Distributed Active Disturbance Rejection Formation Tracking Control for Quadrotor UAVs.

In this article, a distributed active disturbance rejection formation tracking control strategy is developed for a quadrotor unmanned aerial vehicle (UAV) swarm system with switching communication topologies. The proposed control strategy consists of two parts: 1) attitude-loop control and 2) position-loop control. First, a distributed cascade active disturbance rejection control (CADRC) method is designed for the attitude subsystem. With this attitude control method, the stability of the attitude subsystem can be achieved even in the presence of unknown time-varying disturbances. Second, a distributed formation tracking control method is designed for the position subsystem. This position control method ensures that the quadrotor UAV swarm maintains dynamic formation flying and accurately tracks a predetermined trajectory. Through stability analysis, it can be proved that the proposed control strategy can ensure the stability of the whole swarm system. Finally, the proposed control strategy is applied to a quadrotor UAV swarm system to verify its effectiveness and the ability to suppress the influence of unknown time-varying disturbances.

Multiple Environment Integral Reinforcement Learning-Based Fault-Tolerant Control for Affine Nonlinear Systems.

This paper studies the fault-tolerant control (FTC) problem for unknown affine nonlinear systems with actuators faults. The considered types of faults are stuck (lock-in-place), loss-in-effectiveness (LIE), and bias, under which a part of the actuators is disabled. The objective is to find the remaining (not fully LIE) actuators, and manipulate them to obtain the best achievable performance in real time. First, considering that the best achievable performance is determined by the remaining actuators, a set of basic policies is predesigned with multiple levels of performances for different groups of activated actuators. Second, an identifier is designed based on history data to find the remaining actuators and, thus, the suitable predesigned basic policy. Third, to further accommodate the partial LIEs and biases, a compensator works together with the selected basic policy, to build the predesigned performance. In addressing the FTC problem, several techniques are developed: adjustable mechanisms are novelly integrated to deal with the state-dependent nonlinearities in neural network (NN) approximation, disturbances, and mismatch errors; history data are newly applied to estimate the faulty parameters; and a compensator is specially designed to deal with LIEs and biases in different input channels. Also in theory, the convergences of algorithms and the stability of closed-loop systems are proved, by formally giving the invariant sets of the initial state and the NN weights. Unlike the existing FTC methods dealing with LIE and bias based on model information to optimize the tracking error, this result can handle stuck faults without knowing system dynamics and satisfy different levels of performances described by Hamilton-Jacobi-Bellman equations. Finally, a simulation example of quadrotor unmanned aerial vehicle is given to verify the effectiveness of the proposed FTC scheme.