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This paper investigates fractional-order fuzzy quaternion-valued BAM neural networks (FOFQBAMNNs) without decomposition. By virtue of a novel contraction mapping, the existence and uniqueness of the equilibrium point is yielded. Furthermore, according to some basic knowledge on fractional calculus, inequality techniques of fuzzy logic and reduction to absurdity, some criteria are yielded to guarantee finite-time stabilization of FOFQBAMNNs via original quaternion-valued controllers, and the settling times of corresponding finite-time stabilization are derived. Finally, the feasibility of our obtained theoretical results is illustrated by some numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, the leader-following bipartite consensus is investigated for a group of uncertain multiple Euler–Lagrange systems with disturbances. An innovative adaptive distributed observer is developed without requiring that followers surely acquire the leader's auxiliary state and system matrix. A directed signed network satisfying the principle of structural balance is exploited to describe the interaction among agents. Then a novel bipartite consensus control protocol is proposed to solve the bipartite consensus problem of multiple Euler–Lagrange systems. The theoretical proof is provided via constructing a Lyapunov function and applying Barbalat lemma to analyze the convergence problem. Finally, a numerical simulation is utilized to demonstrate the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the reliable control problem for abrupt faults of autoregressive moving average with exogenous input (ARMAX) system. A set of models are used to cover the potential dynamics of the system and for each model minimum variance control (MVC) is designed. By taking the a-posteriori probabilities of models as weight coefficients, a multi-model reliable control (MMRC) is proposed. If the system is normal, MMRC is MVC. When faults occur, the controller can quickly learn the true model of system and degenerate into the corresponding MVC by adjusting weight coefficients, which ensures the acceptable performance of the system. The effectiveness of MMRC is verified by a numerical simulation. In addition, since MMRC is a fusion of control law of each model, it indicates that soft switching is implemented and the jitter to system due to hard switching can be avoided. [ABSTRACT FROM AUTHOR]