Trajectory robust control of autonomous quadcopters based on model decoupling and disturbance estimation

Abstract

In this article, a systematic procedure is given for determining a robust motion control law for autonomous quadcopters, starting from an input–output linearizable model. In particular, the suggested technique can be considered as a robust feedback linearization (FL), where the nonlinear state-feedback terms, which contain the aerodynamic forces and moments and other unknown disturbances, are estimated online by means of extended state observers. Therefore, the control system is made robust against unmodelled dynamics and endogenous as well as exogenous disturbances. The desired closed-loop dynamics is obtained by means of pole assignment. To have a feasible control action, that is, the forces produced by the motors belong to an admissible set of forces, suitable reference signals are generated by means of differentiators supplied by the desired trajectory. The proposed control algorithm is tested by means of simulation experiments on a Raspberry-PI board by means of the hardware-in-the-loop method, showing the effectiveness of the proposed approach. Moreover, it is compared with the standard FL control method, where the above nonlinear terms are computed using nominal parameters and the aerodynamical disturbances are neglected. The comparison shows that the control algorithm based on the online estimation of the above nonlinear state-feedback terms gives better static and dynamic behaviour over the standard FL control method.

Robust Cooperative Control of UAV Swarms for Dual-Camp Divergent Tracking of a Heterogeneous Target

“…Due to the uncertainty of the target's nonlinear fractional-order model, the first stage is to eliminate the uncertainty by obtaining the estimates of the unknown smooth function in the target's dynamics (11). Therefore, we make the following assumptions to utilize conventional linearly parameterized neural networks to approximate the true values of the unknown function: Assumption 2.…”

“…However, the phenomena of divergence are equally common in nature, for instance, the split of a school of fish when encountering an obstacle [4] and the division of an ant colony on their way to food sources [5,6]. In a similar way, when many practical multi-agent systems [7][8][9], especially UAV groups [10][11][12], perform complex behavior, such as coverage [13], formation [14], enclosing [15], surrounding [16] and containment [17], their states exhibit divergence in order to fulfill civil and military tasks related to searching and exploring by space expansion rather than convergence. Relevant studies show that the bipartition of a UAV network is an effective use of divergence behavior.…”

Robust Approximate Optimal Trajectory Tracking Control for Quadrotors

“…However, quadrotors need to be controlled away from the equilibrium point to accomplish complex control tasks and withstand external disturbances. As a result, a technique has been devised that is regarded as a robust feedback linearization method that uses extended state observers to estimate the nonlinear state feedback term online, containing aerodynamic forces, moments and unknown disturbances, and obtains the desired closed-loop dynamics via pole assignment [8]. Moreover, several robust controllers relying on nonlinear techniques have been proposed, such as sliding mode control [9], adaptive control [10], backstepping-based control [11] and robust control [12].…”

Finite-Time Height Control of Quadrotor UAVs

“…The extended state observer (ESO) can estimate disturbances without prior knowledge to actively compensate in the controller [15]. Therefore, the ESO has been increasingly used and developed in various fields of control [16][17][18]. Note that traditional SMC methods slow down the convergence speed of the system, and peak effects may occur in the large gain of traditional ESOs [19].…”