长行程高精度双级执行器建模与控制方法研究

发布时间：2018-12-17 04:34

【摘要】：长行程和高精度是当前驱动进给系统面临的需求和挑战。以传统电机作为宏执行器,虽然能提供较大的行程,但是响应速度慢和精度低的缺陷,使其难以在精密定位系统中发挥作用。而基于智能材料的微执行器响应速度快、定位精度高,但是行程有限。因此需要合理地设计宏执行器和微执行器的控制方案,结合宏执行器和微执行器的优点,使得整个双级执行器实现长行程高精度的定位。主要研究工作和成果如下:(1)考虑一类含有非匹配不确定性的非线性系统,Bouc-Wen模型用于描述非线性系统中的迟滞非线性,采用径向基神经网络(RBFNN),对非平滑、多映射的迟滞非线性进行在线逼近,将时变的迟滞转化为权值矩阵的学习,通过李雅普诺夫稳定性理论得到权值矩阵的更新律。设计的多层滑模自适应控制器,能够有效地解决系统中含有非匹配不确定性的问题,并可将系统跟踪误差收敛到预设的边界层内。(2)针对一类含有backlash-like迟滞特性的状态受限非线性系统,基于障碍型李雅普诺夫函数(BLF)和径向基神经网络(RBFNN)设计了一种输出反馈控制器。首先,在系统状态不可测的情况下,通过BLF解决系统状态受限问题。其次引入RBF神经网络近似逼近backlash-like迟滞中的类扰动项,在类扰动项界未知的情况下,削弱迟滞效应对系统的影响。最后通过李雅普诺夫稳定性定理设计控制器,证明了闭环系统的稳定性,仿真结果表明了该控制方案的可行性。(3)针对质量-弹簧-阻尼结构描述的双级执行器,采用解耦控制方式,宏执行器跟踪参考信号,而微执行器补偿宏执行器的跟踪误差。只要保证宏执行器的跟踪误差在微执行器的执行范围以内,就能使整个双级执行器的输出稳定在目标值上。为宏执行器设计了近似时间最优控制器,能实现快速定位;微执行器采用基于预测控制的离散滑模控制器,对系统中匹配的或非匹配的不确定性都有很强的鲁棒性,且能有效地削弱滑模控制信号的抖振。仿真结果表明了该控制方案的可行性。
[Abstract]:Long stroke and high precision are the requirements and challenges of current drive feed system. Using traditional motor as macro actuator, although it can provide a large stroke, it is difficult to play a role in precision positioning system due to the shortcomings of slow response speed and low precision. The micro actuators based on smart materials have fast response speed and high positioning accuracy, but the stroke is limited. Therefore, it is necessary to design the control scheme of macro actuator and micro actuator reasonably, and combine the advantages of macro actuator and micro actuator to realize the long stroke and high precision positioning of the whole double stage actuator. The main work and results are as follows: (1) considering a class of nonlinear systems with mismatched uncertainties, the Bouc-Wen model is used to describe the hysteresis nonlinearity of nonlinear systems, and the radial basis function neural network (RBFNN),) is used to describe the nonsmoothness. The hysteresis nonlinearity of multiple mappings is approximated on line, and the time-varying hysteresis is transformed into the learning of weight matrix, and the updating law of weight matrix is obtained by Lyapunov stability theory. The multi-layer sliding mode adaptive controller designed can effectively solve the problem of mismatch uncertainty in the system. The tracking error of the system can be converged to the preset boundary layer. (2) for a class of state-constrained nonlinear systems with backlash-like hysteresis, An output feedback controller is designed based on the barrier Lyapunov function (BLF) and the radial basis function neural network (RBFNN). Firstly, under the condition that the system state is unmeasurable, the problem of system state restriction is solved by BLF. Secondly, the RBF neural network is introduced to approximate the perturbation term in backlash-like hysteresis, which weakens the effect of hysteresis on the system when the bound of the perturbation term is unknown. Finally, the stability of the closed-loop system is proved by Lyapunov stability theorem. The simulation results show the feasibility of the control scheme. (3) the two-stage actuator described by mass-spring-damping structure. Using decoupling control, the macro actuator tracks the reference signal, while the micro actuator compensates for the tracking error of the macro actuator. As long as the tracking error of the macro actuator is within the execution range of the microactuator, the output of the whole two-stage actuator can be stabilized at the target value. The approximate time optimal controller is designed for the macro actuator, which can realize fast positioning. The micro actuator adopts a discrete sliding mode controller based on predictive control, which is robust to both matching and mismatching uncertainties, and can effectively reduce the chattering of sliding mode control signal. The simulation results show the feasibility of the control scheme.
【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP273

【参考文献】