This master’s thesis concerns development of embedded control systems. Development process for embedded control systems involves several steps, such as control design, rapid prototyping, fixed-point implementation and hardware-in-the-loop-simulations.
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Another step, which Volvo is not currently using within climate control is on-line tuning. One reason for not using this technique today is that the available tools for this task (ATI Vision, INCA from ETAS or CalDesk from dSPACE) do not handle parameter dependencies in a satisfactory way. With these constraints of today, it is not possible to use online tuning and controller development process is more laborious and time consuming.
The main task of this thesis is to solve the problem with parameter dependencies and to make online tuning possible.
Volvo technology (VTEC) is an innovation company that provides expert functions and develops new technology for “hard” as well as “soft” products within the transport and vehicle industry. Among other things VTEC is working with embedded control systems. For one of the embedded control systems particularly “Climate Control Module (CCM)”, VTEC is working with the whole chain. VTEC does this for Volvo Cars, Volvo Trucks, Volvo Construction Equipment, Renault Trucks and Land Rover.
The work process for embedded control system developmet is typically as follows:
It is an iterative process, but there is one problem for the last step, which limits the possibilities of working iteratively. Control design is typically made in MATLAB/Simulink and Fixed-Point implementation is typically made with a tool such as TargetLink. During these steps the parameters may be handled in an m-file. When going to the on-line tuning step however, the parameters are handled in a tool such as ATI Vision, INCA or CalDesk. Once you have taken this step the connection to the m-file is lost. Therefore the last step is somewhat of a one-way step. It is not completely impossible to go back to the earlier steps in the development chain, but the iterative process is not well supported by available on-line tuning tools of today.
The following m-script instructions are examples of parameter dependencies that will cause the mentioned problems:
Heating = [ -100, -20, 0, 20, 100 ];
BlowerHt = [ 12, 5, 4, 5, 10 ];
Blower_min = min[ BlowerHt];
Defrosting = [ 0, 20, 100 ];
BlowerDef = [ Blower_min, Blower_min, 10 ];
Using the above vectors in interpolation tables, one table with Heating as input vector and BlowerHt as output vector and another table with Defrosting as input vector and BlowerDef as output vector would cause problems during on-line tuning process.
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