无人变胞车多构态分级优化设计及运动控制

doi:10.3901/JME.2025.10.305

机械工程学报 ›› 2025, Vol. 61 ›› Issue (10): 305-321.doi: 10.3901/JME.2025.10.305

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无人变胞车多构态分级优化设计及运动控制

刘俊, 刘宏勋, 朱嘉炜, 崔滔文, 陈无畏

合肥工业大学汽车与交通工程学院合肥 230009

收稿日期:2024-06-08 修回日期:2024-12-20 发布日期:2025-07-12
作者简介:刘宏勋，男，2001年出生，硕士研究生。主要研究方向为车辆动力学与控制。E-mail：3136977564@qq.com朱嘉炜，男，1999年出生，硕士研究生。主要研究方向为车辆动力学与控制。E-mail：zhujiawei0528@qq.com崔滔文，男，1992年出生，博士，讲师。主要研究方向为车辆动力学与控制。E-mail：nuaa_ctw@126.com陈无畏，男，1951年出生，博士，教授。主要研究方向为车辆动力学与控制。E-mail：hfgdcjs@126.com;刘俊(通信作者)，男，1972年出生，博士，副教授，硕士研究生导师。主要研究方向为无人变形车机构学分析及运动控制、车辆动力学控制、汽车液压传动与控制等。E-mail：ljun@hfut.edu.cn
基金资助:
国家自然科学基金(51875148)和安徽省重点研发计划(202104a05020040)资助项目。

Classification Optimization and Motion Control of Multi-configuration Unmanned Metamorphic Vehicle

LIU Jun, LIU Hongxun, ZHU Jiawei, CUI Taowen, CHEN Wuwei

School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009

Received:2024-06-08 Revised:2024-12-20 Published:2025-07-12

摘要/Abstract

摘要： 无人变胞车是在汽车结构基础上引入变胞机构而设计的一种新型可变形机器人，能适应不同路况进行轮式或腿足式移动。其结构参数、位姿参数对于各个构态的运动稳定性和能耗影响程度不同，因此结合调整质心机构对无人变胞车参数进行分级优化设计。采用Sobol灵敏度算法制定了分级优化方案：针对行走稳定性及能耗，采用蜂鸟优化算法(Artificial hummingbird algorithm，AHA)对腿部结构参数进行优化设计；针对重构过程，设计滑模控制器，并在考虑汽车态稳态转向行驶稳定性前提下，结合非支配排序遗传算法(Non-dominated sorting genetic algorithm-Ⅲ，NSGA-Ⅲ)对结构参数、位姿参数及滑模控制器控制参数进行多目标集成优化设计。通过仿真及试验结果表明：在重构工况下，分级优化后的无人变胞车表现出更高的稳定性、更低的能耗及更好的控制品质；在汽车态时，显著提高系统的稳态转向稳定行驶性能；在类人态时，降低了行走中跨步过程能耗，提高行走稳定性，从而更加利于行走。针对不同构态建立分级优化方法，不仅降低后续优化时的计算复杂度，而且可以得到适用于无人变胞车多个构态的全局最优参数。

关键词: 无人变胞车, 运动稳定性与能耗, 稳定性滑模控制, 多构态分级优化, 集成设计

Abstract: An unmanned metamorphic vehicle is a new type of deformable robot designed by introducing metamorphic mechanism on the basis of vehicle structure, which can adapt to different road conditions for wheeled or legged movement. Its structural parameters and posture parameters have different degrees of influence on the stability and energy consumption of the motions of each configuration. Therefore, the parameters of the unmanned metamorphic vehicle are classification optimized combined with the center-of-mass position-adjusting mechanism. The Sobol sensitivity algorithm is used to develop a classification optimization scheme: for walking stability and energy consumption, the Hummingbird optimization algorithm is used to optimize the design of the leg structural parameters; for the reconfiguration process, a sliding mode controller is designed, and the NSGA-III optimization algorithm is used to optimize the structural parameters, the posture parameters, and the control parameters of the sliding mode controller for the multi-objective integrated optimization on the premise of the vehicle's state-steady steering and driving stability. The simulation and test results showed that the unmanned metamorphic vehicle after classification optimization perform higher reconfiguration stability, lower energy consumption, and better control quality under the reconfiguration condition; in the vehicle state, the steady-state steering stability of the system during driving are significantly improved; in the humanoid state, the energy consumption of the striding process in the walking process is reduced and the walking stability is improved, thus is more conducive to walking. Established hierarchical optimization method for different configurations not only reduces the computational complexity during subsequent optimization, but also obtains the globally optimal parameters applicable to multi-configuration of unmanned metamorphic vehicle.

Key words: unmanned metamorphic vehicle, motion stability and energy consumption, sliding mode control for stability, multi-configuration classification optimization, integrated design

中图分类号:

TP273

刘俊, 刘宏勋, 朱嘉炜, 崔滔文, 陈无畏. 无人变胞车多构态分级优化设计及运动控制[J]. 机械工程学报, 2025, 61(10): 305-321.

LIU Jun, LIU Hongxun, ZHU Jiawei, CUI Taowen, CHEN Wuwei. Classification Optimization and Motion Control of Multi-configuration Unmanned Metamorphic Vehicle[J]. Journal of Mechanical Engineering, 2025, 61(10): 305-321.

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无人变胞车多构态分级优化设计及运动控制

Classification Optimization and Motion Control of Multi-configuration Unmanned Metamorphic Vehicle

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