Yehan Ma
Chenyang Lu
Skip Abstract Section
Abstract
Industrial automation is embracing wireless sensor-actuator networks (WSANs). 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. Fundamentally, it leverages self-awareness across control and wireless boundaries to enhance the resiliency of wireless control systems. In this article, we explore efficient holistic control designs to maintain control performance while reducing the communication cost. The contributions of this work are five-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; (2) We present heuristics-based and optimal rate selection algorithms for rate adaptation; (3) We design novel network adaptation mechanisms to support rate adaptation and self-triggered control in a multi-hop WSAN; (4) We build WCPS-RT, a real-time network-in-the-loop simulator that integrates MATLAB/Simulink and a physical WSAN testbed to evaluate wireless control systems; (5) We empirically explore the tradeoff between communication cost and control performance in holistic control approaches. Our 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.
- Pangun Park et al. 2018. Wireless network design for control systems: A survey. Commun. Surv. Tut. 20, 2 (2018).Google Scholar
- ABB. 2020. WirelessHART Networks: 7 Myths That Cloud Their Consideration for Process Control Measurement Made Easy. Retrieved from https://new.abb.com/products/measurement-products/pl/produkty-i-rozwiazania-wireless/wirelesshart-networks-seven-myths-that-cloud-their-consideration-for-process-controll-en.Google Scholar
- Emerson. 2017. White Paper: Emerson Wireless Security – WirelessHart and Wi-Fi. Retrieved from https://www.emerson.com/documents/automation/white-paper-emerson-wireless-security-wirelesshart-wi-fi-security-deltav-en-41260.pdf.Google Scholar
- Yehan Ma et al. 2018. Holistic cyber-physical management for dependable wireless control systems. ACM Trans. Cyber-Phys. Syst. 3, 1 (2018).Google Scholar
- Federico Ferrari et al. 2012. Low-power wireless bus. In Proceedings of the ACM Conference on Embedded Network Sensor Systems.Google Scholar
- Xiaomin Li et al. 2017. A review of industrial wireless networks in the context of Industry 4.0. Wirel. Netw. 23, 1 (2017).Google Scholar
- Chenyang Lu et al. 2016. Real-time wireless sensor-actuator networks for industrial cyber-physical systems. Proc. IEEE 104, 5 (2016).Google Scholar
- Bruno Sinopoli et al. 2004. Kalman filtering with intermittent observations. IEEE Trans. Autom. Control 49, 9 (2004).Google Scholar
- Manuel Mazo et al. 2008. On event-triggered and self-triggered control over sensor/actuator networks. In Proceedings of the IEEE Conference on Decision and Control.Google Scholar
- Manuel Mazo et al. 2011. Decentralized event-triggered control over wireless sensor/actuator networks. IEEE Trans. Autom. Contr. 56, 10 (2011).Google Scholar
- Manuel Mazo Jr et al. 2010. An ISS self-triggered implementation of linear controllers. Automatica 46, 8 (2010).Google Scholar
- José Araújo et al. 2014. System architectures, protocols and algorithms for aperiodic wireless control systems. IEEE Trans. Ind. Inf. 10, 1 (2014).Google Scholar
- Pangun Park et al. 2011. Breath: An adaptive protocol for industrial control applications using wireless sensor networks. IEEE Trans. Mob. Comput. 10, 6 (2011).Google Scholar
- Felix Dobslaw et al. 2016. End-to-end reliability-aware scheduling for wireless sensor networks. IEEE Trans. Ind. Inf. 12, 2 (2016).Google Scholar
- Hyung-Sin Kim et al. 2017. Load balancing under heavy traffic in RPL routing protocol for low power and lossy networks. IEEE Trans. Mob. Comput. 16, 4 (2017).Google ScholarDigital Library
- Marco Zimmerling et al. 2017. Adaptive real-time communication for wireless cyber-physical systems. ACM Trans. Cyber-Phys. Syst. 1, 2 (2017).Google Scholar
- Zheng Li et al. 2006. Adaptive multiple sampling rate scheduling of real-time networked supervisory control system-part II. In Proceedings of the Annual Conference on IEEE Industrial Electronics. IEEE.Google Scholar
- Dip Goswami et al. 2012. Time-triggered implementations of mixed-criticality automotive software. In Proceedings of the Conference on Design, Automation and Test in Europe. EDA Consortium.Google Scholar
- Burak Demirel et al. 2014. Modular design of jointly optimal controllers and forwarding policies for wireless control. IEEE Trans. Autom. Contr. 59, 12 (2014).Google Scholar
- Abusayeed Saifullah et al. 2014. Near optimal rate selection for wireless control systems. ACM Trans. Embed. Comput. Syst. 13, 4s (2014).Google Scholar
- Dohwan Kim et al. 2019. Sampling rate optimization for IEEE 802.11 wireless control systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.Google Scholar
- Bo Li et al. 2016. Wireless routing and control: A cyber-physical case study. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.Google Scholar
- Jia Bai et al. 2012. Optimal cross-layer design of sampling rate adaptation and network scheduling for wireless networked control systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.Google Scholar
- Lei Bao et al. 2009. Rate allocation for quantized control over noisy channels. In Proceedings of the International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. IEEE.Google Scholar
- J. Colandairaj et al. 2007. Wireless networked control systems with QoS-based sampling. IET Contr. Theor. Applic. 1, 1 (2007).Google Scholar
- Anton Cervin et al. 2003. How does control timing affect performance? Analysis and simulation of timing using Jitterbug and TrueTime. IEEE Contr. Syst. Mag. 23, 3 (2003).Google Scholar
- Emeka Eyisi et al. 2012. NCSWT: An integrated modeling and simulation tool for networked control systems. Simul. Model. Pract. Theor. 27 (2012).Google Scholar
- Kannan Srinivasan et al. 2010. An empirical study of low-power wireless. ACM Trans. Sens. Netw. 6, 2 (2010).Google Scholar
- Carlos Santos et al. 2015. Aperiodic linear networked control considering variable channel delays: Application to robots coordination. Sensors 15, 6 (2015).Google Scholar
- Bo Li et al. 2015. Incorporating emergency alarms in reliable wireless process control. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.Google Scholar
- Philip Levis et al. 2003. TOSSIM: Accurate and scalable simulation of entire TinyOS applications. In Proceedings of the International Conference on Embedded Networked Sensor Systems. ACM.Google Scholar
- Miroslav Pajic et al. 2012. Closing the loop: A simple distributed method for control over wireless networks. In Proceedings of the ACM/IEEE International Conference on Information Processing in Sensor Networks. IEEE.Google Scholar
- Fabian Mager et al. 2019. Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.Google Scholar
- Dominik Baumann et al. 2018. Evaluating low-power wireless cyber-physical systems. In Proceedings of the IEEE Workshop on Benchmarking Cyber-Physical Networks and Systems.Google Scholar
- Dominik Baumann et al. 2019. Control-guided communication: Efficient resource arbitration and allocation in multi-hop wireless control systems. IEEE Contr. Syst. Lett. 4, 1 (2019).Google Scholar
- Anders Ahlen et al. 2019. Toward wireless control in industrial process automation: A case study at a paper mill. IEEE Contr. Syst. Mag. 39, 5 (2019).Google Scholar
- Yehan Ma et al. 2019. Optimal dynamic scheduling of wireless networked control systems. In Proceedings of the ACM/IEEE International Conference on Cyber-Physical Systems.Google Scholar
- Kemin Zhou et al. 1996. Robust and Optimal Control. Prentice Hall, Upper Saddle River, NJ.Google Scholar
- Federico Ferrari et al. 2011. Efficient network flooding and time synchronization with glossy. In Proceedings of the International Conference on Information Processing in Sensor Networks. IEEE.Google Scholar
- Gang Zhao. 2011. Wireless sensor networks for industrial process monitoring and control: A survey. Netw. Prot. Algor. 3, 1 (2011).Google ScholarCross Ref
- Timofei Istomin et al. 2016. Data prediction+ synchronous transmissions= ultra-low power wireless sensor networks. In Proceedings of the ACM Conference on Embedded Network Sensor Systems. ACM.Google Scholar
- Tongwen Chen et al. 2012. Optimal Sampled-data Control Systems. Springer Science 8 Business Media.Google Scholar
- Bruce Francis. 1979. The optimal linear-quadratic time-invariant regulator with cheap control. IEEE Trans. Autom. Contr. 24, 4 (1979).Google ScholarCross Ref
- Hai Lin et al. 2009. Stability and stabilizability of switched linear systems: A survey of recent results. IEEE Trans. Autom. Contr. 54, 2 (2009).Google Scholar
- Wei Zhang et al. 2001. Stability of networked control systems. IEEE Contr. Syst. Mag. 21, 1 (2001).Google Scholar
- Huijun Gao et al. 2008. A new delay system approach to network-based control. Automatica 44, 1 (2008).Google Scholar
- Merid Lješnjanin et al. 2014. Packetized MPC with dynamic scheduling constraints and bounded packet dropouts. Automatica 50, 3 (2014).Google Scholar
- Jing Wu et al. 2007. Design of networked control systems with packet dropouts. IEEE Trans. Autom. Contr. 52, 7 (2007).Google Scholar
- Jamal Daafouz et al. 2002. Stability analysis and control synthesis for switched systems: A switched Lyapunov function approach. IEEE Trans. Autom. Contr. 47, 11 (2002).Google Scholar
- Daniel Liberzon et al. 1999. Basic problems in stability and design of switched systems. IEEE Contr. Syst. Mag. 19, 5 (1999).Google Scholar
- S.-H. Lee et al. 2000. A new stability analysis of switched systems. Automatica 36, 6 (2000).Google ScholarDigital Library
- Dragan Nesic et al. 2004. Input-output stability properties of networked control systems. IEEE Trans. Autom. Contr. 49, 10 (2004).Google Scholar
- Bin Hu et al. 2019. Co-design of safe and efficient networked control systems in factory automation with state-dependent wireless fading channels. Automatica 105 (2019).Google Scholar
- Simulink. 2020. Simulink Desktop Real-Time. Retrieved from https://www.mathworks.com/products/simulink-desktop-real-time.html.Google Scholar
- Mo Sha et al. 2015. Implementation and experimentation of industrial wireless sensor-actuator network protocols. In Proceedings of the European Conference on Wireless Sensor Networks. Springer.Google Scholar
- Mo Sha et al. 2017. Empirical study and enhancements of industrial wireless sensor–actuator network protocols. IEEE Internet Things J. (2017).Google Scholar
- PTP. 2020. PTP Protocol. Retrieved from https://www.nist.gov/el/intelligent-systems-division-73500/ieee-1588.Google Scholar
- Xiangheng Liu et al. 2004. Kalman filtering with partial observation losses. In Proceedings of the Conference on Decision and Control. IEEE.Google Scholar
- Yang Shi et al. 2010. Kalman filter-based identification for systems with randomly missing measurements in a network environment. Int. J. Contr. (2010).Google Scholar
- Vijay Shilpiekandula et al. 2012. Load positioning in the presence of base vibrations. In Proceedings of the American Control Conference.Google Scholar
- Yu Jiang et al. 2015. Optimal codesign of nonlinear control systems based on a modified policy iteration method. IEEE Trans. Neural Netw. Learn. Syst. (2015).Google Scholar
- Chayan Sarkar. 2016. LWB and FS-LWB implementation for Sky platform using Contiki. arXiv preprint arXiv:1607.06622 (2016).Google Scholar
- Adam Dunkels et al. 2007. Software-based on-line energy estimation for sensor nodes. In Proceedings of the Workshop on Embedded Networked Sensors. ACM.Google Scholar
- Chengjie Wu et al. 2016. Maximizing network lifetime of WirelessHART networks under graph routing. In Proceedings of the IEEE International Conference on Internet-of-Things Design and Implementation.Google Scholar
Index Terms
- Efficient Holistic Control: Self-awareness across Controllers and Wireless Networks
Recommendations
Feedback control goes wireless: guaranteed stability over low-power multi-hop networks
ICCPS '19: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical SystemsClosing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless technology is preferred for its low cost, ...
Holistic Cyber-Physical Management for Dependable Wireless Control Systems
Special Issue on Dependability in CPSWireless 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 ...
Flexible Energy Efficient Density Control on Wireless Sensor Networks
Heterogenous Wireless Ad Hoc and Sensor NetworksIn dense wireless sensor networks, density control is an important technique for prolonging the network's lifetime while providing sufficient sensing coverage. In this paper, we develop three new density control protocols by considering the tradeoff ...
Comments