Bootstrap control

• Published in: IEEE transactions on automatic control Vol. 51; no. 1; pp. 28 - 37
• Main Authors: Aronsson, M., Arvastson, L., Holst, J., Lindoff, B., Svensson, A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-01-2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Computer science; control theory; systems; control; Control systems; Control theory. Systems; Estimates; Exact sciences and technology; Feedback loop; Generalized predictive control; Matematik; Mathematical analysis; Mathematics; Modelling and identification; Natural Sciences; Naturvetenskap; Noise; Open loop systems; Optimal control; Optimization; Parameter estimation; Probability Theory and Statistics; Process control; quality control; resampling; Sannolikhetsteori och statistik; statistical bootstrap techniques; Statistical distributions; statistical process; statistical process control; stochastic control; Stochastic processes; Stochastic systems; Time varying systems; Uncertainty; Statistical process control; Statistical analysis; Probabilistic approach; Statistical distribution; Generalized predictive control; Set computation; Linear control; Stochastic system; resampling; statistical bootstrap techniques; Statistical method; Loss function; Time varying system; Uncertain system; Optimal control; Quality control; Distribution function; Bootstrap; Stochastic control; Predictive control
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2005.861722

In this paper, we present a new way to control linear stochastic systems. The method is based on statistical bootstrap techniques. The optimal future control signal is derived in such a way that unknown noise distribution and uncertainties in parameter estimates are taken into account. This is achieved by resampling from existing data when calculating statistical distributions of future process values. The bootstrap algorithm takes care of arbitrary loss functions and unknown noise distribution even for small estimation sets. The efficient way of utilizing data implies that the method is also well suited for slowly time-varying stochastic systems.