Dynamic Self-Triggered Controller Codesign for Markov Jump Systems

Adaptive Control & Signal

et al. 2020

Because of the product item of the control input and the state vector, the identification of bilinear systems is difficult. This paper considers the combined parameter and state estimation problems of bilinear state-space systems. On the basis of the observability canonical form and the model transformation, an identification model with a linear combination of the system parameters is obtained. Using the hierarchical principle, the identification model is decomposed into three submodels with fewer variables, and a three-stage least squares-based iterative (3S-LSI) algorithm is presented to estimate the system parameters. Furthermore, we derive a state estimator (SE) for estimating the unknown states, and present an SE-3S-LSI algorithm for estimating the unknown parameters and states simultaneously. After that, the least squares-based iterative algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can identify the bilinear system effectively but the 3S-LSI algorithm can improve the computational efficiency. The simulation results indicate the effectiveness of the proposed algorithms.

Section: Discussionmentioning

confidence: 99%

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Liu

Zhang

Adaptive Control & Signal

et al. 2020

“…Some identification methods are derived based on the identification models of the systems 38,39,40,41 and can be used to estimate the parameters of other linear systems and nonlinear systems. 42,43,44,45,46,47 The objective of this article is to propose a parameter estimation algorithm based on this identification model through constructing a cost function and optimizing it.…”

Section: Nonlinear Sandwich System and Identification Modelmentioning

confidence: 99%

Auxiliary model multiinnovation stochastic gradient parameter estimation methods for nonlinear sandwich systems

Intl J Robust & Nonlinear

Yang

2020

157

This article studies the identification problem of the nonlinear sandwich systems. For the sandwich system, because there are inner variables which cannot be measured in the information vector of the identification models, it is difficult to identify the nonlinear sandwich systems. In order to overcome the difficulty, an auxiliary model is built to predict the estimates of inner variables by means of the output of the auxiliary model. For the purpose of employing the real-time observed data, a cost function with dynamical data is constructed to capture on-line information of the nonlinear sandwich system. On this basis, an auxiliary model stochastic gradient identification approach is proposed based on the gradient optimization. Moreover, an auxiliary model multiinnovation stochastic gradient estimation method is developed, which tends to enhance estimation accuracy by introducing more observed data dynamically. The numerical simulation is provided and the simulation results show that the proposed auxiliary model identification method is effective for the nonlinear sandwich systems.

“…The proposed algorithms in this article are based on this identification model. Many identification methods are derived based on the identification models of the systems [32][33][34][35] and can be used to estimate the parameters of other linear systems and nonlinear systems [36][37][38][39] and can be applied to other fields [40][41][42][43] such as chemical process control systems.…”

Section: Problem Statementmentioning

confidence: 99%

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

Cui

Adaptive Control & Signal

et al. 2020

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.