Model Predictive Control 1 - Introduction to Model Predictive Control

Gives the human or philosophical thinking behind predictive control and explains why this is an intuitively obvious approach to control design.

Predictive control is a way of thinking not a specific algorithm. This video breaks down the thinking into the different aspects which underpin a well designed algorithm - continued in next video.

The choice of a model is a fundamental part of MPC. This video gives a brief overview of typical models that have been found to be effective and some of the thinking the user should deploy.

Any measure of performance must be unbiased, that is the use of this within control design and decision making should not lead to steady-state offsets. This video shows how a performance index can be constructed to make good engineering science and also be unbiased.

Gives a quick demonstration of the m-files available for producing prediction matrices. These cover CARIMA models, state-space models and step response models.

Introduces the concept of good and bad performance which is required to distinguish between the predictions you want and those you do not. Noted that in many cases the definition of 'optimal' performance is to some extent arbitrary and not systematic.

Predictive control is a way of thinking not a specific algorithm. This video continues the previous by breaking down the thinking into the different aspects which underpin a well designed algorithm.

This video illustrates how a naive selection of performance index based on the square of tracking errors or inputs can lead to poor performance.

In the early days and for SISO systems, it may be easier to model with transfer functions rather than state space models. This video gives an elementary and easy to code mechanism for forming compact n-step ahead predictions.

Follows on from the previous video by giving some numerical examples of prediction matrices for a complete prediction horizon, using transfer function models. Includes MATLAB demonstration.

Demonstrates how the use of disturbance estimate in conjunction with deviation variables relative to the expected steady-state can be used to give unbiased predictions when utilising a state space model (assumes observability).

Many commerical MPC algorithms deploy step response models as these are relativey easy to identify. This video shows how one can form a n-step ahead prediction using step response parameters - viewers will note that this requires a subtlety that might be unexpected.

The previous videos showed how to predict, but gave little attention to the accuracy of these predictions. It transpires that an effective MPC design requires the predictions to be unaffected by uncertainty, in the steady-state; this is called unbiased prediction. The method used is a disturbance estimate.

Develops concepts in previous video by showing how the use of a CARIMA model allows the predictions, in steady-state, to have no errors despite the presence of parameter and other uncertainties.

Introduces the procedure of prediction using mathematical models. Prediction is core to the efficacy of MPC and thus good comprehension of how this is done is essential.

Extends the previous video by introducing compact notation for prediction which enables the easier algebra needed for most MPC algorithms.