Mingming Liu

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About

Mingming Liu’s research lies at the intersection of robotics, embedded systems, and intelligent control, with a particular focus on enhancing the real-time performance and adaptability of autonomous mobile platforms. In his most cited work, “FPGA Implementation of Family Service Robot Based on Neural Network PID Motion Control System” (2019, 9 citations), Liu addresses a critical limitation in traditional PID control for mobile robots: the fixed invariability of control parameters, which often leads to poor real-time response and chassis instability. By integrating a BP neural network with an FPGA-based architecture, he developed a motion control system that dynamically adjusts PID parameters, significantly improving the robot’s responsiveness and stability to upper-level motion commands. This contribution is notable for bridging neural adaptive control with hardware-level implementation, offering a practical solution for service robots in domestic environments. Liu’s work demonstrates a strong commitment to advancing embedded intelligent systems, and his approach has influenced subsequent designs in real-time robotic control. His research continues to inspire engineers seeking to combine machine learning with low-latency hardware for more reliable and autonomous robotic applications.