The vision of Industry 4.0, otherwise known as the fourth industrial revolution, is the integration of massively deployed smart computing and network technologies in industrial production and manufacturing settings for the purposes of automation, reliability, and control, implicating the development of an Industrial Internet of Things (I-IoT). Specifically, I-IoT is devoted to adopting the IoT to enable the interconnection of anything, anywhere, and at any time in the manufacturing system context to improve the productivity, efficiency, safety, and intelligence. As an emerging technology, I-IoT has distinct properties and requirements that distinguish it from consumer IoT, including the unique types of smart devices incorporated, network technologies and quality-of-service requirements, and strict needs of command and control. To more clearly understand the complexities of I-IoT and its distinct needs and to present a unified assessment of the technology from a systems’ perspective, in this paper, we comprehensively survey the body of existing research on I-IoT. Particularly, we first present the I-IoT architecture, I-IoT applications (i.e., factory automation and process automation), and their characteristics. We then consider existing research efforts from the three key system aspects of control, networking, and computing. Regarding control, we first categorize industrial control systems and then present recent and relevant research efforts. Next, considering networking, we propose a three-dimensional framework to explore the existing research space and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine communication, and software-defined networking. Similarly, concerning computing, we again propose a second three-dimensional framework that explores the problem space of computing in I-IoT and investigate the cloud, edge, and hybrid cloud and edge computing platforms. Finally, we outline particular challenges and future research needs in control, networking, and computing systems, as well as for the adoption of machine learning in an I-IoT context.

A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective

This paper first offers a transversal view through microfluidics theory and applications, starting from a brief overview on microfluidic systems and related theoretical issues, covering different kinds of phenomena, from continuous to multiphase flow. Multidimensional models, from lumped parameters to numerical models and computational solutions, are then considered as preliminary tools for the characterization of spatio-temporal dynamics in microfluidic flows. Following these, experimental approaches through original monitoring opto-electronic interfaces and systems are discussed. Finally, a vision of two phase microfluidic phenomena is given through nonlinear analyses applied to experimental time series.

Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control

Wireless networked control systems (WNCS) are composed of spatially distributed sensors, actuators, and con- trollers communicating through wireless networks instead of conventional point-to-point wired connections. Due to their main benefits in the reduction of deployment and maintenance costs, large flexibility and possible enhancement of safety, WNCS are becoming a fundamental infrastructure technology for critical control systems in automotive electrical systems, avionics control systems, building management systems, and industrial automation systems. The main challenge in WNCS is to jointly design the communication and control systems considering their tight interaction to improve the control performance and the network lifetime. In this survey, we make an exhaustive review of the literature on wireless network design and optimization for WNCS. First, we discuss what we call the critical interactive variables including sampling period, message delay, message dropout, and network energy consumption. The mutual effects of these communication and control variables motivate their joint tuning. We discuss the effect of controllable wireless network parameters at all layers of the communication protocols on the probability distribution of these interactive variables. We also review the current wireless network standardization for WNCS and their corresponding methodology for adapting the network parameters. Moreover, we discuss the analysis and design of control systems taking into account the effect of the interactive variables on the control system performance. Finally, we present the state-of-the-art wireless network design and optimization for WNCS, while highlighting the tradeoff between the achievable performance and complexity of various approaches. We conclude the survey by highlighting major research issues and identifying future research directions.

Wireless Network Design for Control Systems: A Survey

This paper investigated the problem of H∞output tracking for a type of discrete network-based control systems subjected to quantization and data dropout. The state of controlled system and the reference model state were quantized before being communicated. The quantization error was treated as sector-bounded uncertainty. The effect of the random packet loss was modelled as a Bernoulli process. Later, a sufficient condition was proposed for the existence of H∞tracking controllers in terms of linear matrix inequalities(LMIs). The proposed controller could guarantee the output of the closed-loop networked control system tracks that of a given reference model well in the H∞sense.Finally, a numerical example was prensented to show the effectiveness and usfulness of the proposed H∞output tracking design.

Network-Based H_∞ Output Tracking Control for a Type of Discrete Time Systems

Robust and optimal control of AC machines

Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network

Wireless sensor-actuator networks (WSANs) are being adopted in process industries because of their advantages in lowering deployment and maintenance costs. While there has been significant theoretical advancement in networked control design, only limited empirical results that combine control design with realistic WSAN standards exist. This paper presents a cyber-physical case study on a wireless process control system that integrates state-of-the-art network control design and a WSAN based on the WirelessHART standard. The case study systematically explores the interactions between wireless routing and control design in the process control plant. The network supports alternative routing strategies, including single-path source routing and multi-path graph routing. To mitigate the effect of data loss in the WSAN, the control design integrates an observer based on an Extended Kalman Filter with a model predictive controller and an actuator buffer of recent control inputs. We observe that sensing and actuation can have different levels of resilience to packet loss under this network control design. We then propose a flexible routing approach where the routing strategy for sensing and actuation can be configured separately. Finally, we show that an asymmetric routing configuration with different routing strategies for sensing and actuation can effectively improve control performance under significant packet loss. Our results highlight the importance of co- joining the design of wireless network protocols and control in wireless control systems.

https://www.cse.wustl.edu/~lu/papers/iccps16.pdf

Wireless routing and control: a cyber-physical case study

Wireless networked control system is gaining momentum in industrial cyber-physical systems, e.g., smart factory. Suffering from limited bandwidth and nondeterministic link quality, a critical challenge in its deployment is how to optimize the closed-loop control system performance as well as maintain stability. In order to bridge the gap between network design and control system performance, we propose an optimal dynamic scheduling strategy that optimizes performance of multi-loop control systems by allocating network resources based on predictions of both link quality and control performance at run-time. The optimal dynamic scheduling strategy boils down to solving a nonlinear integer programming problem, which is further relaxed to a linear programming problem. The proposed strategy provably renders the closed-loop system mean-square stable under mild assumptions. Its efficacy is demonstrated by simulating a four-loop control system over an IEEE 802.15.4 wireless network simulator - TOSSIM. Simulation results show that the optimal dynamic scheduling can enhance control system performance and adapt to both constant and variable network background noises as well as physical disturbance.

Optimal dynamic scheduling of wireless networked control systems

Wireless sensor-actuator networks (WSANs) are gaining momentum in industrial process automation as a communication infrastructure for lowering deployment and maintenance costs. In traditional wireless control systems, the plant controller and the network manager operate in isolation, which ignores the significant influence of network reliability on plant control performance. To enhance the dependability of industrial wireless control, we propose a holistic cyber-physical management framework that employs runtime coordination between the plant control and network management. Our design includes a holistic controller that generates actuation signals to physical plants and reconfigures the WSAN to maintain the desired control performance while saving wireless resources. As a concrete example of holistic control, we design a holistic manager that dynamically reconfigures the number of transmissions in the WSAN based on online observations of physical and cyber variables. We have implemented the holistic management framework in the wireless cyber-physicalsimulator (WCPS). A systematic case study is presented based on two five-state plants and a load positioning system using a 16-node WSAN. Simulation results show that the holistic management design has significantly enhanced the dependability of the system against both wireless interferences and physical disturbances, while effectively reducing the number of wireless transmissions.

https://dl.acm.org/doi/pdf/10.1145/3185510

Holistic Cyber-Physical Management for Dependable Wireless Control Systems

Process automation is embracing wireless sensor-actuator networks (WSANs) in the era of Industrial Internet. Despite the success of WSANs for monitoring applications, feedback control poses significant challenges due to data loss and stringent energy constraints in WSANs. Holistic control adopts a cyber-physical system approach to overcome the challenges by orchestrating network reconfiguration and process control at run time. In this paper, we explore efficient holistic control designs to maintain control performance while reducing the communication cost. The contributions of this work are four-fold: (1) We introduce a holistic control architecture that integrates low-power wireless bus (LWB) and two control strategies, rate adaptation and self-triggered control, specifically proposed to reduce communication cost; (2) We design novel wireless network mechanisms to support rate adaptation and self-triggered control, respectively, in a multi-hop WSAN; (3) We build a real-time network-in-the-loop simulator that integrates MATLAB/Simulink and a three-floor WSAN testbed to evaluate wireless control systems; (4) We empirically explore the tradeoff between communication cost and control performance under alternative holistic control approaches. Our case studies show that rate adaptation and self-triggered control offer advantages in control performance and energy efficiency, respectively, in normal operating conditions. The advantage in energy efficiency of self-triggered control, however, may diminish under harsh physical and wireless conditions due to the cost of recovering from data loss and physical disturbances.

Yehan Ma

Papers

Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network

Wireless routing and control: a cyber-physical case study

Optimal dynamic scheduling of wireless networked control systems

Holistic Cyber-Physical Management for Dependable Wireless Control Systems

Efficient Holistic Control over Industrial Wireless Sensor-Actuator Networks