A lifting approach to learning-based self-triggered control with Gaussian processes

References

• Heemels W, Johansson K, Tabuada P. An introduction to event-triggered and self-triggered control. In: Proceedings of the 51st IEEE Conference on Decision and Control (IEEE CDC); 2012. p. 3270–3285.
   Google Scholar
• Peng C, Li F. A survey on recent advances in event-triggered communication and control. Inf Sci. 2018;457:113–125. doi: 10.1016/j.ins.2018.04.055
   Web of Science ®Google Scholar
• Mazo Jr M, Anta A, Tabuada P. An ISS self-triggered implementation of linear controllers. Automatica. 2010;46(8):1310–1314. doi: 10.1016/j.automatica.2010.05.009
   Web of Science ®Google Scholar
• Gommans T, Antunes D, Donkers T, et al. Self-triggered linear quadratic control. Automatica. 2014;50(4):1279–1287. doi: 10.1016/j.automatica.2014.02.030
   Web of Science ®Google Scholar
• Hashimoto K, Adachi S, Dimarogonas DV. Event-triggered intermittent sampling for nonlinear model predictive control. Automatica. 2017;81:148–155. doi: 10.1016/j.automatica.2017.03.028
   Web of Science ®Google Scholar
• Luo B, Yang Y, Liu D, et al. Event-triggered optimal control with performance guarantees using adaptive dynamic programming. IEEE Trans Neural Netw Learn Syst. 2019;31(1):76–88. doi: 10.1109/TNNLS.5962385
   PubMed Web of Science ®Google Scholar
• Lapandić D, Persson L, Dimarogonas DV, et al. Aperiodic communication for mpc in autonomous cooperative landing. IFAC-Pap. 2021;54(6):113–118. 7th IFAC Conference on Nonlinear Model Predictive Control NMPC 2021.
   Google Scholar
• Hashimoto K, Saoud A, Kishida M, et al. A symbolic approach to the self-triggered design for networked control systems. IEEE Control Syst Lett. 2019;3(4):1050–1055. doi: 10.1109/LCSYS.7782633
   Google Scholar
• Hashimoto K, Yoshimura Y, Ushio T. Learning self-triggered controllers with Gaussian processes. IEEE Trans Cybern. 2020;51(12):6294–6304. doi: 10.1109/TCYB.2020.2980048
   Web of Science ®Google Scholar
• Wang R, Takeuchi I, Kashima K. Deep reinforcement learning for continuous-time self-triggered control. IFAC-Pap. 2021;54(14):203–208.
   Google Scholar
• Baumann D, Zhu JJ, Martius G, et al. Deep reinforcement learning for event-triggered control. In: Proceedings of 57th IEEE Conference on Decision and Control (IEEE CDC); 2018. p. 943–950.
   Google Scholar
• Wang J, Zhang H, Ma K, et al. Neural adaptive self-triggered control for uncertain nonlinear systems with input hysteresis. IEEE Trans Neural Netw Learn Syst. 2021;33(11):6206–6214. doi: 10.1109/TNNLS.2021.3072784
   Web of Science ®Google Scholar
• Sahoo A, Xu H, Jagannathan S. Neural network-based event-triggered state feedback control of nonlinear continuous-time systems. IEEE Trans Neural Netw Learn Syst. 2016;27(3):497–509. doi: 10.1109/TNNLS.2015.2416259
   PubMed Web of Science ®Google Scholar
• Vamvoudakis KG, Mojoodi A, Ferraz H. Event-triggered optimal tracking control of nonlinear systems. Int J Robust Nonlinear Control. 2017;27(4):598–619. doi: 10.1002/rnc.v27.4
   Web of Science ®Google Scholar
• Umlauft J, Hirche S. Feedback linearization based on Gaussian processes with event-triggered online learning. IEEE Trans Automat Contr. 2020;65(10):4154–4169. doi: 10.1109/TAC.9
   Web of Science ®Google Scholar
• Xue S, Luo B, Liu D, et al. Event-triggered ADP for tracking control of partially unknown constrained uncertain systems. IEEE Trans Cybern. 2022;52(9):9001–9012. doi: 10.1109/TCYB.2021.3054626
   PubMed Web of Science ®Google Scholar
• Wang Q, Cao J, Liu H. Event-triggered adaptive fuzzy PI control of uncertain fractional-order nonlinear systems with full-state constraints. IEEE Trans Emerg Topics Comput Intell. 2023;7(3):900–911. doi: 10.1109/TETCI.2022.3213708
   Google Scholar
• Deisenroth MP, Fox D, Rasmussen CE. Gaussian processes for data-efficient learning in robotics and control. IEEE Trans Pattern Anal Mach Intell. 2013;37(2):408–423. doi: 10.1109/TPAMI.2013.218
   Web of Science ®Google Scholar
• Nesić D, Teel AR. Sampled-data control of nonlinear systems: an overview of recent results. Perspectives in robust control; 2007. p. 221–239.
   Google Scholar