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.

在本文中，从输入输出线性化模型开始，给出了一种系统的过程，用于确定自主四轴飞行器的鲁棒运动控制律。特别地，建议的技术可以被认为是鲁棒的反馈线性化（FL），其中通过扩展状态观测器在线估算包含空气动力和力矩以及其他未知干扰的非线性状态反馈项。因此，使控制系统具有强大的鲁棒性，可以抵抗未建模的动力学以及内源性和外源性干扰。期望的闭环动力学通过极点分配获得。为了采取可行的控制措施，也就是说，电动机产生的力属于一组允许的力，适当的参考信号通过所需轨迹提供的微分器产生。借助硬件在环方法，在Raspberry-PI板上通过仿真实验对提出的控制算法进行了测试，证明了该方法的有效性。此外，将其与标准FL控制方法进行了比较，在标准FL控制方法中，使用标称参数计算了上述非线性项，而忽略了空气动力干扰。比较表明，基于上述非线性状态反馈项的在线估计的控制算法比标准FL控制方法具有更好的静态和动态性能。展示了所提出方法的有效性。此外，将其与标准FL控制方法进行了比较，在标准FL控制方法中，使用标称参数计算了上述非线性项，而忽略了空气动力干扰。比较表明，基于上述非线性状态反馈项的在线估计的控制算法比标准FL控制方法具有更好的静态和动态性能。展示了该方法的有效性。此外，将其与标准FL控制方法进行了比较，在标准FL控制方法中，使用标称参数计算了上述非线性项，而忽略了空气动力干扰。比较表明，基于上述非线性状态反馈项的在线估计的控制算法比标准FL控制方法具有更好的静态和动态性能。