This paper exposes that although some sensor-based nonlinear fault-tolerant control frameworks including incremental nonlinear dynamic inversion control can passively resist a wide range of actuator faults and structural damage without requiring an accurate model of the dynamic system, their stability heavily relies on a sufficient condition, which is unfortunately violated if the control direction is unknown. Consequently, it is proved in this paper that no matter which perturbation compensation technique (adaptive, disturbance observer, sliding-mode) is implemented, none of the existing nonlinear incremental control methods can guarantee closed-loop stability. Therefore, this paper proposes a Nussbaum function-based adaptive incremental control framework for nonlinear dynamic systems with partially known (control direction is unknown) or even completely unknown control effectiveness. Its effectiveness is proved in the Lyapunov sense and is also verified via numerical simulations of an aircraft attitude tracking problem in the presence of sensing errors, parametric model uncertainties, structural damage, actuator faults, as well as inversed and unknown control effectiveness.

This article proposes a fuzzy adaptive fault-tolerant control (FTC) strategy solving the fast fixed-time constrained tracking problem for hypersonic flight vehicles (HFVs). To handle the actuator faults, a two-step design method is first proposed that only needs to estimate the upper and lower bounds of actuator parameters, under which the adverse influences of actuator faults (e.g. lock-in-place, loss of effectiveness, etc.) have been compensated via an adaptive fashion instead of conventional robust ways. In comparison with the state-of-the-art, we have also presented a new fixed-time corollary that has proved a smaller upper bound of convergence time under the same condition of conventional fixed-time stability. To avoid singularity issues often encountered in fixed-time designs, we introduce a piecewise but differentiable switching control laws whose continuity and differentiability are guaranteed everywhere through an appropriate design. Such a design not only preserves the continuity and differentiability of virtual and actual control laws, but also ensures the continuity of their time derivatives. Fuzzy logic systems are exploited to tackle continuous unknown dynamics and asymmetric time-varying barrier functions are utilized to confine flight states within some predefined compact sets all the time provided their initial conditions remain therein. This property is of great significance in dealing with the challenge that the operating regions of the flight state variables are asymmetric and time-varying in practice, especially when executing various flight missions. Comparative simulations have been performed to validate the effectiveness of the proposed control scheme in terms of fixed-time convergence, smoothness and time-varying constraints satisfaction.

In this brief, an approach is presented to design a robust quadrotor attitude estimator when only inertial measurement units (IMUs) sensor data can be available. A two-step procedure is proposed to improve attitude estimation in the presence of IMU biases and measurement noise. In the first step, a smooth sliding mode observer is designed to estimate the roll and pitch angles using accelerometer measurements. The second step enhances the attitude estimation performance in the presence of noise and bias in IMU data by using a complementary nonlinear filter. Experimental results on a quadrotor test bed together with simulation results obtained using a representative nonlinear model are used to highlight the potential and performance of the proposed solution.