Nonlinear multivariable adaptive control using multiple models and neural networks |
| |
Authors: | Yue Fu [Author Vitae] [Author Vitae] |
| |
Affiliation: | a Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang 110004, China b Research Center of Automation, Northeastern University, Shenyang 110004, China |
| |
Abstract: | In this paper, a multivariable adaptive control approach is proposed for a class of unknown nonlinear multivariable discrete-time dynamical systems. By introducing a k-difference operator, the nonlinear terms of the system are not required to be globally bounded. The proposed adaptive control scheme is composed of a linear adaptive controller, a neural-network-based nonlinear adaptive controller and a switching mechanism. The linear controller can assure boundedness of the input and output signals, and the neural network nonlinear controller can improve performance of the system. By using the switching scheme between the linear and nonlinear controllers, it is demonstrated that improved performance and stability can be achieved simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method. |
| |
Keywords: | Adaptive control Multiple models Neural networks Switching Multivariable system Nonlinear system |
本文献已被 ScienceDirect 等数据库收录! |
|