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The article introduces an innovative adaptive fixed‐time control strategy designed for a robot system grappling with challenges like actuator saturation and model uncertainty. Two strategies are explored: model‐based control and neural networks control. In instances of model uncertainty, neural networks are leveraged to contend with the unknown dynamics of the robot system. These networks undergo training to approximate the elusive model parameters. Through this neural network approach, we establish adaptive laws grounded in fixed‐time convergence, ensuring that system tracking errors converge to a confined range near zero within a predetermined time frame. To tackle the issue of actuator saturation, an enhanced auxiliary system is introduced. This auxiliary system is tailored to counterbalance the adverse impacts of actuator saturation, thereby augmenting the tracking performance of the robot system. The proposed control policy is rigorously analyzed using Lyapunov theory, demonstrating that the system's tracking errors converge within a fixed time frame. To validate the efficacy of the proposed methodology, both numerical simulations and practical experiments are conducted, affirming the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Summary: This article concerns the problem of event‐protocol‐based asynchronous fault detection filtering design for discrete‐time fuzzy singular semi‐Markovian jump systems subjected to system parameters uncertainties, unmatched one‐sided Lipschitz nonlinearities and multi‐cyber‐attacks. First, a well‐designed model structure is proposed to integrate two dynamic quantizers and an improved dynamic event‐triggered protocol into a unified framework to reduce the amount of data in the network transmission and rationalize the use of limited network communication bandwidth resources. Especially, the event‐triggering threshold parameter can be continuously adjusted over the time based on the fluctuation of the network signal. Second, a novel triple‐cyber‐attacks platform is established, of which including random deception attacks, random replay attacks and aperiodic DoS attacks. And with help of the extended decoupling strategy and iterative techniques, the co‐design conditions of the stochastic admissibility with a ℘˘ level of ℋ∞$$ {\mathscr{H}}_{\infty } $$ performance are derived for the fault filtering error systems. Finally, two examples are provided to demonstrate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the article, an event‐triggered control policy is developed for a robotic manipulator with flexible joint (RMFJ) in the presence of finite‐time convergence under the requirement of output constraints. Due to the vibration existing in the flexible joint, compared with the rigid manipulator, it is exceedingly difficult for RMFJ to design an adaptive method for realizing highly accurate control. In order to reduce and restrain the vibration of the flexible joint, a finite‐time convergence policy is designed by introducing the fractional order term. Moreover, the angular displacements of the link are constrained in the predefined region by using the Barrier function, which further enhances the operation safety. Neural networks learning is used to approximate unknown model parameters. Event‐trigger design is used to reduce the communication burden. [ABSTRACT FROM AUTHOR]