A new approach to convergence analysis of RLS-based self-tuning stochastic control - Niederliński, Antoni (Zielona Góra: Uniwersytet Zielonogórski)
It is demonstrated that a recently derived bound on the error convergence rate in (open-loop) RLS estimation is applicable to RLS-based stochastic self-tuning control as well. This leads to a generalized upper bound for the estimation error convergence rate in stochastic self-tuning control. The bound is shown to converge to zero under some assumptions regarding the model structure. The result is used to formulate two principles of self-tuning stating sufficient conditions under which self-tuning to stability and self-tuning to parameter consistency may occur.
Keywords: sterowanie; sterowanie-teoria; sztuczna inteligencja; matematyka stosowana; informatyka