Optimal Controller Switching for Resource-constrained Dynamical Systems
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In this paper, we present the resource-optimal controller switching synthesis for dynamical systems subject to resource constraints. Particularly, for systems having limited computational power (CPU) and onboard energy (battery), it is crucial to keep resource usage as low as possible. Although restrictions on resource utilization may save a CPU time and battery life, it degrades system performance. This paper provides three distinct algorithms that synthesize a controller switching policy for the purpose of resource savings, while not debasing system performance significantly. To measure system performance, we adopted the Waserstein distance that quantifies uncertainty in a probability density function level. The cost function to minimize is then defined based on this Wasserstein metric with a resource utilization penalty. As an example, quadrotor dynamics with two controllers, high performing / high resource consuming and moderate performing / resource saving controllers, is presented. The efficiency and usefulness of the proposed methods are validated in this example.
KeywordsOptimal controller switching resource-constrained system switched system Wasserstein distance
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- F. Vahid and T. Givargis, Embedded System Design: A Unified Hardware/software Introduction, vol. 4. John Wiley & Sons, New York, NY, 2002.Google Scholar
- S. Chakraborty, S. Künzli, and L. Thiele, “A general framework for analysing system properties in platform-based embedded system designs.,” DATE, vol. 3, p. 10190, Citeseer, 2003.Google Scholar
- T. Liu, C. M. Sadler, P. Zhang, and M. Martonosi, “Timplementing software on resource-constrained mobile sensors: experiences with impala and zebrane,” Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services, pp. 256–269, ACM, 2004.Google Scholar
- H. Wang, P. X. Liu, and P. Shi, “Observer-based fuzzy adaptive output-feedback control of stochastic nonlinear multiple time-delay systems,” IEEE Transactions on Cybernetics, 2017.Google Scholar
- K. Kogiso and K. Hirata, “Controller switching strategies for constrained mechanical systems with applications to the remote control over networks,” Proceedings of IEEE International Conference on Control Applications, vol. 1, pp. 480–484, IEEE, 2004. [click]Google Scholar
- K. Lee and R. Bhattacharya, “Optimal switching synthesis for jump linear systems with gaussian initial state uncertainty,” ASME 2014 Dynamic Systems and Control Conference, pp. V002T24A003–V002T24A003, American Society of Mechanical Engineers, 2014.Google Scholar
- D. Zhang, Z. Xu, H. R. Karimi, and Q.-G. Wang, “Distributed filtering for switched linear systems with sensor networks in presence of packet dropouts and quantization,” IEEE Transactions on Circuits and Systems I: Regular Papers, 2017.Google Scholar
- B. Niu, H. R. Karimi, H. Wang, and Y. Liu, “Adaptive output-feedback controller design for switched nonlinear stochastic systems with a modified average dwell-time method,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017.Google Scholar
- M. Rabbat and R. Nowak, “Distributed optimization in sensor networks,” Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, pp. 20–27, ACM, 2004.Google Scholar
- L. Xiao, S. Boyd, and S. Lall, “A scheme for robust distributed sensor fusion based on average consensus,” The Fourth International Symposium on Information Processing in Sensor Networks, IPSN 2005, pp. 63–70, IEEE, 2005. [click]Google Scholar