Neural Network-based Finite-time Control of Nonlinear Systems with Unknown Dead-zones: Application to Quadrotors

Abstract

Over the years, researchers have addressed several control problems of various classes of nonlinear systems. This article considers a class of uncertain strict feedback nonlinear system with unknown external disturbances and asymmetric input dead-zone. Designing a tracking controller for such system is very complex and challenging. This article aims to design a finite-time adaptive neural network backstepping tracking control for the nonlinear system under consideration. In addition, all unknown disturbances and nonlinear functions are lumped together and approximated by radial basis function neural network (RBFNN). Moreover, no prior information about the boundedness of the dead-zone parameters is required in the controller design. With the aid of a Lyapunov candidate function, it has been shown that the tracking errors converge near the origin in finite-time. Simulation results testify that the proposed control approach can force the output to follow the reference trajectory in a short time despite the presence of asymmetric input dead-zone and external disturbances. At last, in order to highlight the effectiveness of the proposed control method, it is applied to a quadrotor unmanned aerial vehicle (UAV).

Altitude Control of UAV Quadrotor Using PID and Integral State Feedback

“…The proportional Integral Derivative (PID) controller has been known for its wide use in many control systems; hence, it also has been applied to control altitude in quadrotor [9] [10]. The other controller is Sliding Mode Control (SMC) [11], Linear Quadratic Regulator (LQR) [12][13], Predictive Control [14], Fuzzy Control [15] [16], Neural Network [17] [18], Fractional Order PID [19], Feedback Linearization [20], and other control techniques [21]. This research presented an application of the Integral State Feedback (ISF) controller for altitude control in quadrotor as a practical solution that enables a precise and robust control system performance.…”