UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

Abstract

This paper proposes an adaptive control method for an underactuated complex nonlinear system, that is, unmanned aerial vehicle (UAV), to realize high‐precision trajectory tracking under input saturation constraint and other disturbances via artificial neural network and anti‐saturation auxiliary system. First, the rigid body motion theory is applied to establish a simplified nonlinear dynamic model of the UAV. Then a Lyapunov function is designed with the error surface to form the preliminary control law based on dynamic surface control. Afterwards, a radical basis function neural network is developed and used to estimate and compensate the disturbance, while the input saturation is also addressed via the designed anti‐saturation auxiliary system. Thus the stability and signal consistency of the closed‐loop UAV control system can be proved via the Lyapunov stability theory with appropriately tuned parameters. Simulation experiments are performed to verify that the proposed control algorithm can be used for UAV high‐precision trajectory tracking under time‐varying disturbances.

NN-Based Parallel Model Predictive Control for a Quadrotor UAV

“…The dynamic models of Q-UAVs have been studied as a system with six degrees of freedom. Under the low speeds assumption, a simple, rigid-body model [35] of Q-UAVs is defined as ẍ = (cos φ sin θ cos ψ + sin φ sin ψ)…”