Tutorial Objectives

In this tutorial, you will learn how to use the MotionSolve-Simulink co-simulation interface, driving the model from Simulink via an S-Function. MotionSolve provides two ways to drive co-simulation from within Simulink – using the Shared Memory (SMP) or Inter Process Communication (IPC) approach.

SMP co-simulation uses shared memory while exchanging data between the two solvers. This approach requires that both MotionSolve and MATLAB be installed on the same machine. Additionally, the two software must be both 64-bit. Please see MV-7002 for additional information.

For IPC co-simulation, the two solvers are run on two separate processes with data being exchanged through sockets. Since the two solvers run on different processes, this method allows you to run a co-simulation between different build types (32-bit/64-bit) for MotionSolve and MATLAB. As an example, a 32-bit MATLAB can be used to co-simulate with a 64-bit installation of MotionSolve on the same or different machine. The rest of this tutorial describes the co-simulation using the IPC approach.

Software and Hardware Requirements

Software requirements:

Hardware requirements:

Simulation Environment

In this tutorial, it is assumed that MATLAB is installed on Machine “A” and MotionSolve is installed on Machine “B”. The following scenarios are feasible:

* Altair HyperWorks no longer supports 32-bit version of MotionSolve starting version 13.0 and beyond. To use the 32-bit version of MotionSolve, you will need to install the 12.0.x version of HyperWorks.

This tutorial describes the process to perform a co-simulation between a 64-bit installation of Simulink on Machine “A” and a 64-bit installation of MotionSolve on Machine “B” i.e., the scenario that is in blue text in the table above. This is illustrated better in Figure 1 below.

Figure 1: Co-Simulation environment used for this tutorial

Inverted pendulum controller

Consider an inverted pendulum, mounted on a cart. The pendulum is constrained at its base to the cart by a revolute joint. The cart is free to translate along the X direction only. The pendulum is given an initial rotational velocity causing it to rotate about the base.

Both the pendulum and the cart are modeled as rigid bodies. The controller, modeled in Simulink, provides a control force to the cart to stabilize the inverted pendulum and prevent it from falling. This control force is applied to the cart via a translational force. The model setup is illustrated in Figure 2 below.

Figure 2: Inverted pendulum model setup

The pre-designed controller generates a control force that keeps the pendulum upright. The controller uses the pendulum’s orientation and angular velocity along with the cart’s displacement and velocity as inputs to calculate the direction and magnitude of the force needed to keep the pendulum upright. A block diagram of the control system is shown in Figure 3.

Figure 3: Block diagram representation of the controller

In the image above:

A disturbance is added to the computed control force to assess the system response. The controller force acts on the cart body to control the displacement and velocity profiles of the cart mass.

In this exercise, you will do the following:

Before you begin, copy all the files in the <altair>\tutorials\mv_hv_hg\mbd_modeling\motionsolve\cosimulation folder to your working directory (referenced as <Working Directory> in the tutorial). Here, <altair> is the full path to the HyperWorks installation.

Step 1: Run the baseline MotionSolve model.

In this step, use a single body vector force (Force_Vector_OneBody) to model the control force in MotionSolve.  The force on the cart is calculated as:

, where

is the disturbance force,

is the control force,

are gains applied to each of the error signals

is the error (difference between reference and actual values) on the input signals.

The angular displacement and velocity of the pendulum are obtained by using the AY() and WY() expressions respectively. The translational displacement and velocity of the cart are obtained similarly, by using the DX() and VX() expressions.

Figure 4: The MotionSolve model of the inverted pendulum

The MotionSolve model consists of the following components:

The results that we get from Step 2 will be used as the baseline to compare the results that we get from co-simulation.

Step 2: Use modified MotionSolve Model to define the plant in the control scheme.

A MotionSolve model needs a mechanism to specify the input and output connections to the Simulink model.  The MotionSolve model (XML) used above is modified to include the Control_PlantInput and Control_PlantOutput model elements and provide these connections.   In this tutorial, this has already been done for you, and you can see this by opening the model InvertedPendulum_Cosimulation.mdl from your <Working_Directory>.

This model contains two additional modeling components:

Figure 5: The definition of the input channel to MotionSolve

The inputs specified using the Control_PlantInput and Control_PlantOutput elements can be accessed using the PINVAL() and POUVAL() functions, respectively. Since the Control_PlantInput and Control_PlantOutput list the ids of solver variables, these inputs and output variables may also be accessed using the VARVAL() function. For more details, please refer to the MotionSolve User’s Guide on-line help.

In this model, we have the following connections:

Plant Output: The pendulum’s angular displacement and angular velocity; the cart’s translational displacement and velocity.

Step 3: Setting up environment variables to run MotionSolve from MATLAB\Simulink.

A few environment variables are needed for successfully running a co-simulation using MATLAB.  These can be set using one of the following methods:

An example of the usage of these commands is listed below

Set the following environment variables

where <altair> is the full path to the HyperWorks installation on Machine “A” i.e. on the machine where Simulink in installed. For example, on Windows, this would typically be C:\Program Files\Altair\14.0.

The “MS_SERVER_IP_ADDRESS” environment variable needs to be set to the IP address of Machine “B” i.e. the machine where only MotionSolve is installed. The IP address should be the full address that can be obtained by using the “ipconfig \all” command on Windows or “ip addr show” command in Linux.

Note that other optional environment variables may be set for your model.  See MotionSolve Environment Variables for more information on these environment variables.

Step 4: Perform the co-simulation.

The core feature in Simulink that creates the co-simulation is an S-Function (System Function) block in Simulink.  This block requires an S-Function library (a dynamically loaded library) to define its behavior.  MotionSolve provides this library, but the S-Function needs to be able to find it.  To help MATLAB/Simulink find the S-Function, you need to add the location of the S-Function to the list of paths that MATLAB/Simulink uses in order to search for libraries.

The S-Function libraries for co-simulation with MotionSolve are called either:

mscosim – for Shared Memory (SMP) communication. Please see the tutorial MV-7002 for details on how to use SMP communication for co-simulation.

mscosimipc – for Inter Process Communication (IPC) using TCP/IP sockets for communication.

Changing the name of this library in the S-Function block in Simulink changes the communication behavior of the co-simulation.

These files are installed under <altair>\hwsolvers\motionsolve\bin\<platform>.

The location of these files needs to be added to the search path of MATLAB for the S-Function to use mscosimipc.

This can be done in one of the following ways:

Figure 7: Add path through the MATLAB GUI

(for example, <altair>\hwsolvers\motionsolve\bin\win64).

At the MATLAB command line, type:

addpath(‘<altair>\hwsolvers\motionsolve\bin\<platform>’)

to add the directory where mscosimipc library resides into the MATLAB search path.  It remains valid until you exit MATLAB.  You can also create a .m script to make this process more easily repeatable.

For example, you can set the MATLAB Path and the necessary environment variables using MATLAB commands in a MATLAB (.m) script:

addpath('<altair>\hwsolvers\motionsolve\bin\win64')

setenv('NUSOL_DLL_DIR','<altair>\hwsolvers\motionsolve\bin\win64')

setenv('RADFLEX_PATH',['<altair>\hwsolvers\common\bin\win64')

setenv('PATH',['<altair >\hwsolvers\common\bin\win64;' getenv('PATH')])

setenv(‘MS_SERVER_IP_ADDRRESS’,’192.168.1.123’)

For a Linux machine, additionally:

setenv(‘LD_LIBRARY_PATH’, '<altair>\hwsolvers\motionsolve\bin\linux64:<altair>\hwsolvers\common\bin\linux64\')

Step 5: View the controller modeled in Simulink

The Open file… dialog is displayed.

You will see the control system that will be used in the co-simulation.

Figure 8: The control system in MATLAB

The S-function (system-function) is one of the blocks provided by Simulink and represents the MotionSolve model.  It can be found in the Simulink User-Defined Functions block library.  An S-Function allows you to model any general system of equations with inputs, outputs, states, and so on, and is somewhat analogous to a Control_StateEqn in MotionSolve.  See the MATLAB/Simulink documentation for more details.

' InvertedPendulum_Cosimulation.xml', ' InvertedPendulum_Cosimulation.mrf', ''

The three parameters are the following:

Step 6: Perform the co-simulation.

On Machine “A”, click Simulation > Start to start the co-simulation.  Simulink uses ODE45 to solve the Simulink model.  From this, the co-simulation should begin and Simulink will wait to connect to the MotionSolve model.        

After starting the simulation in Simulink, MotionSolve will connect with the Simulink model and the subsequent co-simulation will result in an output .mrf file for post-processing.  Set the Scopes in the Simulink model to display the results.  Also, check the .log file to make sure no errors or warnings were issued by MotionSolve.

Figure 9: Running the co-simulation from Simulink

Step 7: Compare the MotionSolve-only results to the co-simulation results.

Figure 10: Create two plot windows

Figure 11: Plot the cart’s displacement

Figure 12: Plot the control force on the cart

Select Marker Force for Y-Type, then REQ/70000002 Output control force- (on Slider cart) for Y Request, and FX for Y Component to overlay the plot in the right window.

You will notice that both the signals match as shown below.

Figure 13: Comparison of the cart displacement and cart control force between the two models

The blue curves represent results from the MotionSolve-only model and the red curves represent results from the co-simulation.