本文介紹了一種用於可再生能源系統的FPNN補償NFTCSMC，它能在非

本文介紹了一種用於可再生能源系統的FPNN補償NFTCSMC，它能在非線性整流負載下產生低THD，在瞬態負載下產生快速暫態。與經典的FTCSMC相比，NFTCSMC的重要性在於有限的系統狀態收斂時間和無奇异性。然而，當系統的不確定性界被高估或低估時，NFTCSMC將發生抖振或穩態誤差。利用FPNN估計參數不確定性和外部干擾的上界，以降低NFTCSMC設計的過保守性。一旦過保守開關增益减小，就可以抑制NFTCSMC中存在的抖振，從而提高可再生能源系統的效能。利用Lyapunov方法對改進科技進行了穩定性分析，證明了滑動面的有限時間可達性、閉環系統的漸近穩定性和跟踪誤差的有限時間收斂到零。囙此，我們相信改進後的科技將有助於未來人工智慧相關系統的控制設計。利用數位信號處理器（DSP）對可再生能源系統樣機進行了模擬和實驗，驗證了改進科技的適用性。

結果 (英文) 1:

This article describes a method for compensating NFTCSMC FPNN renewable energy system, it can produce at a low THD linear rectifier load, produce rapid transient during transient loading. Compared with the classic FTCSMC, NFTCSMC importance lies in the limited convergence time system status and no singularity. However, when the uncertainty bound of the system is underestimated or overestimated, NFTCSMC the steady state error or buffeting occurs. FPNN using the upper bound estimate parameter uncertainty and external disturbances, in order to reduce over-conservative NFTCSMC design. Once the gain is reduced over traditional switch, chattering can be suppressed NFTCSMC present, thereby increasing the effectiveness of renewable energy systems. The method of using the improved technology for Lyapunov stability analysis, finite time prove sliding surface accessibility, limited time asymptotic stability of the closed-loop system and a tracking error converges to zero. Yin this, we believe that technology will help improve the design of future artificial intelligence-related control systems. System prototype regeneration of energy can be simulated and experiments, to verify the applicability of the technology improved using digital signal processors (DSP).

結果 (英文) 2:

This paper introduces an FPNN compensation NFTCSMC for renewable energy systems, which can produce low THD under nonlinear rectifier loads and fast transients under transient loads. Compared with the classic FTCSMC, NFTCSMC is important in the limited system state convergence time and no singularity. However, when the uncertainty of the system is overestimated or underestimated, nFTCSMC will experience vibration or steady-state errors. The upper bounds of FPNN estimation parameter uncertainty and external interference are used to reduce the overconservative design of NFTCSMC. Once the over-conservative switch gain is reduced, the vibration existing in the NFTCSMC can be suppressed, thereby increasing the efficiency of the renewable energy system. The stability analysis of improved technology is carried out by Lyapunov method, which proves that the limited time accessibility of the sliding surface, the asymptomatic stability of the dead cycle system and the limited time of tracking error converge to zero. In this way, we believe that improved technology will contribute to the control design of ad intelligence-related systems in the future. The simulation and experiment of the prototype of renewable energy system are simulated and experimented with the digital signal processor (DSP), which proves the applicability of the improved technology.

結果 (英文) 3:

This paper introduces a kind of FPNN compensation nftcsmc for renewable energy system, which can generate low THD under nonlinear rectifier load and fast transient under transient load. Compared with the classical ftcsmc, the importance of nftcsmc is limited in the time of system state convergence and singularity free. However, when the uncertainty bound of the system is overestimated or underestimated, chattering or steady-state error will occur in nftcsmc. FPNN is used to estimate the upper bound of parameter uncertainty and external interference, so as to reduce the over conservatism of nftcsmc design. Once the over conservative switching gain is reduced, the chattering in nftcsmc can be suppressed and the efficiency of renewable energy system can be improved. The Lyapunov method is used to analyze the stability of the improved technology. The finite time reachability of the sliding surface, the asymptotic stability of the closed-loop system and the finite time convergence of the tracking error to zero are proved. Therefore, we believe that the improved technology will contribute to the control design of artificial intelligence related systems in the future. The digital signal processor (DSP) is used to simulate and test the prototype of renewable energy system, which verifies the applicability of the improved technology.