This article addresses the safe tracking control problem of quadrotor unmanned aerial vehicles (QUAV) considering uncertain aerodynamics and unknown external disturbances. First, a robust adaptive backstepping controller is designed, where the bias of the control variable, that is, the tracking error of the attitude subsystem, is considered in the design process of the position subsystem. Meanwhile, the safe obstacle avoidance objective is realized by the quadratic programming (QP) method that combines the tracking controller with the exponential control barrier function (ECBF). Furthermore, an event-triggered mechanism is used for the QP process to conserve computing resources. Simulation validates the feasibility and superiority of the proposed control scheme in presence of multi-obstacle.
In this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un-transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter-class variance. Two examples are provided to demonstrate the advantages of our algorithm.
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