This paper proposes a self-adaptive interval type-2 neural fuzzy network (SAIT2NFN) control system for the high-precision motion control of permanent magnet linear synchronous motor (PMLSM) drives. The antecedent parts in the SAIT2NFN use interval type-2 fuzzy sets to handle uncertainties in PMLSM drives, including payload variation, external disturbance, and sense noise. The SAIT2NFN is firstly trained to model the inverse dynamics of PMLSM through concurrent structure and parameter learning. The fuzzy rules in the SAIT2NFN can be generated automatically by using online clustering algorithm to obtain a suitable-sized network structure, and a back propagation is proposed to adjust all network parameters. Then, a robust SAIT2NFN inverse control system that consists of the SAIT2NFN and an error-feedback controller is proposed to control the PMLSM drive in a changing environment. Moreover, the Kalman filtering algorithm with a dead zone is derived using Lyapunov stability theorem for online fine-tuning all network parameters to guarantee the convergence of the SAIT2NFN. Experimental results show that the proposed SAIT2NFN control system achieves the best tracking performance in comparison with type-1 NFN control systems. 相似文献
This paper presents a new optimization method for coupled vehicle–bridge systems subjected to uneven road surface excitation. The vehicle system is simplified as a multiple rigid-body model and the single-span bridge is modeled as a simply supported Bernoulli–Euler beam. The pseudo-excitation method transforms the random surface roughness into the superposition of a series of deterministic pseudo-harmonic excitations, which enables convenient and accurate computation of first and second order sensitivity information. The precise integration method is used to compute the vertical random vibrations for both the vehicle and the bridge. The sensitivities are used to find the optimal solution, with vehicle ride comfort taken as the objective function. Optimization efficiency and computational accuracy are demonstrated numerically. 相似文献
The formation and distribution of dioxin-like polychlorinated biphenyls (dl-PCBs) during cooking was investigated. Concentrations of dl-PCBs in liquid residues, cooked beef, and oil fumes generated during heating were determined by isotope dilution HRGC/HRMS. Although the levels of dl-PCBs in well-done beef were lower compared with those of raw beef, relatively high concentrations of dl-PCBs were detected in the oil fumes produced during heating. This suggests that dl-PCBs in raw beef may have volatilized into the oil fumes during the cooking process. Sucralose and chloropropanols contained in raw materials may have resulted in increased dl-PCB concentrations and the level of toxic equivalents (TEQ) in the oil fumes produced under high temperature during cooking. Concentrations of dl-PCBs did not vary greatly in cooked beef, except when higher levels of chloropropanols were presented in the uncooked raw materials. Results indicate that sucralose and chloropropanols may promote the formation of dl-PCBs during the cooking process. The newly produced dl-PCBs from raw beef cooking were mainly present in oil fumes, which gave rise to high levels of TEQ in oil fumes. 相似文献
In order to avoid the overflow problem of network flow table caused by hackers attacking the network in the process of using the network, a method for overflow attack defense of SDN network flow table based on stochastic differential equation is proposed. In this method, the stochastic differential equation is first proposed, and the drift coefficient and diffusion coefficient of the equation are expanded and adjusted by Taylor. By using the limit theorem, the spillover attack of SDN network is weakly converged to an approximate two-dimensional Markov diffusion process, and the improved stochastic differential equation is obtained. Then, according to the stochastic nature of SDN network attack, the stochastic differential equation is transformed into an amplitude equation, which is based on the amplitude. The equation establishes a SDN attack detection scheme based on flow table statistics, which detects the spillover attacks of SDN network flow tables. Finally, according to the test results, it is proposed to use other switches instead of network flow table overflow switches to control the data upload rate, thus reducing the possibility of network crash and meeting the attack defense requirements of flow table overflow. The simulation results show that the proposed method has better detection performance and shorter running time, and can provide help for network security related work.