[14] modeled intermittent faults as Bernoulli distributed random variables and then designed a passive controller, while [PERSON] et al. [15] described the actuator failures as fuzzy discrete-time interconnected events. However, certain fault modes, such as the referred gradual faults in NT-DRAS, are difficult to detect because of no obvious fault characterization, and in those cases alternative fault modeling methods need to be considered. The FTC with gradual faults and outside disturbance is considered to be equivalent to the problem of system robustness in this paper. Since the gradual faults in NT-DRAS present the uncertainty characteristics and are difficult to detect, gradual faults can be considered as system uncertainty [16]. The system uncertainty due to the gradual faults is different from the model uncertainty caused by the system identification error: The former one is caused by the changing gradual faults and can result in the system dynamic performance variant with adverse influences of different fault degree, while the latter one, under fault-free conditions, has only a minor adverse influence due to the invariant system parameter errors. The first type of uncertainty modeling method for system failures is also used in flight control systems, for example, [PERSON] and [PERSON] [17] designed a passive fault tolerant controller against actuator failures. [PERSON] [11] determined that system component failures are often treated as system uncertainties, and that the robust fault tolerant control can be used to deal with failure caused by the parameter variations and model uncertainties. [PERSON] and [PERSON] [18] studied a delayed singular system with linear fractional parameter uncertainty using robust control method. [PERSON] et al. [19] analyzed the robust stability of stochastic delayedgenetic regulatory networks with polytope and linear fractional parameter uncertainties. In both of these latter two studies, all of the uncertainties are expressed in the form of uncertainty matrices, and are assumed to meet certain conditions. However, it is difficult to find matching conditions for NT-DRAS, and consequently application of the uncertainty modeling method in NT-DRAS requires some modifications. In [PERSON] and [PERSON]'s research [20], a robust fuzzy adaptive fault tolerant control method was proposed for a class of nonlinear systems with mismatched uncertainties and actuator faults. The research results indicate that the proposed method is effective for that particular class of nonlinear system with severe faults and mismatched uncertainties. Application of a time-invariant controller is typically sufficient and effective in the case of systems with gradual faults. The controller provides the advantage of a simple control structure where the controller gains are commonly determined with linear matrix inequality (LMI) technique and a convex optimization. For example, the work by [PERSON] [21], who studied the LMI conditions for time-varying uncertain systems, indicates that LMI technique is effective for the uncertain systems. [PERSON] et al. [22] designed the observer-based controller for linear systems with parameter uncertainty. [PERSON] et al. [23] studied the reliable robust flight tracking control using this technique. In the previously cited research, the LMI technique is used to realize the pole placement in order to form a stable closed-loop system and guarantee the system robustness. The major problem in the design of a fixed fault-tolerant controller by pole placement for NT-DRAS with gradual faults is to identify adequate representation of the system uncertainty and to determine the solution conditions using LMI technique. In this paper, a novel convex optimization-based fault-tolerant control (CO-FTC) strategy is proposed for NT-DRAS subject to gradual faults. The gradual faults are included in the system's state space formulation according to the characteristics of the fault modes.