机器鱼运动学SSPR建模与自适应迭代学习控制  

Robotic fishes kinematics SSPR modeling and adaptive iterative learning control

作　　者：任光[1] 戴亚平[1] 

机构地区：[1]北京理工大学自动化学院,北京100081

出　　处：《控制与决策》2014年第9期1605-1610,共6页Control and Decision

基　　金：长江学者和创新团队发展计划项目(IRT1208)

摘　　要：针对一类多关节机器鱼推进速度的调节,提出一种运动学建模与控制匹配设计的新方法.以可控性为目标,建立了基于能量转化系数的鱼尾摆动规律与推进速度性能参考(SSPR)模型,系统已知参数把能量转化率收敛到一个可控可调节范围.自适应迭代学习控制策略与之匹配,能适时辨识并周期性地更新该模型的能量转化系数,实现机器鱼在陌生水环境中的推进速度自调节.仿真分析验证了该模型和控制方法的正确性.A class of modeling and control combination method is proposed to realize the speed adjustment of robotic fish with multi-joint caudal fin. Targeting at controllability, a swing rule and propulsion speed performance reference（SSPR） model is established based on an energy conversion coefficient, and the efficiency is converged to a controllable and adjustable range by system’s known parameters. Then, an adaptive iterative learning control policy is designed to match the SSPR model, the control system can identify and update the energy conversion coefficient timely and periodically, and realize the speed self-adjustment of robotic fish in a strange water environment. Simulation analysis verifies the correctness of the model and control method.

关 键 词：机器鱼 能量转换系数 参考模型 自适应迭代学习控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]