摘要
动力学模型是进行动力学特性分析的基础,同时也是实现机构高精度时实控制的前提。以一种新型驱动冗余并联机构为研究对象,采用Lagrange方程法建立了基于工作空间的动力学模型,并借助最小2范数法实现机构工作空间的非约束等效广义力到轴向驱动力的优化。由于机构动力学方程存在非线性和强耦合特性,计算量大,难以满足实时控制要求,为此,通过对机构各主要构件所引入的驱动力的分析,提出基于RBF神经网络误差补偿的动力学模型简化方案。仿真结果验证了所建模型的正确性和模型简化方法的有效性。
Dynamics plays an important role in the application of parallel mechanism, which is the key to analyze the dynamic characteristics and achieve high-precision operation. The inverse dynamic model for a novel redundantly actuated parallel mechanism is formulated in the task space using Lagrangian formalism and the driving force is optimized by utilizing the minimal 2-norm method. By investigating the contribution of each term in the dynamic model to the driving force, a simplified strategy of the dynamic model for real-time control application is presented, and the subsequent model error is compensated by adopting RBF neural network. Simulation results verify the correctness and effectiveness of the proposed dynamic model and simplified strategy.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2014年第19期41-49,共9页
Journal of Mechanical Engineering
基金
国家自然科学基金(51375210)
江苏省高校优势学科建设工程(苏政办发(2011)6号)
江苏省高校研究生科研创新计划(CXLX11_0598)
江苏大学高级专业人才科研启动资金(13JDG047)资助项目
