MPI Objects Overview 2

本文概述了运动控制器的关键组件及其功能,包括电机驱动通信、位置捕获、比较触发、滤波算法等,并介绍了不同类型的运动轨迹计算及系统配置工具。

MPI Objects Overview

Motor
Drive Communication
I/O (Home, Motor, Limit switches)
Encoder

Capture

 
  • high speed position capture (triggered by digital input)
  • useful for homing

Probe

  • Latches and stores position or node clock data
  • Triggers up to16 times within one controller sample

Compare

 
  • high speed position compare (triggers a digital output)
  • useful for triggering some other instrument

Filter
Control Algorithm (PID, PIV, custom)
Ouput filtering (postfilters)

 
  • biquad (6 filters)
  • IIR (32 coefficients)

Axis
Represents a physical axis
Trajectory calculations
Position information

Motion (Supervisor)
Coordinates motion between axes
Responsible for clearing errors

Recorder
Record any 32 locations of controller memory at 10kHz continuously in real time
Large buffer on controller
Useful for 

 
  • Tuning (Motion Scope)
  • Data correlation (ex: ADC inputs vs. axis position)
  • System well being (Is a part about to fail?)

EventMgr (Event Manager)
Responsible for distributing gathering and distributing events
NOTE: mpiEventMrgFlush() flushes events from associated notify objects, not from the event pool

Notify 
Report events

Platform
Useful for translating memory addresses between the host PC and the XMP

Motion Types
Trapezoidal
S-Curve

 
  • Uses parameter "jerk percent"

S-Curve Jerk

 
  • Specify acceleration and deceleration jerks

Velocity
Velocity Jerk
PT

 
  • Linear interpolation (segments have constant velocity)
  • Useful for streaming points

PVT

 
  • Useful for specifying custom paths

Spline
Bessel
Path

Motion Attributes
Delay

 
  • Wait for a period of time before starting a move

Hold

 
  • Wait for a status or I/O change before starting a move

SynqStart

 
  • Synchronize the start of motion for all axes

SynqEnd

 
  • Synchronize the end of motion for all axes

Special Case: Neither SynqStart or SynqEnd are used

 
  • Only one trajectory is used
  • Useful for ensuring maximum accelerations and velocities are not exceeded on x-y systems

Server
A computer without XMP (client) can communicate efficiently with a computer with an XMP (server) over an Ethernet connection. The client can then run any application writtten with the MPI over the Ethernet connection. This is useful for using Windows based Tools if the server is running at RTOS.

 
  • Motion Console
  • Motion Scope
  • System Analysis Tools

Useful for changing configurations if the server is in a clean room.

 

Trajectory

 

Beyond the MPI

Firmware
The contoller firmware is written in C.

 
  • Easy to change/customize
  • Can be compiled and downloaded quickly (under 5 minutes)
提供了基于BP(Back Propagation)神经网络结合PID(比例-积分-微分)控制策略的Simulink仿真模型。该模型旨在实现对杨艺所著论文《基于S函数的BP神经网络PID控制器及Simulink仿真》中的理论进行实践验证。在Matlab 2016b环境下开发,经过测试,确保能够正常运行,适合学习和研究神经网络在控制系统中的应用。 特点 集成BP神经网络:模型中集成了BP神经网络用于提升PID控制器的性能,使之能更好地适应复杂控制环境。 PID控制优化:利用神经网络的自学习能力,对传统的PID控制算法进行了智能调整,提高控制精度和稳定性。 S函数应用:展示了如何在Simulink中通过S函数嵌入MATLAB代码,实现BP神经网络的定制化逻辑。 兼容性说明:虽然开发于Matlab 2016b,但理论上兼容后续版本,可能会需要调整少量配置以适配不同版本的Matlab。 使用指南 环境要求:确保你的电脑上安装有Matlab 2016b或更高版本。 模型加载: 下载本仓库到本地。 在Matlab中打开.slx文件。 运行仿真: 调整模型参数前,请先熟悉各模块功能和输入输出设置。 运行整个模型,观察控制效果。 参数调整: 用户可以自由调节神经网络的层数、节点数以及PID控制器的参数,探索不同的控制性能。 学习和修改: 通过阅读模型中的注释和查阅相关文献,加深对BP神经网络与PID控制结合的理解。 如需修改S函数内的MATLAB代码,建议有一定的MATLAB编程基础。
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