Professor and Doctoral Supervisor at the School of Automation, Beijing Institute of Technology (BIT).
His current research focuses on intelligent control, optimization, game theory, and their applications in fields such as intelligent unmanned systems and integrated energy systems. In recent years, he has undertaken a number of scientific research projects, including those funded by the National Natural Science Foundation of China (NSFC), the International Cooperation Special Project of the Ministry of Science and Technology, the Beijing Natural Science Foundation, and the State Grid Corporation of China. he leads his team in developing intelligent unmanned aerial vehicle (UAV), unmanned underwater vehicle (UUV), and bionic robotic fish platform systems based on multi-sensory information fusion. These systems enable path planning, target recognition, and formation coordination of heterogeneous groups in complex scenarios, and have been applied to autonomous line inspection, fault diagnosis, and other tasks in the power industry and other sectors. he has published more than 80 academic papers in IEEE Transactions and other prestigious journals.
Bionic robotic fish, as an autonomous underwater vehicle (AUV) that mimics the behavior and morphology of biological fish in nature, possesses prominent advantages in underwater concealment and maneuverability. This presentation will cover the design, dynamic model, control strategy, and perception technology of bionic robotic fish, aiming to demonstrate the latest research progress and broad application potential of robotic fish technology.
Starting with the mechanical structure design of bionic robotic fish, the presentation will introduce multi-sensor fusion technology based on optics, inertia, and other sensors, as well as methods for establishing kinematic and dynamic models based on bionic analysis, data-driven approaches, and fluid mechanics theory. In the section on control strategies, it will present the control technologies developed for the characteristics of robotic fish, and analyze the comprehensive performance in achieving autonomous control and completing complex tasks.
马中静，北京理工大学自动化学院，教授、博士生导师。目前主要从事智能控制、优化、博弈理论及其在智能无人系统、综合能源系统等领域的应用；近年来承担了国家自然科学基金、科技部国际合作专项、北京市自然科学基金、国家电网等多项科研课题；带领团队开发了基于多感知信息融合的智能无人机、UUV和仿生机器鱼平台系统，实现了复杂场景下的路径规划、目标识别以及异构群体的编队协同运行等，并应用于电力等行业的线路自主巡检、故障诊断等，在IEEE汇刊等发表学术论文80余篇。
报告题目：仿生机器鱼系统的设计与控制决策研究
报告摘要：仿生机器鱼，作为一种模仿自然界生物鱼类行为和形态的自主水下机器人，具有突出的水下隐蔽性、机动性优势。本次报告将涉及仿生机器鱼的设计、动力学模型、控制策略、感知技术等，旨在展现机器鱼技术的最新研究进展与广泛应用潜力。从仿生机器鱼的机械结构设计展开，介绍基于光学与惯性等多传感器的融合技术，以及基于仿生学分析、数据驱动方法、流体力学理论的运动学与动力学模型建立方法；在控制策略部分，将介绍针对机器鱼特性研发的控制技术，并分析完成自主控制和复杂任务时的综合效能等。
