Description: 单输入单输出模型预测控制算法实现--一般实现，逆响应和线性规划-SISO model predictive control- a general approach, inverse response and linear programming Platform: |
Size: 4096 |
Author:Yao Chen |
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Description: Developing Models from Experimental Data using System Identification Toolbox-1. webinar_walk_through.m: contains all the linear and nonlinear estimation examples presented during the webinar.
2. Data files and Simulink models: process_data.mat, ExampleModel.mdl, Friction_Model.mdl. Any other data files used in the presentation already ship with the toolbox (ver 7.0).
Products used:
- You basically need only System Identification Toolbox (SITB) to try out most examples.
- To use Simulink blocks, you would, of course, need Simulink.
- Control System Toolbox is used at one place to show how estimated models can be converted into LTI objects (SS, TF etc)
- Optimization Toolbox will be used if available for grey box estimation. If not, SITB s built-in optimizers will be used automatically.
- Other products mentioned: Neural Network Toolbox, Model Predictive Control Toolbox and Robust Control Toolbox.
Platform: |
Size: 34816 |
Author:陈翼男 |
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Description: Model-based control strategies like model predictive
control (MPC) require models of process dynamics accurate
enough that the resulting controllers perform adequately in
practice. Often, these models are obtained by fitting convenient
model structures (e.g., linear finite impulse response (FIR) models,
linear pole-zero models, nonlinear Hammerstein or Wiener
models, etc.) to observed input–output data. Real measurement
data records frequently contain “outliers” or “anomalous data
points,” which can badly degrade the results of an otherwise
reasonable empirical model identification procedure. This paper
considers some real datasets containing outliers, examines the
influence of outliers on linear and nonlinear system identification,
and discusses the problems of outlier detection and data cleaning.
Although no single strategy is universally applicable, the Hampel
filter described here is often extremely effective in practice. Platform: |
Size: 119808 |
Author:JTNT |
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Description: 对线性时变模型和非线性模型的预测控制算法进行了仿真分析，设计了两种模型下的模型预测控制的实现(Simulation analysis of linear time-varying model and nonlinear model predictive control algorithm) Platform: |
Size: 7168 |
Author:白银三段 |
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Description: 为减少道路突发事故，提高车辆通行效率，需要研究车辆的紧急避障以实现自主驾驶。
基于车辆点质量模型，设计了非线性模型预测控制( MPC) 路径规划器; 基于车辆动力学模型，设计了线性时变MPC 轨迹跟踪器。(Emergency obstacle avoidance is one of the key points for autonomous driving system. A path planning controller based on non-linear model predictive control and a path tracking controller based on linear time-varying model predictive control are designed. In path planning controller, an obstacle-avoiding function is used to adjust the distance between the intelligent vehicle and obstacles by calculating the
value of obstacle-avoiding function.) Platform: |
Size: 1884160 |
Author:zshou |
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Search - linear model predictive control