Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control

Decentralized Stochastic Linear-Quadratic Optimal Control with Risk Constraint and Partial Observation

This paper addresses a risk-constrained decentralized stochastic linear-quadratic optimal control problem with one remote controller and one local controller, where the risk constraint is posed on the cumulative state weighted variance in order to reduce the oscillation of system trajectory. In this model, local controller can only partially observe the system state, and sends the estimate of state to remote controller through an unreliable channel, whereas the channel from remote controller to local controllers is perfect. For the considered constrained optimization problem, we first punish the risk constraint into cost function through Lagrange multiplier method, and the resulting augmented cost function will include a quadratic mean-field term of state. In the sequel, for any but fixed multiplier, explicit solutions to finite-horizon and infinite-horizon mean-field decentralized linear-quadratic problems are derived together with necessary and sufficient condition on the mean-square stability of optimal system. Then, approach to find the optimal Lagrange multiplier is presented based on bisection method. Finally, two numerical examples are given to show the efficiency of the obtained results

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

Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control

We consider a joint uplink and downlink scheduling problem of a fully distributed wireless networked control system (WNCS) with a limited number of frequency channels. Using elements of stochastic systems theory, we derive a sufficient stability condition of the WNCS, which is stated in terms of both the control and communication system parameters. Once the condition is satisfied, there exists a stationary and deterministic scheduling policy that can stabilize all plants of the WNCS. By analyzing and representing the per-step cost function of the WNCS in terms of a finite-length countable vector state, we formulate the optimal transmission scheduling problem into a Markov decision process and develop a deep-reinforcement-learning-based algorithm for solving it. Numerical results show that the proposed algorithm significantly outperforms benchmark policies.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl