{"title":"Design of Small-Phase Time-Variant Low-pass Digital Fractional Differentiators and Integrators","authors":"Mateusz Sakow","doi":"10.14313/jamris/2-2024/15","DOIUrl":null,"url":null,"abstract":"The design method and the time-variant FIR architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"6 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Automation, Mobile Robotics and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14313/jamris/2-2024/15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
The design method and the time-variant FIR architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.
本文介绍了小数和整数微分和积分实时估算的设计方法和时变 FIR 架构。拟议的 FIR 架构分为两部分。小相滤波、整数微分以及本地数据上的分数微分和积分由第一部分完成,它是时变的。第二部分是时变的,处理分数和全局微分和积分。将这两部分分开是必要的,因为单个时变 FIR 架构需要进行实时矩阵反演或大量的分析求解,而这对高阶 FIR 架构来说可能是计算密集型的。不过,设计方法中使用了矩阵反转,以实现滤波、微分和集成信号中可忽略的延迟。通过最小二乘法获得的最佳输出可实现可忽略的延迟。实验结果表明,尽管分数微分和积分过程对噪声和有限的测量分辨率比较敏感,但所提出的解决方案可以执行分数和整数微分和积分。在需要闭环控制、干扰观测和模型参数实时识别的系统中,可以采用这种解决方案。
期刊介绍:
Fundamentals of automation and robotics Applied automatics Mobile robots control Distributed systems Navigation Mechatronics systems in robotics Sensors and actuators Data transmission Biomechatronics Mobile computing