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

Multiaxis motion control systems have gained widespread adoption. However, the implementations of multiaxis 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 multiaxis controls all in parallel. Inspired by the concept of human muscle memory, this article proposes a multiaxis 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 multiaxis motion curve. By employing time-division multiplexing technology, the multiaxis 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 μs. To validate this control structure, experiments are conducted on a test platform featuring four permanent magnet synchronous motors, demonstrating that the proposed design facilitates rapid, flexible multiaxis motor control, achieving high-performance synchronous operation and enabling the extension of motor algorithms to multiaxis applications.