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水下机械臂电动静液作动器的模型预测控制

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电动静液作动器(EHA)作为水下液压机械臂的作动系统具有诸多优势.为提高EHA的控制性能,采用基于卡尔曼滤波的模型预测控制(MPC)方法,为提高预测效率,MPC的预测模型采用线性化的EHA模型.在Simulink环境中实现水下机械臂EHA的MPC控制,并与PID和滑模控制(SMC)进行对比.仿真结果表明:MPC的位置跟踪稳态误差约为SMC的10%~24%,为PID的3%~37%;出现负载扰动之后,MPC的平均受扰偏差仅为SMC的31%~66%,为PID的3%~48%;随着油液弹性模量的降低,3种控制方法下的受扰偏差均呈上升趋势,但MPC的上升趋势更加平缓,平均偏差值也较低.这表明MPC控制方法具有更高的位置跟踪精度和更强的鲁棒性.
Model Predictive Control of Electro-Hydrostatic Actuator for Underwater Manipulator
Electro-hydrostatic actuator(EHA)has many advantages as the actuator system of underwater hydraulic manipulator.In order to improve the control performance of EHA,the model predictive control(MPC)method based on Kalman filter was adopted.To improve prediction efficiency,the prediction model of MPC adopted a linearized EHA model.MPC control for the underwater manipu-lator EHA was implemented in Simulink simulation environment and it was compared with PID and sliding mode control(SMC).The simulation results show that the steady-state error of MPC is about from 10%to 24%of SMC and from 3%to 37%of PID.After load disturbance,the average disturbance deviation of MPC is only from 31%to 66%of SMC and from 3%to 48%of PID.With the decrease of oil elastic modulus,the disturbed deviation under the three control methods shows an upward trend,but the upward trend of MPC is gentler and the average deviation value is lower.This indicates that the MPC control method has higher position tracking accuracy and stronger robustness.

underwater robot armmodel predictive controlelectro-hydrostatic actuatorrobustness analysisKalman filter

王朋飞、朱建阳、赵慧

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武汉科技大学,冶金装备及其控制教育部重点实验室,湖北武汉 430081

武汉科技大学机器人与智能系统研究院,湖北武汉 430081

水下机械臂 模型预测控制 电动静液作动器 鲁棒性分析 卡尔曼滤波器

国家自然科学基金项目

51505347

2024

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子:0.32
ISSN:1001-3881
年,卷(期):2024.52(11)