Neuroadaptive finite-time output feedback control for PMSM stochastic nonlinear systems with iron losses via dynamic surface technique

2020 ◽  
Vol 402 ◽  
pp. 162-170
Author(s):  
Shuai Cheng ◽  
Jinpeng Yu ◽  
Chong Lin ◽  
Lin Zhao ◽  
Yumei Ma
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Output Feedback ◽  
Finite Time ◽  
Output Feedback Control ◽  
Stochastic Nonlinear Systems ◽  
Dynamic Surface ◽  
Iron Losses ◽  
Surface Technique

scholarly journals Adaptive Neural Output Feedback Control of Stochastic Nonlinear Systems with Unmodeled Dynamics

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaonan Xia ◽  
Tianping Zhang
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Output Feedback ◽  
Output Feedback Control ◽  
Weight Vector ◽  
Dynamic Surface Control ◽  
Unmodeled Dynamics ◽  
Stochastic Nonlinear Systems ◽  
Dynamic Surface ◽  
The Neural Networks

An adaptive neural output feedback control scheme is investigated for a class of stochastic nonlinear systems with unmodeled dynamics and unmeasured states. The unmeasured states are estimated by K-filters, and unmodeled dynamics is dealt with by introducing a novel description based on Lyapunov function. The neural networks weight vector used to approximate the black box function is adjusted online. The unknown nonlinear system functions are handled together with some functions resulting from theoretical deduction, and such method effectively reduces the number of adaptive tuning parameters. Using dynamic surface control (DSC) technique, Itô formula, and Chebyshev’s inequality, the designed controller can guarantee that all the signals in the closed-loop system are bounded in probability, and the error signals are semiglobally uniformly ultimately bounded in mean square or the sense of four-moment. Simulation results are provided to verify the effectiveness of the proposed approach.

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