AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
This paper investigates the practical prescribed‐time (PPT) tracking control problem for a class of nonlinear non‐strict‐feedback systems with arbitrarily bounded initial states with arbitrarily bounded initial states and employs a radial basis function neural network to approximate unknown and possibly non‐differentia...
This paper proposes an adaptive prescribed-time tracking control strategy based on the dynamic surface technique for hydraulic servo systems subject to time-varying parameters, external disturbances, and output constraints. First, a state-constrained transformation function is introduced to convert the strict output co...
This article addresses the problem of adaptive prescribed performance control for interconnected nonlinear systems with time delays in the presence of sensor and actuator faults, input saturation, and dead‐zone nonlinearities. The objective is to achieve accurate reference tracking while ensuring boundedness of all c...
A unified adaptive neural control framework is developed that synergistically combines predefined‐time stabilization, fixed‐time prescribed performance (FTPP) constraints, command‐filtered backstepping, and a dynamic event‐triggering mechanism (DETM), guaranteeing that both transient and steady‐state behaviors rigorous...
This paper proposes a prescribed‐time tracking control method for non‐affine nonlinear systems with input delay and unmatched disturbances, addressing the challenge that existing research often struggles to coordinate the handling of non‐affine characteristics, input delay, and disturbance suppression simultaneously....
Adaptive finite‐time prescribed performance control for autonomous surface vehicles (ASVs) subject to stochastic noise and asymmetric dead‐zone output nonlinearities is investigated in this paper. First, a finite‐time prescribed performance function (FTPPF) is introduced to ensure that the tracking error converges to...
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