Tutorial: Working with ASCMO-MOCA

This chapter will help you with an example to familiarize yourself with the basic functions of ETAS ASCMO-MOCA.

About this Tutorial

In this section you can find information about the structure of the tutorial and about the requirements on the measurement data that are used for the parameter optimization.

This section consists of the following sub-chapters:

Challenge in this Tutorial

An ECU often contains models for the calculation of signals, as the sensor-based data logging is either too difficult or too expensive. A common use case is, for example, the calculation of the engine torque. With ASCMO-MOCA you can set up and calibrate a function and optimize the function's parameters based on the measured sensor data. The goal of the optimizer is to minimize the root mean square error RMSE (Root Mean Squared Error) of the function's parameter. That means that the deviation between the function prediction and the measured sensor data will be minimized.

The structure of the torque related function, that will be modeled step by step during the tutorial, is displayed in Step 5: Build Up the Function.

Structure of the Tutorial

The subsequent tutorial is structured with the following working steps:

  • Step 1: Data Import

    In this first step, the measurement data will first of all be loaded and the channels will be associated with a function node.

  • Step 2: Data Analysis

    For clearing up and evaluating the measuring data, at any time, you have the possibility to visualize it after the import graphically for anytime.

  • Step 4: Models

    In this step, you are able to link an existing Simulink model with and prepare the mapping of the parameters, the inputs and outputs.

  • Step 5: Build Up the Function

    After reading the measuring data and check the plausibility, you can start to set up the function for the torque sensor that will be modeled during the tutorial.

  • Step 3: Parameters

    This step allows you to check and possibly adapt the parameters. Only the parameters will be visualized, which you have defined as reference after an optimization Step 6: Optimization.

  • Step 6: Optimization

    Before starting with the optimization you have to insert different settings, which influence the optimization. After you have inserted these settings, you can finally start the optimization.

  • Step 7: Export

    In this step you will export the created and optimized parameters. The parameters can be exported as DCM file (*.dcm) and the project can be saved for the runtime environment with limited functionality.

Requirements on Measurement Data

Basically, a simple rule needs to be considered for a successful parameter optimization in ASCMO-MOCA: The quality of the function's parameter optimization result always depends on the quality of the measurement data. Or in other words: If the parameters have been calibrated based on non space-filling or even wrong data, the function prediction is of little use.

Importing the measurement file in ASCMO-MOCA requires a file with the following properties:

  • Data format:
    • Microsoft Excel (*.xls / *.xlsx)
    • MDA Export (*.ascii)
    • Comma Separated Values (*.csv / *.txt)
    • Measurement Data Format (*.dat / *.mf4 / *.mdf / *.mdf3)
  • Outputs in columns
  • Names (and perhaps the units) have to be inserted in the first row (or in the first and second row).

Note  

The data used for parameterization do not necessarily have to be derived from a physical experiment (e.g. test bench). They can also be for example a result of a computer simulation.

Data for Modeling

The data used for the parameter optimization in this tutorial can be found in the Torque_Data.xlsx Excel sheet in the <installation>\Example directory.

<installation> is the installation directory. By default, <installation> = C:\Program Files\ETAS\ASCMO x.x.

The measurement data from this file meets the already mentioned requirements for a successful parameter optimization in ASCMO-MOCA:

  • The experimental design for logging the sensor data (e.g. at a test bench) corresponds to the DoE method, i. e. the measurements have been varied independently and are space-filling.
  • The measured sensor data from the measurement file does not include any absurd values (e.g. values ≤ 0 for torque).