介电高弹聚合物运动轨迹跟踪控制研究

Trajectory Tracking Control Study of Dielectric High-Elastic Polymers

针对介电高弹聚合物的轨迹跟踪控制问题,本文提出自适应径向基函数(radial basis function,RBF)神经网络滑模控制(sliding mode control,SMC)算法,实现了对介电高弹聚合物运动轨迹的跟踪控制。首先考虑超弹性和粘黏性等引起动力学模型中的未知项,外界干扰导致模型的不确定性,利用RBF神经网络的任意逼近连续函数特性、滑模控制的鲁棒性和自适应控制在线调整控制率设计控制器,并构造Lyapunov函数,证明控制器的稳定性,实现了对介电聚合物轨迹跟踪控制,结果显示聚合物的位置跟踪极值误差小于1%,响应时间约为0.7 s,表明系统具有较高的动态性能,实现了结构的线性运动轨迹跟踪。同时,分析了激励频率和宽度预拉伸比对结构跟踪轨迹的影响规律,设计了系数参数的优化方法。研究结果表明,该控制器提高了轨迹跟踪的精确度,验证了所提控制系统具有较强的自适应性、稳定性及优化方法的有效性。该研究为软体机器人的仿生驱动与轨迹跟踪控制提供了理论依据。

This paper proposes an adaptive Radial Basis Function(RBF)neural network sliding mode control(SMC)algorithm for the trajectory tracking control of dielectric high-elasticity polymers.Firstly,the unknown terms in the kinetic model caused by hyperelasticity and viscosity,and the uncertainty of the model caused by external disturbance are considered.The controller is designed by using the arbitrary approximation of continuous function property of RBF neural network,the robustness of sliding mode control,and the online adjustment of control rate by adaptive control.The Lyapunov function is then constructed to demonstrate the stability of the controller as a means of achieving tracking control of the dielectric polymer trajectory.The results show that the extreme value error of the polymer position tracking is less than 1%and the response time is 0.7 s,indicating that the system has high dynamic performance and achieves linear motion trajectory tracking of the structure.Finally,the influence of excitation frequency and width pre-stretch ratio on the tracking trajectory of the structure is analysed,and an optimisation method of the coefficient parameters is designed.The results show that the effectiveness of the optimisation method improves the accuracy of the trajectory tracking,and also verifies that the proposed control system has strong self-adaptability and stability,which provides a theoretical basis for the research of bionic drive and trajectory tracking control of soft robots.