A fault-tolerant trajectory tracking controller is proposed for a quadrotor. Usually, the effect of actuator faults is mainly regarded as an external disturbance, which makes it difficult to properly respond to time-varying loss of effectiveness faults or input saturation under state constraints. Adaptive control based on the Nussbaum -type function provides robust control performance for the unknown time-varying control effectiveness. However, numerical instability is the biggest obstacle to applying Nussbaum -type functions to fault-tolerant control, especially for state-constrained problems due to the large feedback from tracking errors. In this study, an adaptive control scheme utilizing the Nussbaum -type function is proposed to address the problems of input saturation and numerical instability. The Nussbaum -type gain is assigned to each rotor directly to consider the independent faults and saturation of each rotor. The quadrotor position control structure is redesigned to properly update the Nussbaum -type gains by passing the total thrust command from the position control loop to the attitude control loop. A nonlinear mapping is also proposed to constrain the states as well as to address the numerical instability of the Nussbaum -type function by exploiting a gradient modifier in the nonlinear mapping. The stability analysis shows that all the closed-loop signals are uniformly ultimately bounded under mild assumptions. Numerical simulations demonstrate that the proposed method has satisfactory trajectory tracking performance under various fluctuating loss of effective faults and input saturation while satisfying state constraints.

A control design scheme that integrates policy gradient-based integral reinforcement learning with an extended state observer is proposed for a morphing aircraft. A general continuous-time nonaffine system, such as a morphing aircraft, in which the morphing parameters are considered as control effectors, can be addressed by the proposed control scheme. By combining a Q-function with the policy gradient method, the optimal control problems for completely unknown and nonaffine systems can be resolved. The actor-critic parameter estimation employs the integral reinforcement learning technique. The learning-based strategy enables the prediction of optimal morphing parameters, even in the absence of a complete aerodynamic model. To estimate the disturbances derived from morphing variations, an extended state observer is designed using the knowledge of the nominal model. This approach allows for the independent design of controllers for both morphing parameters and conventional control effectors. A stability analysis shows the asymptotic stability of the closed-loop system and the optimal convergence of the Q-function. To demonstrate the effectiveness of the proposed method, a numerical simulation is performed.

In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme.

We propose an active weighting algorithm for composite adaptive control to reduce the state and estimate errors while maintaining the estimation quality. Unlike previous studies that construct the composite term by simply stacking, removing, and pausing observed data, the proposed method efficiently utilizes the data by providing a theoretical set of weights for observations that can actively manipulate the composite term to have desired characteristics. As an example, a convex optimization formulation is provided, which maximizes the minimum eigenvalue while keeping other constraints, and an illustrative numerical simulation is also presented.