摘要
针对半主动型膝关节置换手术机器人的柔顺跟随医生意图运动和手术安全范围约束问题,提出了一种模型预测导纳控制算法.首先,为了提高算法运行效率,使用斯特林插值方法对机械臂动力学模型进行线性化,并作为预测模型,该方法计算简单且求解精度高;其次,基于导纳模型力柔顺控制机理识别操作医生的力意图生成机械臂期望运动轨迹;然后,设计虚拟状态,提高模型预测控制算法的显式处理约束能力,利用模型预测控制的滚动优化和反馈校正特性提高控制鲁棒性;最后,结合成机械臂模型预测导纳控制器.设计三环PID(Proportional integral derivative)控制对照试验,验证了模型预测控制算法的轨迹跟踪性能更好,可以更好的实现期望的导纳动态,从而得到更优的柔顺效果.在此基础上,进一步验证模型预测导纳控制器在具有强耦合性、复杂系统参数结构特性的机械臂上的主动约束效果.结果表明模型预测导纳控制算法能实现比传统三环PID控制更好的柔顺性,且具备满足膝关节置换手术需求的安全性.本文有望促进半主动手术机器人的实际应用.
In response to the compliant tracking of surgical intent and adherence to safety range constraints in semi-active knee arthroplasty robots,a model predictive impedance control(MPIC)algorithm is proposed.First,to enhance this algorithm’s operational efficiency,the Stirling interpolation method is employed to linearize the dynamics model of the robotic arm as the predictive model.This method offers computational simplicity and high-precision solving accuracy.Second,based on the impedance model,the forcecompliant control mechanism is used to identify the surgeon’s force intention,thereby generating the desired motion trajectory for the robotic arm.To facilitate programming implementation,the impedance model is discretized.Third,leveraging the rolling optimization and feedback correction properties of model predictive control,a virtual state enhancement is designed to improve the explicit constraint handling capability of the MPIC algorithm.This enhancement addresses the infeasibility issues encountered by traditional model predictive control near state constraint boundaries in practical applications.Transforming the model predictive problem into a quadratic programming problem reduces the difficulty of solving the model predictive problem and increases problem-solving speed.Finally,MPIC is integrated as the lower-level position-tracking controller for the robotic arm,with the impedance model serving as the upperl evel task planning controller,thus forming the MPIC controller.Comparative experiments with three-loop PID(Proportional integral derivative)control are conducted on the ROKAE seven-axis collaborative robot experimental platform,confirming that the MPIC algorithm achieves better trajectory tracking accuracy and response speed,effectively realizing the desired impedance dynamics and yielding superior compliance.Additionally,further validation is conducted by installing a six-axis force sensor between the end-effector and the wrist of the robotic arm to measure human–robot interaction forces,confirming that the MPIC algorithm exhibits better compliance than traditional position-tracking control methods.Drag experiments are designed to verify the active constraint effect of MPIC on mechanically coupled robotic arms with complex system parameter structures,demonstrating that the control algorithm can actively constrain the motion of the robotic arm when it is manually manipulated to exceed the set state constraint range.Overall,the MPIC algorithm achieves better compliance and meets the safety requirements for knee arthroplasty surgery compared to traditional three-loop PID control methods.This advancement holds promise for further development and adoption of semi-active surgical robots,reducing the complexity of using surgical robots as surgeons and accelerating the widespread adoption of domestically produced surgical robots in hospitals.This paper should promote the practical application of semi-active surgical robots.
作者
胡飘
张丽
杨闳竣
杨妍
HU Piao;ZHANG Li;YANG Hongjun;YANG Yan(School of Artificial Intelligence,China University of Mining and Technology(Beijing),Beijing 100083,China;State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;School of Intelligence Science and Technology,University of Science and Technology Beijing,Beijing 100083,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2024年第9期1638-1646,共9页
Chinese Journal of Engineering
基金
北京市自然科学基金-海淀原始创新联合基金资助项目(L212034)
国家自然科学基金资助项目(62103039)。
关键词
手术机器人
人机交互
模型预测
导纳控制
安全约束
surgical robot
human–robot interaction
model predictive control
admittance control
safety constraints
