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This paper aims to solve the problem of altitude control of the variable load quadrotor unmanned aerial vehicle. Generally, the controller parameters of the quadrotor are adjusted for constant load and external disturbance factors. However, whether in military fields or civilian fields, the quadrotor will encounter variable load situations, such as throwing weapons or delivering express. Variable load as a large internal disturbance factor results in control instability. Toward this end, a novel robust sliding mode controller SMC) is designed to track the desired trajectory promptly under variable load condition while restraining the chattering problem. This novel controller consists of learning rate-based sliding mode surface and inverse hyperbolic function-based adaptive sliding mode reaching law. It is worth mentioning that a mass estimation algorithm is added to the proposed novel SMC to estimate the real-time mass of the quadrotor in the air, which improves the quadrotor's performance in restraining the large variable load disturbance impact. In addition, the altitude control law is derived on the basis of the Lyapunov stability theory. Finally, the simulation and experimental results are carried out to show the effectiveness and robustness of the proposed controller in terms of altitude tracking, mass estimation, and disturbance resistance.

This study is concerned with stochastic stability of a new extended filtering for non-linear systems subject to measurement packet losses. The measurements sensored are transmitted to the estimator through a packet-dropping network. By introducing a time-stamped packet arrival indicator sequence, the measurement loss process is modelled as an independent, identically distributed (i.i.d.) and therefore a Bernoulli process. The boundedness of estimation error covariance matrices is proved by showing the existence of a critical threshold for measurement packet arrival probability. It is also shown that, under appropriate assumptions, the estimation error remains bounded as long as the noise covariance matrices and the initial estimation error can be ensured small enough. Finally, simulation results validating the effectiveness of this proposed filtering framework are also presented.