What is the Model-Based Design Cartographer and why do you need one? A guide, like the Fylgja, mapping out the changes to the domain of Model-Based Design. A painter illustrating how the established footpaths transform into superhighways, (1) a bard chronicling the frontier territories transformation into states.(2) I’m inventing the maps of Model-Based Design, pushing out the borders into where the dragons used to lay.(3)(4)
The Three Opportunities
The reward for dragon slaying is a hoard of gold;(5) the reward for solving engineering problems is the understanding that leads to better tools and processes.(6) Currently Model-Based Design faces three “dragons.” Today I peer out with one eye (7) looking down the tree of possibilities.
Dragon 1: Effective application of AI/DL/ML to controls problems (and more)
AI/DL/ML, call it what you will, this is the rough and tumble frontier town of Model-Based Design. AI/DL/ML and Model-Based Design have a lot to offer each other. AI/DL/ML can solve control problems that traditional methods are not able to resolve. Model-Based Design can provide the experimental infrastructure to develop these algorithms. What is missing is the confidence in the algorithms allowing for deployment in the field. How can you validate a black box?
AI/DL/ML systems are “Black Box” systems. Unlike traditional black box systems, with mathematical approaches for determining the limits of the system, (8) AI/DL/ML systems do not easily lend themselves to analysis. So what can we do?
This is where Model-Based Design, coupled with Design of Experiments comes into play. With a sufficiently complex plant model statistical analysis can be performed on the system to establish the boundaries of the behavior. Further if the system is pertubated with external noise you can establish a the boundaries of operation with even higher confidence.
Dragon two: Understanding Event Driven and Distributed Systems
On the other side of the world in another frontier town, we have event driven modeling; with event driven modeling the effect of an action upstream often has chaotic downstream impacts. (9) Modeling the initial driving actions allows for prediction of the final resolution.
Many modern electronic devices operate in an event driven environment. These “events” are often orders of magnitude more complex than traditional “a button was pushed.” (10) Coupled with the highly distributed nature of event driven environments, this results in complex architectures.
Again the ability to simulate (using models) come to the front of the stage.(11) Using the correct modeling language to define the event driven simulation, coupled with appropriate workflows to verify the algorithms is of paramount importance. Assuming that, one approach to the validation is similar to existing coverage analysis methodologies, where the highly temporal nature of the events is the key variable of analysis.
Dragon 3: Bounding Exponential Growth
The modern control algorithms have grown exponentially to the point where established Model-Based Design approaches have difficulty encompassing the full development cycle. The three primary challenges are increase of “system of systems,” the inability to simulate massive system of systems, and the management of information throughout the design process. (12)
The answer lies in the redefinition of the boundaries between Model-Based Systems Engineering (MBSE) and Model-Based Design. Using the meta-data rich nature of systems engineering with their high levels of abstraction and traceability while leveraging the rich simulation and validation methodologies of Model-Based Design, we have the ability to start to untangle the knot growth.
From Art to Engineering (by way of Science): Drawing a new map
There is Art, there is Science and there is Engineering. When a field is in the Art Stage things are accomplished by masterly people who have a feel for what needs to be done. There are some astonishing results but they are rarely reproducible.
We are in the middle of the Science Stage of the “Next generation of Model-Based Design.” This is the point where measurements are taken and the field is defined. Scientists are explorers, drawing the map.
The final stage is engineering, where the map is drawn and the roads (those paths that anyone can travel down) are paved.
I am your Model-Based Design Cartographer. My hope is that we are entering a “Renaissance” for Model-Based Design, a time when Artists were Scientists and Engineers are all in one
- In this analogy “footpaths” are the initial rough workflows that individual groups work out, the “superhighway” is the final industry established best practice workflows.
- The territories cover a wide range of terrain, what we are doing is “taming the wild west.”
- One could say that the change from the existing state of Model-Based Design to the next state is “how the Wrum turns”
- In Das Ring des Nebelungen, Fanfnir turns himself into a dragon to protect the hoard of gold. In much the same way some of our “problems” arise from earlier solutions.
- Most dragons moved away from the Gold Standard in the 20th Century. Unfortunately paper currency and fire breathing lizards do not mix. This is the real cause for the Great Depression.
- With engineering, processes are the “real gold.” A good tool is just that, good; how to best use the tool is priceless.
- Wotan gave up one eye so that he could see more clearly; I hope today this blog can serve this purpose.
- Not all systems can be analyzed in this method, however even in the worst cases it is possible to create reduced order models which can provide data on the limits of the system.
- Event driven systems do not lend themselves to traditional mathematical analysis; hence the “chaotic” nature of their responses. However their boundaries and the effects of actions can be discovered. And truth be told, when you first read Hamlet did you think that everyone would be dead at the end? Only Horatio knows the truth, and an unseen actor (Fortinbras) becomes King.
- The event may still be triggered by a button push but the data associated with that push (the button ecosystem) is much richer.
- All the worlds a stage and all the controllers and networks merely players
They have their exits (conditions) and their entrance (initialization)
And one ECU in it’s time sends many a signal
- I selected the word “information” here as the types of “data” that exist for system of systems design that are so much richer than the single or multi controller examples and often encompass things beyond the traditional controls engineering definitions.