Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

Abstract

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

Effects of unmodelled dynamic factors on an under-actuated quadrotor: A review of hybrid observer design methods

“…Researchers also proposed B-spline artificial neural networks with online disturbance estimation for a quadrotor unmanned aerial vehicle where the linear extended state again estimates the external disturbances. 39,42…”

“…Researchers also proposed B-spline artificial neural networks with online disturbance estimation for a quadrotor unmanned aerial vehicle where the linear extended state again estimates the external disturbances. 39,42 The Table 6 is formulated using the basic understanding of predictive control optimisation method with nonlinear disturbance observer technique as proposed in Wang et al 27 By amalgamating it with any robust or adaptive control design, the better performance can be obtained. The measured and hypothetic parameters are shown in Table 6.…”

“…Researchers provided several methods where they used disturbance and extended state observers-based control laws to cope up with the cable suspended payload smooth and non-smooth disturbances. 42 One should study and evaluate the mathematics so that it can also work for unbounded variation with partially known information. Researchers also proposed B-spline artificial neural networks with online disturbance estimation for a quadrotor unmanned aerial vehicle where the linear extended state again estimates the external disturbances.…”

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

“…Suppose that there are N(N > 1) UAVs with the same dynamic characteristics moving in ℜ 3 , which together compose a multi-UAV system, as shown in Figure 1. 25 In what follows, the dynamical model supposes that the UAV always performs coordinated maneuvers and its thrust is directed along the velocity vector. It is also assumed that the earth is flat and the mass center is time-invariant.…”

“…Suppose that there are N ( N > 1) UAVs with the same dynamic characteristics moving in ℜ 3 , which together compose a multi‐UAV system, as shown in Figure 1 25 …”