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Overview
Overview
Predictive control is a very popular technique in industry and thus having resources on this within the control101 toolbox makes sense. The focus of these resources will be to reinforce a good understanding of the underlying concepts by enabling extensive visualisation. For detailed theory please see chapter 8 and the numerous text books on the topic.
Those who wish to implement MPC in anger should note that there are numerous MATLAB toolboxes available for this on file exchange.
These items are in preparation so have not yet been published.
The contents are summarised as follows. More details are lower down.
An introduction to prediction in decision making.
Choosing tuning parameters for a finite horizon algorithm
Challenging scenarios and what might go wrong with finite horizon algorithms?
Handling uncertainty and noise: the T-filter
Constraint handling and why MPC is so useful
Feedforward and target information
As normal in the toolbox, all the interactive laboratories are supported by manuals written as livescripts which give more detailed background information. As the focus is on concepts, for now the available resources focus on linear SISO systems as more complicated scenarios make the core concepts harder to illustrate.
Prediction in decision making
Prediction in decision making
This is a simple interface (predictionconcepts_control101.mlapp) to understand both how prediction underpins control decision making modelling and also how, with poor choices of horizons, these decisions may be poorly thought through. Users can select their desired values for the future inputs and view the impact on the predictions. There is also an option to view the associated GPC/DMC predictions. The implement button will then use the first of these and move to the next sample.
Boxes at the top summarise the sum of errors squared and input changes squared to help with any quantitative assessment.
Choosing the tuning parameters
Choosing the tuning parameters
This app (mpctuning_control101.mlapp) is a simple interface to understand and investigate how the core tuning parameters within an MPC law affect the decision making and performance. The tuning paramters dictate the performance index used to determine the 'optimal' current control and thus their choice is important. However, it can be seen that different choices lead to hugely variable behaviour and thus understanding what constitute good and bad choices is essential.
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