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Self-tuning control based on multi-innovation stochastic gradient parameter estimation. (English) Zbl 1154.93040

Summary: This paper uses the Multi-Innovation Stochastic Gradient (MISG) algorithm to estimate the parameters of discrete-time systems, and presents an MISG based self-tuning control scheme. Furthermore, we prove that the parameter estimation error converges to zero under persistent excitation, and the parameter estimation based control algorithm can asymptotically achieve virtually optimal control, and ensure that the closed-loop systems are stable and globally convergent. A simulation example is included.

MSC:

93E10 Estimation and detection in stochastic control theory
93E12 Identification in stochastic control theory
93C40 Adaptive control/observation systems
93E25 Computational methods in stochastic control (MSC2010)
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