Arm Cortex-M4-based MCUs boost motor control performance while Google TensorFlow Lite Micro framework gives enhanced failure detection
Designed for motor control applications targeting smart homes, industrial automation and building automation, the new RA6T1 Group MCUs feature a rich set of peripheral functions and AI-based failure detection that meet the unique needs of motor control in home appliances, HVAC, solar inverters and AC drives.
Based on the Arm Cortex-M4 core, the new RA6T1 32-bit MCUs operate at 120 MHz and feature a rich collection of peripherals optimised for high performance and precision motor control. The integrated peripheral functions with high-speed analogue significantly reduce bill of materials (BOM) cost while boosting motor control performance. For example, a single RA6T1 MCU can simultaneously control up to two brushless DC (BLDC) motors.
Also, the Google TensorFlow Lite Micro framework for TinyML applications adds enhanced failure detection to the RA6T1 MCUs, offering customers an intelligent, easy-to-use and cost-effective sensorless motor system for predictive maintenance. The TensorFlow AI framework detects potentially detrimental anomalies in motor systems earlier and more accurately to help customers improve their predictive maintenance processes and reduce maintenance costs.
Key Features of the RA6T1 Group
- Scalable from 64-pin to 100-pin LQFP packages
- 64 KB RAM and scalable from 256 KB to 512 KB Flash
- 32-bit PWM timer with advanced functions, including support for seven types of complementary PWM modes for the carrier generation
- 250 usec sampling period when used with the motor control solution bundle
- High-speed 12-bit ADC with a maximum speed of 0.4 usec and a sample/hold function that allows simultaneous acquisition of 3 shunt currents
- 6-channel programmable gain amplifier
- Supports IEC 60730 standard for functional safety in home appliances
“As home appliances and building and industrial automation equipment become smarter and more complex, manufacturers are grappling with rising BOM costs to support increasing motor performance demands,” said Roger Wendelken, Senior Vice President of Renesas’ IoT and Infrastructure Business Unit. “The RA6T1 MCUs combine the superior performance and flexibility of the Arm-based RA Family with Renesas’ long-standing motor control expertise.”
“AI and machine learning are taking predictive maintenance to the next level as the industry advances toward Maintenance 4.0.,” said Ian Nappier, Product Manager at Google. “Integrating open-source TensorFlow AI framework with Renesas’ powerful RA6T1 MCUs brings breakthrough intelligence to motor control equipment.”
The Renesas Solution Starter Kit (RSSK) is suitable for developers working on a motor control solution using the RA6T1 MCUs. The RSSK offers easy motor control debugging and allows customers to immediately begin evaluating their motor control design, executing real-time analysis and tuning to accelerate development. The easy-to-use motor solution includes a RA6T1 CPU card and 48V-compatible inverter board, along with a GUI tool for the motor workbench and a sensorless vector control sample program with a three-shunt method that corresponds with the Flexible Software Package (FSP).
With this launch, Renesas Electronics Corporation has extended its Arm-based RA MCU family. The RA6T1 MCUs and RSSK are available now from Renesas’ worldwide distributors.
