Stochastic Linearization of Feedback Systems with Multivariate Nonlinearities and Systems with State Multiplicative Noise

Author(s):  
Sarnaduti Brahma ◽  
Hamid Ossareh
2019 ◽  
Author(s):  
Péter Gáspár ◽  
Zoltán Szabó ◽  
József Bokor
Keyword(s):  

Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).


2020 ◽  
Vol 38 (9A) ◽  
pp. 1342-1351
Author(s):  
Musadaq A. Hadi ◽  
Hazem I. Ali

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.


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