Abstract
A new algorithm for hybrid state estimation, the K-Limited Mode-Change (KLMC) algorithm, is presented. Given noisy measurements, this algorithm estimates the continuous and discrete state histories for a class of hybrid systems that exhibit limited mode changes over time. The KLMC algorithm is compared to an existing hybrid state estimator, the Interacting Multiple Model (IMM), using a newly developed performance metric based on the concept of probability of error. Monte Carlo methods are used to obtain numerical estimates of the performance metric for simple hybrid system models. Simulation results show that KLMC outperforms IMM in terms of the estimate-error metric but requires larger storage and computational resource consumption.
This research was supported by ONR under the CoMotion MURI contract N00014-02-1-0720, by NASA JUP under grant NAG2-1564, and by NASA grant NCC2-5536.
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Roy, K., Tomlin, C.J. (2007). A New Hybrid State Estimator for Systems with Limited Mode Changes. In: Bemporad, A., Bicchi, A., Buttazzo, G. (eds) Hybrid Systems: Computation and Control. HSCC 2007. Lecture Notes in Computer Science, vol 4416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71493-4_38
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DOI: https://doi.org/10.1007/978-3-540-71493-4_38
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