An experimental high-performance four-channel DC motor controller based on Renesas RA8M1 microcontroller with advanced motion control algorithms and comprehensive filesystem support.

MC80_4DC is an experimental DC motor controller designed for precision motion control applications. The project demonstrates advanced embedded systems programming with real-time operating system integration, high-speed communication interfaces, and motor control with analog position feedback.

⚠️ Note: This is an experimental project under active development. Some features may be incomplete or subject to change.

• Four Independent DC Motor Channels with individual control
• Analog Position Feedback: High-precision analog sensors for position monitoring
• High-Performance MCU: Renesas RA8M1 (ARM Cortex-M85, 480MHz)
• Real-Time Operating System: Azure RTOS ThreadX integration
• Storage Solutions:
  • exFAT filesystem on SD card for data logging and configuration
  • Custom OSPI driver for external flash memory (under development)
• Communication Interfaces: CAN 2.0 for industrial applications
• Development Environment: Visual Studio Code for code editing, IAR Embedded Workbench 9.70 for compilation
• Comprehensive Debug Support: RTT logging, VT100 terminal interface, SWD/JTAG debug interfaces, and hardware trace

The MC80_4DC motor controller is designed for a wide range of automation and motion control applications:

• Multi-leaf Automatic Doors: Precise control of synchronized door panels
• 3D Rotating Platforms: Solar panel tracking systems and antenna positioning
• Automated Blinds and Awnings: Smart building automation and shading systems
• Gate and Barrier Control: Access control systems and parking barriers
• Stair Lifts: Mobility assistance and accessibility solutions
• Industrial Automation: Various precision motion control applications

• MCU: Renesas RA8M1AH3GT (ARM Cortex-M85, 480MHz)
• Internal Flash: 2MB
• External Flash Memory: MX25UM25645GMI00 (256Mbit OSPI)
• RAM: 1MB internal SRAM
• Package: LQFP176

• Channels: 4 independent DC motor drivers
• Servo Sensors: 2 analog inputs for servo sensor feedback
• Quadrature Encoders: 2 interfaces for incremental position encoders
• Hall Sensors: 2 interfaces for 3-phase Hall sensor inputs
• Analog Inputs: Equipped with differentiator for low speed measurement
• Current Monitoring: Real-time current sensing capabilities
• PWM Generation: High-frequency PWM for motor control

• SD Card: exFAT filesystem for data storage
• CAN 2.0: Industrial communication protocol
• USB: Device and host support

• SWD: Serial Wire Debug interface for programming and debugging
• Trace: Hardware trace interface for real-time debugging

The MC80_4DC board supports multiple control interfaces for different operational scenarios:

• USB Control Software: Specialized control and monitoring software for PC-based testing and debugging
• Terminal Programs: Direct control via USB using standard terminal applications
• Debug Interface: Full development support through SWD and trace interfaces

• CAN Bus Control: Primary control interface for industrial applications
• Real-time Communication: Deterministic control through CAN 2.0 protocol
• Distributed Systems: Integration into larger automation networks

The project is built on Azure RTOS ThreadX, providing:

• Preemptive multitasking
• Real-time scheduling
• Inter-thread communication
• Memory management
• Timer services

DC motor control system featuring:

• Analog Sensor Interface: Multi-channel ADC for position feedback
• Current Monitoring: Real-time current sensing and monitoring
• PWM Control: Hardware-generated PWM signals for motor driving

Advanced storage solution using exFAT on SD card:

• FAT32-compatible filesystem
• Long filename support
• Efficient storage management
• Configuration file storage
• Data logging capabilities

High-performance OSPI flash driver featuring:

• Memory-mapped access capabilities
• DMA-based transfers for maximum throughput
• Protocol switching (SPI ↔ Octal DDR)
• Comprehensive error handling
• Note: Driver development is ongoing

• Motor Control Task: Real-time motor control operations
• Sensor Processing Task: ADC data acquisition and processing
• Communication Task: Handle CAN 2.0 communication
• Filesystem Task: Background file operations on SD card
• Monitor Task: System health monitoring and diagnostics

• Sensor Sampling: High-frequency ADC conversion
• Interrupt Response: <5μs average interrupt latency
• Task Switching: <1μs context switch time
• File Operations: SD card access with optimized buffering

This project is licensed under the MIT License - see the LICENSE file for details.

This release marks the successful completion of comprehensive filesystem testing and performance analysis for the MC80_4DC platform. The testing framework has been fully validated and provides detailed performance metrics for both supported filesystems.

• Complete Filesystem Testing Framework: Unified testing interface supporting both LittleFS and FileX filesystems
• Performance Analysis Tools: Comprehensive Python-based analysis suite with detailed performance graphs
• Unified Statistics Engine: Common performance measurement framework for both filesystems
• Bug Fixes: Resolved speed calculation issues and disk space reporting accuracy
• Configuration Optimization: FileX directory entry limits configured for optimal performance

The testing framework provides comprehensive analysis of:

• Read Performance: File open/close timing and throughput analysis
• Write Performance: Write speed analysis with first-file overhead measurements
• Delete Performance: Deletion speed analysis with performance degradation tracking
• Storage Efficiency: Accurate disk space utilization and percentage calculations

• Unified Test Functions: FileX read tests now include pattern validation and size verification
• Fixed Statistics Calculations: Corrected average and minimum speed calculations for delete operations
• Accurate Disk Usage: Fixed FileX media information display with proper used space calculations
• Python Analysis Suite: Created universal log parsers supporting both LittleFS and FileX log formats

The release includes Python scripts for detailed performance analysis:

• Parse_FS_read_log.py - Read operation analysis with timing breakdowns
• Parse_FS_write_log.py - Write operation analysis with speed metrics
• Parse_FS_delete_log.py - Delete operation analysis with performance tracking

All analysis tools support both filesystem formats and generate comprehensive matplotlib-based visualizations.

MC80_4DC - Experimental DC motor control with precision analog feedback.

⚠️ Disclaimer: This is an experimental project. Use at your own risk in production environments.