Xinliang Yang

Xinliang Yang

A nomandic researcher swimming in the pool of optimization, control, circuits, artificial intelligence and embedded systems

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Star Observation

less than 1 minute read

Published:

From the facebook, we heard that there is possibility that we could notive meteor in the nearby area. So three energetic boys immediately drive a car towards the destination for capturing meteor phenomenon. Due to the ability limitation of my phone camera, it is hard to share with others the shining star flowing in the sky.

portfolio

publications

Two Effective Spectrum-Shaped FCS-MPC Approaches for Three-Level Neutral-Point-Clamped Power Converters

Published in 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 2021

Abstract: Unlike pulse width modulation (PWM) based approaches, the spectra of classical finite control set model predictive control techniques spreads uniformly across a wide frequency range for power converters, which makes the design of their filters particularly difficult. An effective method to achieve spectra similar to those of PWM for two-level power converters is the so-called model predictive period control (MPPC) approach, which makes the frequency of the converter’s output voltage quasi-fixed by adding a constraint on the switching frequency. In terms of output voltage change, voltage-state-change (VSC) is more relevant than switch-state-change (SSC) because the output voltage change is unequal to that of SSC for multi-level power converters. In this work we propose two different control techniques, namely, switch-state-change (SSC-MPPC) and voltage-state-change based model predictive period control (VSC-MPPC), for 3L-NPC power converters. Simulation data confirm that, i) both approaches achieve current spectra similar to those of PWM-based solutions, ii) both of them achieve quasi-fixed switch frequency, which simplifies the heat sink design, however, VSC-MPPC practically operates with half of the reference frequency for 3L-NPC converter, iii) SSC-MPPC outperforms the VSC-MPPC approach in terms of power quality and neutral point voltage regulation but consumes much more switching losses especially for high level power systems.

Fixed Switching Frequency Predictive Control for PMSM Drives With Guaranteed Control Dynamics

Published in 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 2021

Abstract: In this work, we propose a switched direct model predictive control (DMPC) method for permanent magnet synchronous motor (PMSM) drives; it achieves a fixed switching frequency while preserving fast control dynamics of the DMPC technique. In steady-state operation, the predictive period control (PPC) approach is adopted to carry out fixed switching frequency operation. Meanwhile, during transients, the classical DMPC (CDMPC) method is used to provide fast control dynamics. Furthermore, the criteria to switch between the two modes are presented in detail; these criteria ensure a smooth transition. The proposed method has been verified using a PMSM drive fed by a two-level (2L) voltage source converter. The simulation results confirm its effectiveness.

Artificial neural network-based FCS-MPC for three-level inverters

Published in Journal of Power Electronics, 2022

Abstract: Finite control set model predictive control (FCS-MPC) stands out for fast dynamics and easy inclusion of multiple nonlinear control objectives. However, for long horizontal prediction or complex topologies with multiple levels and phases, the required computation burden surges exponentially as the increases of candidate switch states during one control period. This phenomenon leads to longer sample period to guarantee enough time for traverse progress of cost function minimization. In other words, the allowed highest switching frequency is bounded considerably far from the physical limits, especially for wide-band semiconductor applications. To overcome this issue, the parallel computing characteristic of artificial neural network (ANN) motivates the idea of an ANN-based FCS-MPC imitator (ANN-MPC). In this article, ANN-MPC is implemented on a neutral point clamped (NPC) converter using a shallow neural network. The expert (FCS-MPC) is initially designed, and the basic structure, including activation function selection, training data generation, and offline training progress, and online operation of the imitator (ANN-MPC) are then discussed. After the design of the expert and imitator, a comparative analysis is conducted by field programmable gate array (FPGA) in-the-loop implementation in MATLAB/Simulink environment. The verification results of ANN-MPC show highly similarly qualified control performance and considerably reduced computation resource requirement.

A Weighting Factor Design Approach for FCS-MPC Techniques Based on PSO and K-Means Algorithm

Published in 2022 IEEE Energy Conversion Congress and Exposition (ECCE), 2022

Abstract: A cost function with proper weighting factors (WFs) enables finite control set model predictive control (FCS-MPC) of power electronics to include multiple physical constraints and control objectives. On the other hand, lack of systematical WFs design and with the classical empirical trial-and-error adjustment will become time-consuming and tedious, particularly for complex system topology and multiple nonlinear control objectives. In this paper, a novel WFs design strategy based on particle swarm optimization (PSO) and k-means algorithm for the classical FCS-MPC techniques is proposed. A grid-tied three-level neutral point clamped (3L-NPC) converter is chosen as an explanatory case of verification. The fitness values of all control objectives (here, current tracking, DC-link voltage balancing, and quasi-fixed instantaneous switching frequency) are based on integral time-weighted absolute error (IATE). Because the control objectives have different natures (different physic units and numerical magnitudes), instead of globally optimal WFs, there would be Pareto (non-dominated) solution set during PSO operation. Then the k-means cluster algorithm is used to select typical WFs for efficient selection from Pareto solutions. Simulation data confirm the effectiveness of the proposed solution, which guarantees qualified WFs design and is simple in calculation.

A Computationally Efficient FCS-MPC Imitator for Grid-Tied Three-Level NPC Power Converters Based on Sequential Artificial Neural Network

Published in 2022 IEEE Energy Conversion Congress and Exposition (ECCE), 2022

Abstract: Finite control set model predictive control (FCS-MPC) has attracted lots of attention recently thanks to its intuitive design and ease to include multiple control objectives and constraints. However, its computational demand surges exponentially with the number of independent switching pairs and the prediction horizons. To reduce the computational burden, we propose a fast FCS-MPC imitator based on two sequential shallow artificial neural networks (SANN). Compared with the existing ANN based strategies, the proposed solution has much reduced number of neurons in hidden layers, and is much suitable for redundant voltage vector selection. The proposed solution is tested with a grid-tied three-level neutral point clamped power converter (3L-NPC). Simulation results confirm that, i) the computational complexity is much reduced than both FCS-MPC and ANN-MPC while achieving similarly qualified control performance, ii) SANN-MPC shows better imitation accuracy than the classical ANN based MPC.

FCS-MPC Based Dual-module ANN Controller for Three-level Converter

Published in 2023 11th International Conference on Power Electronics and ECCE Asia (ICPE 2023 - ECCE Asia), 2023

Abstract: Finite-control-set model predictive control (FCS-MPC) shows great control performance and adaptability for different converter topologies and operating modes. However. The computation burden increases significantly for long prediction step and multi-level topology. Artificial neuron network (ANN) is developed to imitate FCS-MPC controller for similar control effect with lower computation burden. However, the imitation accuracy is not good enough for single ANN. To achieve acceptable control effect using a simple ANN, we propose an FCS-MPC-based dual-module ANN controller. We first off-line train the ANN to imitating the FCS-MPC. Then we designed a dual-module structure which combines ANN and FCS-MPC to increase the imitation accuracy. The simulation result shows that the accuracy of our design increases to 99.87% while the computation burden is reduced by 58.8% compared with FCS-MPC. It can achieve similarly control performance and significantly reduce computation burden.

talks

woshinidie

Published:

This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.