Abstract: Embodiments of a polyester industrial yarn dedicated to a marine hawser and a preparation method thereof, relating to the technical field of industrial yarns, are disclosed. In some examples, the preparation method includes: S1, preparing a regenerated polyester chip; S2, high-viscosity anti-ultraviolet modified polyester chip; S3, antibacterial master batch; S4, mixing the high-viscosity anti-ultraviolet modified polyester chip and the antibacterial master batch, subjecting a resulting mixture to extruding and melting to form a spinning melt, and metering, spinning, and cooling to form a tow; S5, spraying a waterproof and acid- and alkali-resistant layer onto a surface of the tow; S6, subjecting the tow to first oiling; and S7, subjecting the tow to heat-setting of two-stage drawing and one-stage relaxation, network processing, second oiling, and winding forming, to obtain the polyester industrial yarn dedicated to the marine hawser.

Abstract: The present invention discloses a neural network-based NARMA-L2 multivariable control method, which includes: deriving a NARMA-L2 multivariable control law of a nonlinear discrete system; using neural networks to offline identify nonlinear functions in the control law; designing the controller with the control law, designing a closed-loop control system for a certain type of turbofan engine, and adding the function of making the online update of neural network errors on the basis of this to correct the controller parameters, and studying the tracking performance of the control system. Aimed to minimize the quadratic performance index for the errors of the engine's high-pressure rotating speed and pressure ratio, by using neural networks in combination with the deduced NARMA-L2 multivariable control law, the present invention has designed a dual-variable controller, calculated the engine's control variables and controlled the fuel flow of the engine and the critical interface area of the exhaust nozzle.