Deterministic learning enhanced neutral network control of unmanned helicopter. (15th November 2016)

• Record Type: Journal Article
• Authors: Jiang, Yiming; Yang, Chenguang; Dai, Shi-lu; Ren, Beibei
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 6(2016)
• Publication Date: 2016-11-15
• Subjects: Unmanned helicopter -- global neural control -- deterministic learning; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416671118
• Languages: English
• ISSNs: 1729-8806

Abstract

In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design.