This paper proposes an adaptive fault tolerant attitude controller, based on variable structure control, to address tracking control problem of spacecraft in the presence of unknown actuator failure, control input saturation and external disturbances. In contrast to traditional robust fault tolerant attitude controllers, the proposed controller is capable of controlling the transient response of the closed loop system by means of a dedicated parameter. Also this method does not require exact knowledge of the actuator faults and is implemented with uncertain value of fault information. By adjusting a simple parameter dynamically and using Lyapunov direct method and the properties of quaternion representation, the ultimate boundedness of attitude and angular velocity errors in presence of external disturbances is proven. Numerical simulations used to prove the success of proposed controller in achieving high attitude performance in the presence of external disturbances, inexact knowledge of fault information, actuator failures and control input saturation.

In this paper, an online optimal distributed learning algorithm is proposed to solve leader-synchronization problem of nonlinear multi-agent differential graphical games. Each player approximates its optimal control policy using a single-network approximate dynamic programming U+0028 ADP U+0029 where only one critic neural network U+0028 NN U+0029 is employed instead of typical actorcritic structure composed of two NNs. The proposed distributed weight tuning laws for critic NNs guarantee stability in the sense of uniform ultimate boundedness U+0028 UUB U+0029 and convergence of control policies to the Nash equilibrium. In this paper, by introducing novel distributed local operators in weight tuning laws, there is no more requirement for initial stabilizing control policies. Furthermore, the overall closed-loop system stability is guaranteed by Lyapunov stability analysis. Finally, Simulation results show the effectiveness of the proposed algorithm.

In this study, a stable direct adaptive general type‐2 fuzzy logic controller (DAG2FLC) is introduced for a class of non‐linear systems. The proposed controller uses advantages of general type‐2 fuzzy logic systems (GT2FLSs) in handling dynamic uncertainties to approximate unknown non‐linear actions. Implementing general type‐2 fuzzy systems is computationally costly; however, by using a recently introduced α ‐plane representation, a GT2FLS can be seen as composition of several interval type‐2 fuzzy logic systems with a corresponding level of α for each. Linguistic rules are directly incorporated into the DAG2FLC controller and a H ^∞ compensator is added to attenuate external disturbance and fuzzy approximation error. Also general type‐2 fuzzy adaptation laws are derived using Lyapunov approach, and the stability of the closed‐loop system has been proven by mathematical analysis. In order to evaluate the performance of the proposed controller, the results are compared with those obtained by direct adaptive type‐1 fuzzy logic controller and a direct adaptive interval type‐2 fuzzy logic controller, which are the latest researches in the problem in hand. The proposed controller is applied to a chaotic Gyro system as a case study. Simulation reveals the effectiveness of the proposed controller in presence of dynamic uncertainties and external disturbances.