Iterative state and parameter estimation algorithms for bilinear state-space systems by using the block matrix inversion and the hierarchical principle

2021 ◽  
Author(s):  
Siyu Liu ◽  
Feng Ding ◽  
Erfu Yang
Keyword(s):  
Parameter Estimation ◽  
State Space ◽  
Matrix Inversion ◽  
Block Matrix ◽  
Space Systems ◽  
Estimation Algorithms ◽  
State Space Systems

scholarly journals Multi-Innovation Stochastic Gradient Parameter and State Estimation Algorithm for Dual-Rate State-Space Systems with d-Step Time Delay

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ya Gu ◽  
Quanmin Zhu ◽  
Jicheng Liu ◽  
Peiyi Zhu ◽  
Yongxin Chou
Keyword(s):  
Parameter Estimation ◽  
Time Delay ◽  
State Space ◽  
Estimation Algorithm ◽  
The State ◽  
Stochastic Gradient ◽  
Parameter Estimates ◽  
Space Systems ◽  
Gradient Parameter ◽  
State Space Systems

This paper presents a multi-innovation stochastic gradient parameter estimation algorithm for dual-rate sampled state-space systems with d-step time delay by the multi-innovation identification theory. Considering the stochastic disturbance in industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. The difficulty of identification is that the information vector in the identification model contains the unknown states. The proposed algorithm uses the state estimates of the observer instead of the state variables to realize the parameter estimation. The simulation results indicate that the proposed algorithm works well.

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