Universal fractional-order design of linear phase lead compensation multirate repetitive control for PWM inverters

Repetitive control (RC) with linear phase lead compensation provides a simple but very effective control solution for any periodic signal with a known period. Multirate repetitive control (MRC) with a downsampling rate can reduce the need of memory size and computational cost, and then leads to a more feasible design of the plug-in repetitive control systems in practical applications. However, with fixed sampling rate, both MRC and its linear phase lead compensator are sensitive to the ratio of the sampling frequency to the frequency of interested periodic signals: (1) MRC might fails to exactly compensate the periodic signal in the case of a fractional ratio; (2) linear phase lead compensation might fail to enable MRC to achieve satisfactory performance in the case of a low ratio. In this paper, a universal fractional-order design of linear phase lead compensation MRC is proposed to tackle periodic signals with high accuracy, fast dynamic response, good robustness, and cost-effective implementation regardless of the frequency ratio, which offers a unified framework for housing various RC schemes in extensive engineering application. An application example of programmable AC power supply is explored to comprehensively testify the effectiveness of the proposed control scheme

Virtual Delay Unit Based Digital nk ± m-order Harmonic Repetitive Controller for PWM Converter

Repetitive control (RC) scheme presents an attractive solution to achieve excellent steady-state tracking error and low total harmonic distortion (THD) for periodic signals. RC can produce extremely large gains at fundamental and each harmonic frequency of reference signal to achieve all harmonics suppression. However, a DC-AC inverter always has uneven THD distribution, e.g. THD concentrates at 4fc ± 1 orders for signal-phase inverter, and 6k ± 1 orders for three-phase inverter. Furthermore, a digital RC requires a integral ratio of the sampling frequency and the reference frequency, whereas the digital control system cannot always meet this requirement. For example, (e.g. 60 Hz reference signal with a 5 kHz sampling frequency, or grid-connected converter under grid frequency fluctuation, etc.). In this paper, virtual delay unit (VDU) based digital nk ± m-order harmonic RC is presented to solve the problems above. The VDU produces a different virtual RC sampling frequency from the system sampling frequency. The virtual sampling frequency for digital RC can be flexibly adjusted based on the integral ratio requirement. The advantage of VDU is that it does not vary the system sampling frequency and it is easy to be realized. Furthermore, nk ± m-order harmonic repetitive controller is selected to provide a selective harmonic compensation (SHC). Experimental results of VDU based nk±m-order harmonic RC for 60 Hz single-phase DC/AC inverter with 5 kHz system sampling frequency are provided to show the effectiveness of the proposed VDU-based SHC

Virtual variable sampling discrete fourier transform based selective odd-order harmonic repetitive control of DC/AC converters

This paper proposes a frequency adaptive discrete Fourier transform (DFT) based repetitive control (RC) scheme for DC/AC converters. By generating infinite magnitude on the interested harmonics, the DFT-based RC offers a selective harmonic scheme to eliminate waveform distortion. The traditional DFT-based selective harmonic RC, however, is sensitive to frequency fluctuation since even very small frequency fluctuation leads to a severe magnitude decrease. To address the problem, virtual variable sampling method, which creates an adjustable virtual delay unit to closely approximate a variable sampling delay, is proposed to enable the DFT-based selective harmonic RC to be frequency adaptive. Moreover, a selective odd-order harmonic DFT filter is developed to deal with the dominant odd order harmonic. Because it halves the number of sampling delays in the DFT filter, the system transient response gets nearly 50% improvement. A comprehensive series of experiments of the proposed VVS DFT-based selective odd-order harmonic RC controlled programmable AC power source under frequency variations are presented to verify the effectiveness of the proposed method

Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance

The chest X-ray is often utilized for diagnosing common thoracic diseases. In recent years, many approaches have been proposed to handle the problem of automatic diagnosis based on chest X-rays. However, the scarcity of labeled data for related diseases still poses a huge challenge to an accurate diagnosis. In this paper, we focus on the thorax disease diagnostic problem and propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents and the model parameters can also be continuously updated as the data increases, like a person's learning process. Especially, 1) prior knowledge can be learned from the pre-trained model based on old data or other domains' similar data, which can effectively reduce the dependence on target domain data, and 2) the framework of reinforcement learning can make the diagnostic agent as exploratory as a human being and improve the accuracy of diagnosis through continuous exploration. The method can also effectively solve the model learning problem in the case of few-shot data and improve the generalization ability of the model. Finally, our approach's performance was demonstrated using the well-known NIH ChestX-ray 14 and CheXpert datasets, and we achieved competitive results. The source code can be found here: \url{https://github.com/NeaseZ/MARL}