Home
MotionSolve User Guide
MotionSolve is a system level, multibody solver that is based on the principles of mechanics.
MotionSolve Optimization Guide
PID Controller Example
In this example, we are designing a PID controller whose job is to minimize the effects of a disturbance force on the position, velocity, and acceleration of a block.
The Parametric Model Class - PID Model
The simulate Method

Welcome
What's New
View new features for MotionSolve 2024.1.
Overview
MotionSolve^® is an integrated solution to analyze, evaluate, and optimize the performance of multibody systems.
Tutorials
Discover MotionSolve functionality with interactive tutorials.
MotionSolve User Guide
MotionSolve is a system level, multibody solver that is based on the principles of mechanics.
- Introduction to MotionSolve
- Platform Support, Recommended Hardware, and Licensing
- Run MotionSolve
- Model Systems
- Model Simulations
- Visualize Results - Animation and Request Plots
- Couple MotionSolve with Other Software
- Customization Capabilities
- Analysis Tips
- NLFE Bodies Introduction
- User Subroutines in MotionSolve
- msolve API
- MotionSolve Verification Manual
- MotionSolve Optimization Guide
  - Introduction
    The MotionSolve Optimization Guide explains the design sensitivity and optimization capabilities in MotionSolve.
  - Optimization Capabilities in MotionSolve
    Optimization capabilities in MotionSolve include equations of motion for a multibody system, design variables and limits, and analysis support.
  - Application Areas
    Optimization is a vast area that has a many applications in diverse areas such as engineering, economics, business and medicine.
  - Optimization Input Data
    The input data for an optimization is organized in a python parametric model class with methods that operate on the class.
  - Optimization Output Data
    Every optimization run generates a variety of files.
  - Advanced Topics
    Advanced topics include a scaling optimization problem and tips on how to debug optimization runs.
  - SLA Suspension Example
    This example, referred to as MV3010 in this section, describes the optimization of an SLA suspension and is also available in MotionSolve tutorial topics/solvers/ms/optimization_using_mv_hs_intro_r.html.
  - PID Controller Example
    In this example, we are designing a PID controller whose job is to minimize the effects of a disturbance force on the position, velocity, and acceleration of a block.
    - The Parametric Model Class - PID Model
    - Results
  - Glossary
- MotionSolve Environment Variables
- References
MotionSolve Reference Guide
This manual provides a detailed list and usage information regarding command statements, model statements, functions and the Subroutine Interface available in MotionSolve.

The simulate Method

The simulate method for the PID model is shown below. Notice that the force is deactivated after the first 10 seconds.

def simulate(self):
    """
    Run a trial simulation
    """
    self.run = self.model.simulate(end=10,dtout=0.01,returnResults=True,output='on')
    self.model.force.active = False
    self.run = self.model.simulate(end=100,dtout=0.01,returnResults=True,output='on')