Objects Relationships

对象关联方法
本文介绍了三种在运动应用中关联对象的方法:使用对象列表、位图和对象编号数组。这些方法适用于不同场景,如需要保持顺序的对象列表,不关心顺序且位于同一控制器上的对象位图,以及既需保持顺序又位于同一控制器上的对象编号数组。

Objects Relationships

Introduction | Object List | Bitmap | Array of Object Numbers
Using Lists, Bitmaps, & Arrays | Application-MPI-Controller Interface

Introduction

To create a motion application, you create objects, link them in relationships (associate them), and provide the desired parameters for motion.

There are three ways that you can associate one object with another object:

 
  • Using an object List
  • Using bitmap
  • Using an array of object numbers

Objects using bitmaps or an array of numbers must all be present physically on the same controller, while objects on an object list can come from different controllers (such as objects on the EventMgr control list).

Three Ways to Associate Objects with Objects

Object.When to Use

List

an ordered list

Use a List 
when the order of the objects to be associated is important, OR when some of the objects reside on different controllers.

Bitmap

an un-ordered list

Use a bitmap 
when the order of the objects is not important, AND the objects must all reside on the same controller (same Control object).

Array of object numbers

an ordered list

Use an array of object numbers 
when the order of the objects is important, AND the objects must all reside on the same controller (same Control object).

Object List

Use an object list to associate objects when ordering is required, such as the Axis list maintained by a Motion object. For example, when using a Motion object, a list of Axes 0, 1, 2 is not the same as a list of Axes 0, 2, 1. Such a distinction is not available when using an object map. Refer to the List methods for Motion, Notify, Sequence and EventMgr objects.

This object:Can have object lists of:
MotionAxes
NotifyEvents
EventMgrControls
Notify objects
SequenceCommands

Bitmap

An object map is a bitmap, where each numbered bit represents the presence or absence of the correspondingly numbered object. You can order the objects in the bitmap from numeric low-to-high or high-to-low, but there is no other meaningful capability for ordering objects with an object map.

Another consideration with object maps is that all objects specified in the map and the object (that those objects are being associated with) must be resident on the same controller (Control object).

Note: Although the MPI allows an Axis to be associated with a Motion without regard to the controller (Control), the XMP implementation of the MPI does not allow this.

Object maps tend to be used to associate low-level objects that:

 
  • must all reside on the same controller (Control)
  • and the order in which the objects are ordered does not matter

Note that the objects specified in the bitmap may be associated with another object, without those objects having to be created and deleted. A goal of the MPI has been to minimize the need to create and delete objects, especially objects that are just used for configuration. For example, an MPI application that uses the default configuration doesn't have to create Filter or Motor objects, yet the application can still configure an Axis, by calling mpiAxisFilterMapSet(...), where the Filters are specified by the bitmap.

Object Maps for Objects

This object:Has associated object maps of:
Filter

Axes
Motors

AxisFilters
Motors
MotorFilters
Axes
Adcs

Array of Object Numbers

The MPI provides a third means of associating objects, by using object numbers. Object numbers should be used when ordering is required, and all the resources arelocated on the same controller.

Using List, Bitmaps, & Arrays

Application-MPI-Controller Interface

基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值