What is Model Predictive Control

MPC challenges

History of neural networks in MPC

How to use autoencoders to get non-linear state space models?

Piecewise linear models

Semi-explicit approach

Reducing the number of variables: control horizon

Closed-loop cost comparison

Closed-loop Performance

Conclusion

Intro

What is MPC?

Why use Model Predictive Control (MPC)?

Advantages of MPC

Handling Constraints

Applications of Model Predictive Control

Summary

What is GPC?

Theoretical weaknesses of GPC/DMC

Course objectives

Arrow Notation

Forward arrow notation

Predictor structure

State-space model and deviation variables

Multivariable system

Multivariate performance index

Performance index notation

Intro

Preprocessor Explanation and Comparison

ADAIN

Reference Only and ADAIN+ATTN

Preprocessor Review and Pros and Cons

Settings for ControlNet Reference

Style Fidelity

Weight

Troubleshooting Blurriness

Workflows, the law of equivalent exchange

Workflow, Style Change

Workflow, Image Variation

Physical constraints

Example: Tank Level Control

Closed-loop control

Integral windup

Example

Inverted Pendulum

Predictive Controllers with Constraints

Saturation Control Example

Enforcing Saturation

Integrating constraints

Human-based concepts

Desired Settling Time

First-order model

Anchoring example

Desired dynamics

Examples

PFC effectiveness

Conclusion