This paper deals with state and fault estimations for a class of uncertain Lipschitz nonlinear systems. The proposed scheme combines a descriptor form observer and an adaptive sliding mode observer. The adaptive descriptor sliding approach is proposed to jointly estimate the state and reconstruct the sensor fault while rejecting the effect of uncertainties and Lipschitz nonlinearities. Using a Lyapunov analysis, the stability condition is analyzed and the observer gains are designed such that the reduced-order system is practically stable. Then, an adaptive super-twisting observer is derived to reconstruct the actuator faults. The main feature of the proposed adaptive scheme is that it does not overestimate the observer gains, which mitigates chattering in the presence of bounded uncertainties, actuator, and sensor faults with unknown boundaries. Simulation results for a robotic manipulator show the effectiveness of the proposed method.constraining the system motion along manifolds of reduced dimensionality in the state space and is applicable to a broad variety of practical applications. Robustness properties against various kinds of uncertainties such as parameter perturbations and external disturbances can be guaranteed.Several SMO-based FDI approaches have been developed recently for linear systems and a class of nonlinear systems. The first-order SMOs in [14][15][16] are designed to directly tackle the actuator faults through the sliding mode terms via some transformations. Unfortunately, the realization of first-order sliding mode implies the undesirable chattering phenomenon, that is, high-frequency vibrations of the closed-loop system, which may excite unmodeled high-frequency dynamics, degrade the system performances, and lead to instability. Other techniques rely on a nonlinear coordinate transformation to obtain a form suite for the design of high-gain observer [17,18]. The gains are designed based on the inverse jacobian of the state transformation. However, this transformation is valid only locally and no sensor fault is considered. Recently, some SMOs have been proposed to reconstruct both the system state, actuator faults, and sensor faults. In [19,20], actuator fault is reconstructed with the so-called equivalent output estimation error injection concept. Then, sensor fault is estimated via a low-pass filter and appropriate secondary SMO. Nevertheless, the sensor fault estimation is very sensitive to the filter parameters, and no uncertainty acting on the system is considered. An SMO is proposed in [21,22] to solve this problem for nonlinear systems without uncertainty assuming some conditions on the fault distribution matrix. Descriptor sliding mode approaches are introduced in [5, 23] to simultaneously reconstruct state, sensor, and actuator faults for linear systems. In [24], a sliding mode unknown input observer is given for linear systems where the same additive perturbation affects both the dynamics and the output. In [25], a H 1 SMO is derived for a class of uncertain nonlinear Lipschitz sy...