Hybrid adaptive negative imaginary- neural-fuzzy control with model identification for a quadrotor. (June 2021)

• Record Type: Journal Article
• Authors: Tran, Vu Phi; Mabrok, Mohamed A.; Garratt, Matthew A.; Petersen, Ian R.
• Is Part Of: IFAC journal of systems and control. Volume 16(2021)
• Publication Date: 2021-06
• Subjects: Strictly Negative Imaginary controller -- Neural-Fuzzy controller -- Hybrid control -- Online identification -- Quadcopter unmanned aerial vehicle -- Uncertainties; Automatic control -- Periodicals; Relay control systems -- Periodicals; Embedded computer systems -- Periodicals; Feedback control systems -- Periodicals; Artificial intelligence -- Periodicals; Artificial intelligence; Automatic control; Embedded computer systems; Feedback control systems; Relay control systems; Electronic journals; Periodicals; 629.89
• DOI: 10.1016/j.ifacsc.2021.100156
• Languages: English
• ISSNs: 2468-6018

Abstract

Abstract: Quadrotor system is subject to multiple disturbances, including both internal and external effects (e.g. wind gusts, coupling effects, and unmodeled dynamics). For example, severe wind disturbances may significantly degrade trajectory tracking during the flight of autonomous aerial vehicles, or even cause loss of control or failure of a tracking mission. This paper introduces a robust hybrid control system, including a linear Strictly Negative Imaginary (SNI) controller and an adaptive nonlinear Neural-Fuzzy control law, to enable high-precision trajectory tracking tasks for a quadcopter drone. Based on a parallel form, both proposed controllers are able to enhance the transient performance, the system response, and the robustness of the quadcopter controllers. Also, a linear time-invariant SNI UAV dynamic model, in combination with an online adaptive residual nonlinear model using the neural network identification, is proposed to model the natural behavior of a quadcopter system. Through a series of numerical simulations, this paper highlights the effectiveness of our hybrid controller in the face of some parameter variations, such as nonlinear aerodynamic models and exogenous disturbances (e.g., wind gusts). Moreover, it compares its tracking performance with that of a fixed-gain SNI controller and the adaptive Neural-Fuzzy controller separately. Finally, the stability of the closed-loop control system is also discussed using the SNI theorem.