Real-Time Transient Response Optimization

Abstract

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.