Multi-innovation stochastic gradient algorithms for dual-rate sampled systems with preload nonlinearity. (English) Zbl 1251.93130
Summary: Since the stochastic gradient algorithm has a slower convergence rate, this letter presents a multi-innovation stochastic gradient algorithm for a class of dual-rate sampled systems with preload nonlinearity. The basic idea is to transform the dual-rate system model into an identification model which can use dual-rate data by using the polynomial transformation technique. A simulation example is provided to verify the effectiveness of the proposed method.
MSC:
93E12 | Identification in stochastic control theory |
93C57 | Sampled-data control/observation systems |
93E25 | Computational methods in stochastic control (MSC2010) |