoT
Wireless networked control systems (WNCS) consist of spatially distributed sensors, actuators, and controllers communicating through wireless networks. WNCS has recently emerged as a fundamental infrastructure technology to enable reliable control for mission-critical Industrial Internet of Things (IIoT) applications such as factory automation, intelligent transportation systems, telemedicine and smart grids. The design of WNCS requires the joint design of communications, computing and control. WNCS faces challenges such as unreliable transmission and latency in transmitting control and sensing information due to channel impairment in wireless communications for large scale deployment. This can have a significant impact on the stability and performance of WNCS. Most existing works have mainly focused on the design of WNCS from a control perspective rather than communications or have considered an ideal or simplified wireless model. How to reliably control WNCS in practical wireless channels and design wireless communication scheduling policy to optimize control performance is a challenging task.
This thesis presents the design of practical communication protocols of a general discrete linear time-invariant (LTI) dynamic system in WNCS. We address the transmission scheduling problems in WNCS in three scenarios, which require the development of different strategies. Firstly, to minimize the long-term average remote estimation mean-squared-error (MSE), a hybrid automatic repeat request (HAQR)-based real-time estimation framework is proposed. Secondly, a downlink-uplink transmission scheduling policy is developed for a half-duplex (FD) controller to optimize the system performance.
Finally, a novel controller with adaptive packet length is studied, and a variable-length packet-transmission policy is proposed to balance the delay-reliability tradeoff in WNCS optimally. Numerical results show that our dynamic scheduling policies can significantly improve the performance of WNCS in terms of estimation and control costs while maintaining the stability of the system
On stochastic sensor network scheduling for multiple processes
We consider the problem of multiple sensor scheduling for remote state estimation of multiple process over a shared link. In this problem, a set of sensors monitor mutually independent dynamical systems in parallel but only one sensor can access the shared channel at each time to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling in which each sensor makes transmission decisions based on both channel accessibility and distributed event-triggering conditions. The corresponding minimum mean squared error (MMSE) estimator is explicitly given. Considering information patterns accessed by sensor schedulers, time-based ones can be treated as a special case of the proposed one. By ultilizing realtime information, the proposed schedule outperforms the time-based ones in terms of the estimation quality. Resorting to solving an Markov decision process (MDP) problem with average cost criterion, we can find optimal parameters for the proposed schedule. As for practical use, a greedy algorithm is devised for parameter design, which has rather low computational complexity. We also provide a method to quantify the performance gap between the schedule optimized via MDP and any other schedules.Accepted versio