实时瞬态响应优化
Real-Time Transient Response Optimization
AI总结：
本文提出集成于微控制器的补充控制器方案，实现100%实时数据覆盖，可降低系统响应误差达68%，引入新型关键性能指标，为电机控制提供全新系统级优化能力。
AI中文摘要：
可部署在微控制器中的电机控制应用人工智能技术需满足电机控制系统的实时需求，本文提出一种可集成于现有微控制器的补充控制器方案。该方案已在weeteq电机控制集成电路上实现并通过实验室测试，开发出完整的无监督电机控制部署方案，在动态负载下验证了补充控制器的实时系统响应校正效果。该方案在控制回路采样率为100微秒至1毫秒、模型推理周期内，实现了100%的实时数据覆盖率；证明了延迟对降低瞬态响应期间动态调节裕度的重要性，可将系统响应误差降低多达68%。本文引入了一种基于主成分变换的新型关键性能指标，该指标可从动态调节裕度、稳定性考量及连续回归模型输出的迭代改进等方面，为瞬态响应的提升提供定量性能指标。此外，还以毫秒级分辨率检测设备及外部运行条件动态变化相关的关键事件，并将其记录为高度压缩的向量，用于表征系统响应与线性稳态条件的偏差；该向量数据的时间分辨率与精度，将实现当前设备健康监测方案中物联网传感器时间序列数据无法达成的全新系统级优化水平。
英文摘要：
Use of artificial intelligence in motor control applications that can be deployed within the microcontrollers need to comply with real-time demands of motor control systems. A supplementary controller approach that can be integrated within existing microcontrollers is presented. The proposed approach is implemented on a weeteq motor control integrated circuit and tested in the lab. A complete unsupervised motor control deployment solution was developed, and the real-time system response correction demonstrated under dynamic loads measured with and without the supplementary controller. The solution provides 100% coverage of real-time data at control loop sample rate and model inference period between 100usec and 1msec. The importance of latency for reduction of dynamic regulation margin during transient response is demonstrated with up to 68% reduction of the system response error. A novel key performance indicator based on principal components transform is introduced that provides a quantitative figure of merit for improvement of the transient response, in terms of the dynamic regulation margin, stability considerations and iterative improvements of consecutive regression model outputs. The significant events related to dynamic changes in the equipment and external operating conditions are detected at milliseconds resolution and recorded as highly compressed vectors representing deviation of the system response from linear steady state conditions. The resolution in time and accuracy of this vector data will enable a new level of system level optimisation that has not been possible using the time series data from IoT sensors in current equipment health monitoring solutions.
