Hybrid deep learning controller for nonlinear systems based on adaptive learning rates

Abstract

In this paper, a hybrid deep learning neural network controller (HDLNNC) for nonlinear systems is proposed. The proposed controller structure consists of a multi-layer feed-forward neural network, which can be trained based on the hybrid deep learning. The Lyapunov stability criterion is used to develop an adaptive learning rate due to the learning rate of the updating parameters plays a worthy role in achieving the stability of a system. To show the robustness of the proposed controller and its performance, several tests such as disturbance signals and parameter variations are carried on a numerical example. In this concern, the practical implementation of the proposed HDLNNC is executed on a real system. The results indicate that the proposed controller is able to improve the system performance compared with other existing controllers.

KEYWORDS:

• Lyapunov stability
• hybrid deep learning
• nonlinear systems
• Hebbian learning rule
• self-organised map
• Kohonen procedure

Disclosure statement

There is no conflict of interest between the authors to publish this manuscript. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.