Muscle Memory Inspired Robotic-Joint-Multiaxis Motion Control Structure With Parallel Algorithm Modules and Sequential Random Access Processes

Multi-axis motion control systems have gained widespread adoption. However, the implementations of multi-axis servo control using distributed controllers are hindered by the data exchange rates of the communication bus. By contrast, centralized FPGA possess parallel computing capabilities, but their limited resources restrict their effectiveness in completing multi-axis controls all in parallel. Inspired by the concept of human muscle memory, this paper proposes a multi-axis motion control structure integrating parallel and sequential control components, where the parallel components are responsible for individual motor motion control and the sequential ones are for performing muscle memory operations, i.e., predefined functionalities such as a multi-axis motion curve. By employing time-division multiplexing technology, the multi-axis controller can efficiently manage parallel computation modules under FPGA resource constraints, so a typical FPGA can easily handle more than ten motors. With the designed sequential scheduling feature, the controller can randomly access the parallel modules to operate independently and complete their calculations within 1 microsecond. To validate this control structure, experiments are conducted on a test platform featuring four PMSMs, demonstrating that the proposed design facilitates rapid, flexible multi-axis motor control, achieving high-performance synchronous operation and enabling the extension of motor algorithms to multi-axis applications.