SCIENCE CHINA Information Sciences, Volume 61 , Issue 1 : 012202(2018) https://doi.org/10.1007/s11432-016-9073-y

Optimal control data scheduling with limited controller-plant communication

More info
  • ReceivedNov 7, 2016
  • AcceptedApr 20, 2017
  • PublishedAug 25, 2017


This paper considers optimal control data scheduling for finite-horizon linear quadratic regulation (LQR) control of scalar systems with limited controller-plant communication. Both the single-system and multiple-system scenarios are studied. For the first scenario, we derive the necessary and sufficient condition for a comparison function to be positive. Using this condition, the optimality of an explicit schedule is extended from unstable systems in the existing work to general systems. For the second scenario, we are able to construct explicit optimal scheduling policies for three particular classes of problems. Numerical examples are provided to illustrate the proposed results.


This work was supported by National Natural Science Foundation of China (Grant Nos. U1509203, 61333011, U1664264, 61603133).


[1] Lee E A, Seshia S A. Introduction to Embedded Systems: a Cyber-Physical Systems Approach. LeeSeshia.org, 2015. Google Scholar

[2] Wang L Y, Guo G, Zhuang Y. Stabilization of NCSs by random allocation of transmission power to sensors. Sci China Inf Sci, 2016, 59: 067201. Google Scholar

[3] Liu Q, Wang Z, He X, et al. Event-based distributed filtering with stochastic measurement fading. IEEE Trans Ind Informat, 2015, 11: 1643--1652. Google Scholar

[4] Liu H, Guo D, Sun F. Object recognition using tactile measurements: kernel sparse coding methods. IEEE Trans Instrum Meas, 2016, 65: 656--665. Google Scholar

[5] Liu H, Liu Y, Sun F. Robust exemplar extraction using structured sparse coding. IEEE Trans Neural Netw Learn Syst, 2015, 26: 1816--1821. Google Scholar

[6] Gaid M E M B, Cela A S, Hamam Y. Optimal real-time scheduling of control tasks with state feedback resource allocation. IEEE Trans Control Syst Tech, 2009, 17: 309--326. Google Scholar

[7] Sui T, You K, Fu M. Optimal sensor scheduling for state estimation over lossy channel. IET Control Theory Appl, 2015, 9: 2458--2465. Google Scholar

[8] He L, Han D, Wang X. Optimal periodic scheduling for remote state estimation under sensor energy constraint. IET Control Theory Appl, 2014, 8: 907--915. Google Scholar

[9] Sinopoli B, Schenato L, Franceschetti M, et al. Kalman filtering with intermittent observations. IEEE Trans Autom Control, 2004, 49: 1453--1464. Google Scholar

[10] You K, Xie L. Minimum data rate for mean square stabilizability of linear systems with markovian packet losses. IEEE Trans Autom Control, 2011, 56: 772--785. Google Scholar

[11] Wen S, Guo G. Control and resource allocation of cyber-physical systems. IET Control Theory Appl, 2016, 10: 2038--2048. Google Scholar

[12] Lu Z B, Guo G. Communications and control co-design: a combined dynamic-static scheduling approach. Sci China Inf Sci, 2012, 55: 2495--2507. Google Scholar

[13] Guo G, Lu Z, Han Q L. Control with Markov sensors/actuators assignment. IEEE Trans Autom Control, 2012, 57: 1799--1804. Google Scholar

[14] Joshi S, Boyd S. Sensor selection via convex optimization. IEEE Trans Signal Process, 2009, 57: 451--462. Google Scholar

[15] Mo Y, Ambrosino R, Sinopoli B. Sensor selection strategies for state estimation in energy constrained wireless sensor networks. Automatica, 2011, 47: 1330--1338. Google Scholar

[16] Imer O C, Bac sar T. Optimal control with limited controls. In: Proceedings of American Control Conference, Minneapolis, 2006. 298--303. Google Scholar

[17] Bommannavar P, Bac sar T. Optimal control with limited control actions and lossy transmissions. In: Proceedings of IEEE Conference Decision and Control, Cancun, 2008. 2032--2037. Google Scholar

[18] Lincoln B, Bernhardsson B. LQR optimization of linear system switching. IEEE Trans Autom Control, 2002, 47: 1701--1705. Google Scholar

[19] Savage C O, La Scala B F. Optimal scheduling of scalar Gauss-Markov systems with a terminal cost function. IEEE Trans Autom Control, 2009, 54: 1100--1105. Google Scholar

[20] Yang C, Shi L. Deterministic sensor data scheduling under limited communication resource. IEEE Trans Signal Process, 2011, 59: 5050--5056. Google Scholar

[21] Ren Z, Cheng P, Chen J, et al. Optimal periodic sensor schedule for steady-state estimation under average transmission energy constraint. IEEE Trans Autom Control, 2013, 58: 3265--3271. Google Scholar

[22] Ren Z, Cheng P, Chen J, et al. Dynamic sensor transmission power scheduling for remote state estimation. Automatica, 2014, 50: 1235--1242. Google Scholar

[23] Howard S, Suvorova S, Moran B. Optimal policy for scheduling of Gauss-Markov systems. In: Proceedings of the 7th International Conference on Information Fusion, Stockholm, 2004. 888--892. Google Scholar

[24] La Scala B F, Moran B. Optimal target tracking with restless bandits. Digital Signal Process, 2006, 16: 479--487. Google Scholar

[25] Cabrera J B D. A note on greedy policies for scheduling scalar Gauss-Markov systems. IEEE Trans Autom Control, 2011, 56: 2982--2986. Google Scholar

[26] Shi L, Zhang H. Scheduling two Gauss-Markov systems: an optimal solution for remote state estimation under bandwidth constraint. IEEE Trans Signal Process, 2012, 60: 2038--2042. Google Scholar

[27] Shi L, Yuan Y, Chen J. Finite horizon LQR control with limited controller-system communication. IEEE Trans Autom Control, 2013, 58: 1835--1841. Google Scholar

[28] Horn R A, Johnson C R. Matrix Analysis. 2nd ed. Cambridge: Cambridge University Press, 2012. 493--504. Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备17057255号       京公网安备11010102003388号