Parameter estimation for a controlled autoregressive autoregressive moving average system based on a recursive framework

TL;DR: A method to utilize the estimation error information, and to establish an adaptive estimator by combining the parameter initial error information are introduced to improve the estimation bias phenomenon.

TL;DR: In this paper , a recursive identification algorithm for an expanded-sandwich system is proposed, in which an estimator is developed on the basis of parameter identification error data rather than the traditional prediction error output information.

TL;DR: A novel self-error learning framework-based algorithm is proposed for parameter recovery of extended Wiener-Hammerstein systems with hysteresis nonlinearity under quantized measurements, achieving high-performance estimation with adaptive filtering and online compensation.

TL;DR: It is shown that the parameter estimation error consistently converges to zero under generalized or weak persistent excitation conditions and unbounded noise variance, and that the output estimates uniformly converge to the true outputs.

TL;DR: A hierarchical gradient iterative algorithm and a hierarchical stochastic gradient algorithm are proposed and it is proved that the parameter estimation errors given by the algorithms converge to zero for any initial values under persistent excitation.

TL;DR: A novel reaching law is designed based on hyperbolic functions to guarantee that the sliding mode variable infinitely approaches to the equilibrium point instead of crossing it, so that the fast convergence and chattering-free property can be achieved simultaneously.

TL;DR: A hierarchical Newton recursive algorithm is proposed, which can realize the on-line parameter estimation and overcome the existence of the singular matrix during the Newton search and to make the algorithm more stable, a positive definite diagonal matrix is introduced to the algorithm.