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• W4328124431 endingPage "659" @default.
• W4328124431 startingPage "659" @default.
• W4328124431 abstract "A new control algorithm was designed to solve the problems of actuator physical failure, remote network attack, and sudden change in trajectory curvature when a port’s artificial intelligence-based transportation robots track transportation in a freight yard. First of all, the nonlinear, redundant, saturated sliding surface was designed based on the redundant information of sliding mode control caused by the finite nature of control performance; the dynamic acceleration characteristic of super-twisted sliding mode reaching law was considered to optimize the control high frequency change caused by trajectory mutation; and an improved super-twist reaching law was designed. Then, a nonlinear factor was designed to construct a nonlinear, fault-tolerant filtering mechanism to compensate for the abnormal part of the unknown input that cannot be executed by adaptive neural network reconstruction. On this basis, the finite-time technology and parameter-event-triggered mechanism were combined to reduce the dependence on communication resources. As a result, the design underwent simulation verification to verify its effectiveness and superiority. In the comparative simulation, under a consistent probability of a network attack, the tracking accuracy of the algorithm proposed in this paper was 22.65%, 12.69% and 11.48% higher those that of the traditional algorithms." @default.
• W4328124431 created "2023-03-22" @default.
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• W4328124431 date "2023-03-21" @default.
• W4328124431 modified "2023-10-18" @default.
• W4328124431 title "Neural Network, Nonlinear-Fitting, Sliding Mode, Event-Triggered Control under Abnormal Input for Port Artificial Intelligence Transportation Robots" @default.
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• W4328124431 doi "https://doi.org/10.3390/jmse11030659" @default.
• W4328124431 hasPublicationYear "2023" @default.
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