基于迭代学习控制的柔性结构振动控制设计与研究

发布时间：2019-05-12 04:59

【摘要】：与刚性结构相比,柔性结构质轻、灵活和低耗,广泛应用在航天航空、海洋立管以及机器人等工程领域。但是在实际工程环境中,外部干扰经常导致柔性结构振动,从而缩短使用寿命,严重时会导致系统瘫痪。在设计控制器的过程中,观测器和执行器会出现振幅受限、输出延迟等非线性情况,如果忽略这些非线性特性也会导致柔性结构系统的不稳定。本文主要考虑了两种非线性输入和四种柔性结构,其中柔性弦系统是最基本、最简单的柔性结构系统,可以由一个二阶的波动方程表示。在柔性弦系统中,本研究使用了两个饱和函数来处理受限输入。柔性伯努利-欧拉梁是一个四阶的高阶偏微分方程系统,其中双曲正切函数和饱和函数用来处理受限输入。具有受限输入的柔性机械臂系统是一个旋转的伯努利-欧拉梁系统,是一个横向振动与旋转相互耦合的柔性结构系统。相比较而言,具有backlash输入的柔性铁木辛柯梁系统是最为复杂的,涉及到了铁木辛柯梁的横向振动和其横截面的旋转。随着系统环境复杂度和控制目标精确度的提高,单一的控制方法无法完美的满足控制过程的需求。面对具有无穷自由度的分布式参数系统、受限输入、backlash输入、分布式干扰和边界干扰,单一的边界控制、自适应控制和迭代学习控制都无法实现闭环系统的渐进稳定。为此,本文使用了双环耦合的迭代学习方法,即将一个作为副环的学习环嵌套在一个作为主环的调控环。本文设计了双环耦合的边界迭代学习控制器和双环耦合的自适应迭代学习控制器。在这两类控制器中,副环在本质上都是一个典型的D型迭代学习控制率,主要是为了抑制系统振动和保证控制器的非线性特性。在双环耦合的边界迭代学习控制器中,主环本质上是一个边界控制法则,主要是通过系统状态的反馈来抑制分布式干扰和边界干扰。在双环耦合的自适应迭代学习控制器中,主环主要由观测器和系统状态反馈信号组成。通过定义复合能量函数,本文证明了闭环系统系统在每个迭代周期内的有界性和迭代轴上的收敛性。为了显示闭环系统的性能和所设计的控制器的有效性,本文做了MATLAB数字仿真和机械臂实验。在数字仿真中,本文对比了无控制外力下的开环系统和所设计控制器下的闭环系统。在机械臂实验中,本文对比了无控制下的开环系统、PD控制下的闭环系统和双环耦合的迭代学习控制下的闭环系统。
[Abstract]:Compared with rigid structure, flexible structure is light, flexible and low consumption, and is widely used in aerospace, marine riser, robot and other engineering fields. However, in the actual engineering environment, external interference often leads to the vibration of flexible structures, thus shortening the service life, which will lead to the paralysis of the system in serious cases. In the process of designing the controller, the observer and actuator will have nonlinear cases such as amplitude limitation and output delay. If these nonlinear characteristics are ignored, the instability of flexible structure system will also be caused. In this paper, two kinds of nonlinear inputs and four kinds of flexible structures are considered, in which the flexible string system is the most basic and the simplest flexible structure system, which can be represented by a second-order wave equation. In flexible string system, two saturation functions are used to deal with restricted input. Flexible Bernoulli-Euler beam is a fourth-order system of higher order partial differential equations, in which hyperbolic tangent function and saturation function are used to deal with restricted input. The flexible manipulator system with limited input is a rotating Bernoulli Euler beam system, which is a flexible structure system coupled with transverse vibration and rotation. In comparison, the flexible Timoxinke beam system with backlash input is the most complex, which involves the transverse vibration and the rotation of its cross section. With the improvement of the complexity of the system environment and the accuracy of the control target, a single control method can not meet the needs of the control process perfectly. In the face of distributed parameter systems with infinite degrees of freedom, restricted input, backlash input, distributed interference and boundary interference, single boundary control, adaptive control and iterative learning control can not achieve the progressive stability of the closed-loop system. In this paper, a double ring coupling iterative learning method is used, that is, a learning ring as a secondary ring is embedded in a control ring as a main ring. In this paper, the boundary iterative learning controller with double loop coupling and the adaptive iterative learning controller with double loop coupling are designed. In these two kinds of controllers, the secondary loop is essentially a typical D-type iterative learning control rate, mainly to suppress the vibration of the system and ensure the nonlinear characteristics of the controller. In the double-loop coupled boundary iterative learning controller, the main loop is essentially a boundary control rule, mainly through the feedback of the state of the system to suppress distributed interference and boundary interference. In the adaptive iterative learning controller with double loop coupling, the main loop is mainly composed of observer and system state feedback signal. By defining the compound energy function, the bounded property of the closed-loop system in each iterative period and the convergence on the iterative axis are proved in this paper. In order to show the performance of the closed-loop system and the effectiveness of the designed controller, MATLAB digital simulation and manipulator experiments are carried out in this paper. In the digital simulation, the open loop system under uncontrolled external force and the closed loop system under the designed controller are compared in this paper. In the manipulator experiment, the open-loop system under uncontrolled control, the closed-loop system under PD control and the closed-loop system under double-loop coupled iterative learning control are compared in this paper.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TB535

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