An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
Automatica (Journal of IFAC)
Adaptive Digital Control of Hammerstein Nonlinear Systems with Limited Output Sampling
SIAM Journal on Control and Optimization
Self-tuning control based on generalized minimum variance criterion for auto-regressive models
Automatica (Journal of IFAC)
Adaptive Filtering Prediction and Control
Adaptive Filtering Prediction and Control
Brief paper: Dual-rate adaptive control
Automatica (Journal of IFAC)
A blind approach to the Hammerstein-Wiener model identification
Automatica (Journal of IFAC)
Brief Analysis of dual-rate inferential control systems
Automatica (Journal of IFAC)
Combined parameter and output estimation of dual-rate systems using an auxiliary model
Automatica (Journal of IFAC)
Identification of Hammerstein nonlinear ARMAX systems
Automatica (Journal of IFAC)
Hi-index | 0.02 |
A polynomial transformation technique is used to obtain a model for a dual-rate nonlinear system in which the output sampling interval is an integer multiple of the control interval. Based on this model, a self-tuning control algorithm is presented by minimizing output tracking error criteria from directly the dual-rate measurement data. The self-tuning algorithm proposed can achieve virtually asymptotically optimal control and ensure the closed-loop system to be stable and globally convergent. The proposed algorithm is illustrated by examples.