Special Feature
This paper is the first in twopart series on the use of Opensource Software in Mechatronics and Robotics Engineering. Part two will be available in Vol 12 Issue 4 slated currently slated to go live midJanuary 2021.
– The ASEE COED, Editorial Team
Use of Opensource Software in Mechatronics and Robotics Engineering Education – Part I: Model Simulation and Analysis
Abstract
Opensource Software (OSS) provide attractive solutions for complementing Mechatronics and Robotics Engineering (MRE) education due to their numerous advantages such as free access, customizability and wide community support, increased adoption and utilization in industry, etc. To provide a deeper insight on the current status, limitations, and potentials of the OSS, a summary of the results of an online survey, conducted among various community stakeholders, is included. Furthermore, the two parts of this contribution are intended to provide an exposure to the OSS which have the potential to be used in MRE education. To this end, two specific problems, namely, model simulation and analysis of a DC motor (Part I) and controller implementation for a 2DOF robot manipulator (Part II), are solved using Python, Java, Modelica, GNU Octave, and Gazebo. The systems chosen for this work are some of the mostcommonly encountered systems and the considered problems, i.e. model simulation, analysis, and control implementation, are fundamental problems in the context of MRE. Therefore, this work can help MRE instructors familiar with these OSS to easily integrate them into their respective courses. On the other hand, the students can also greatly benefit from this work as it provides an entry point into applying different OSS in MRErelated courses and projects. Exposure to various ways in which an MRE problem is translated into a software solution enables the students to generalize their problemsolving process across different computational tools. Students equipped with such a skillset would potentially have a flexible mindset and could make wellbalanced and informed judgements when devising a solution to a realworld engineering problem. Full code scripts for each of the OSS introduced in this work, along with Matlab which is intended as a point of comparison, are included on the GitHub repository of the paper to provide free access to the community and to help with widespread adoption of the OSS in MRE higher education.
Keywords
Open Source Software, Robotics Engineering Education, Model Simulation, Programming Languages
Introduction
The field of Mechatronics and Robotics Engineering (MRE) has experienced tremendous growth in the past few decades, mainly due to the advancements in integrated circuits and electronics, computers, control systems, and connectivity and networking. Applications such as consumer electronics, home and building automation, medicine and healthcare, manufacturing and industrial robotics, heavy machinery, autonomous transportation, and aerospace applications, in addition to democratization of access through Maker Movement have further expedited the advancements in the field of MRE.
Considering the prevalence and potentials of MRE, it is of utmost importance to educate the future engineers by providing an interdisciplinary knowledge of mechanical, electrical, computer, software, and systems engineering so that they can undertake and oversee multidisciplinary design and development efforts in this field and to define the future of work at the humantechnology frontier. In addition to technical breadth and depth, the MRE educational institutions should also provide the students with a rich handson skillset. To this end, in the past decades, numerous software and hardware packages have been developed to further engage the students through laboratory and projectbased learning paradigms. Such experiences could give students a deeper understanding of the core MRE concepts as well as an opportunity to practice the application of those concepts – linking knowledge and realworld skills. Students who demonstrate this understanding can confidently study realworld global challenges, devise multiple solutions to these problems, evaluate the feasibility of the various solutions based on the qualitative and quantitative criteria and constraints, and implement the optimal solution while considering the broader societal impacts.
Although commercial products such as Matlab and Simulink, which are targeted towards improving MRE education, have been widely adopted in academia, their acquisition and licensing fees can be a big hurdle for academic institutions which lack the necessary financial infrastructure. Furthermore, the students typically lose access to these resources once they graduate and leave the educational environments. Finally, there is an increasing trend in industry towards the development, distribution, and adoption of opensource platforms as they can reduce the total cost of ownership, reduce dependence on vendors, and facilitate higher flexibility and customizability.
The opensource community has been growing steadily since its first introduction in the early 1980s. The “opensource way”, which was initially used in software development, distribution and maintenance, was motivated by the need for better collaboration amongst developers, improved accessibility and reliability of software through a network of contributors. Opensource software (OSS) is defined as software released under a license which grants users the right to study, use, modify, and distribute the source code to anyone and for any purpose 1. Linux operating system and Apache Web server are some of the early developments of the community. Currently, numerous highquality opensource alternatives to proprietary software are available in various applications, which have gained widespread adoption.
In higher education, the first exposure to the OSS has traditionally been in introductory programming courses. Different programming languages such as C, C++, Java, etc. have been used to introduce the students to the fundamentals of programming and to develop their computational thinking mindset. In recent years, scripting languages such as Python have attracted more attention in the introductory programming courses, specifically due to their increased popularity, simplicity, and flexibility 2, 3. Different types of the OSS have also been used in higherlevel courses in Computer Science4 and Software Engineering5 programs. A detailed overview of the OSS implementation in Software Engineering during a 3year project along with students’ perception and feedback are presented in 6. The accessibility and customizability of the OSS have also contributed significantly to online and distance education 7, 8. An empirical study was conducted in9 to evaluate the common perceptions about the OSS and their applications and to provide a guideline for practitioners who wish to implement or switch to OSS.
The applications of the OSS in MRE education have mainly revolved around the use of the family of C languages, and recently Python, in embedded system programming and hardware interface. Reduced costs and advancements in opensource hardware such as Arduino, Raspberry Pi, BeagleBone, etc. have further increased the use and popularity of these software. The opensource hardware platforms have also enabled the community to develop lowcost laboratory experiments in order to complement the heavy theoretical content of MRE courses such as Embedded Systems 10, Controls 11, 12, 13, and Robotics 14, 15, 16, 17. A number of simulation platforms have also been developed to teach motion dynamics18 and industrial robotics 19. Finally, Journal of Open Source Software is an extensive repository that contains articles on the development of specialized OSS tools. Some of such tools related to MRE include: ros_control: A generic and simple control framework for ROS 20, CoreRobotics: An objectoriented C++ library with crosslanguage wrappers for crossplatform robot control 21, DART: Dynamic Animation and Robotics Toolkit 22, DmpBbo: A versatile Python/C++ library for Function Approximation, Dynamical Movement Primitives, and BlackBox Optimization 23, Phobos: A tool for creating complex robot models 24, Manif: A micro Lie theory library for state estimation in robotics applications 25, SLAM Toolbox: SLAM for the dynamic world 26, and Pybotics: Python Toolbox for Robotics 27.
Utilizing the OSS in MRE education is a feasible option for a wider range of institutions. Furthermore, it can familiarize the students with the development details of realworld systems and therefore, provide a deeper learning experience, which can be very attractive for future employers who are also wishing to utilize OSS. There are, however, a few challenges that need to be resolved before widespread adoption of the OSS in MRE education. These challenges include lack of familiarity among MRE educators with the utilization and application of OSS platforms; lack of tutorials and documentation tailored specifically for the design, analysis, and realtime implementation of MRE systems; the skillset to navigate through vast online resources about OSS for debugging and troubleshooting.
To overcome some of the aforementioned challenges, this work, presented in two parts, aims to increase the MRE community awareness and familiarity with the applications of the OSS. This article, i.e. Part I, addresses model simulation and analysis of a DC motor using some of the commonlyused OSS. DC motors, which are one of the most versatile systems in any MRE system, are governed by a set of linear ordinary differential equations. Familiarity with the simulation and analysis of DC motors using the OSS can pave the way towards their utilization in a wide range of MRE systems. This paper is organized as follows: Section 2 provides background information about the OSS with an extensive online support and the potential to be used in MRE education, namely, Python, Modelica, Java, GNU Octave, and Gazebo. Section 3 summarizes the results of a recent survey conducted among various community stakeholders to gauge the current status, potentials, and limitations of the OSS. Section 4 introduces the dynamics and the parameters of the DC motor used in simulations. In Section5, Python, Modelica, Java, and GNU Octave are used to simulate the dynamics of the introduced DC motor. Although the DC motor simulations can be accomplished in Gazebo, this software package is only considered in Part II of this work for the closedloop control of a robot manipulator. Code snippets from each software are provided in Section 5 to show the structure of the programs used for the simulations along with the other capabilities of the OSS and resources to learn more about them. This work is not an introduction to programming, neither is it a work on the modeling and control of dynamic systems. It, however, can provide a detailed and thorough exposure to the capabilities and application of some of the common OSS in the context of MRE systems. To the authors’ best knowledge, this work is a unique contribution aiming to ease the adoption and application of various OSS in MRE education.
Overview of Different Opensource Software
In this section, the OSS with the high potential for use in MRE education and with wide community support are introduced. These software include general programming languages such as Python and Java and more specialized platforms such as Modelica, Gazebo, and GNU Octave. All the opensource software in this work, except for Java, are technically considered scripting languages, as opposed to programming languages, and interpreted languages, as opposed to compiled languages. For brevity and clarity of presentation, the generic term software (and the abbreviation OSS) is used throughout the article to refer to these languages.
Python
Python is a generalpurpose and interpreted programming language, first developed by Guido van Rossum and released in 1991. In order to make it freely accessible to everyone, it is developed under an opensource license. Python Software Foundation, which is responsible for administering Python’s license, was formed “to promote, protect, and advance the Python programming language, and to support and facilitate the growth of a diverse and international community of Python programmers” 28. Due to its highlevel and opensource nature, large community of developers in Python, an abundance of packages developed for Python 29, and easy integration with other programming languages, Python has gained popularity in various applications and disciplines and has turned into an industry standard. Consequently, many valuable resources have been developed for getting started with Python. The website 28 provides a rich repository of documentation about Python installation, learning materials, news and events. In addition to many books written about Python 30, 31, several MOOCs (Massive Open Online Courses) also exist to provide familiarity with the fundamentals, advanced topics, and applications of Python 32, 33.
In the past decade, the Python community has developed numerous packages for mathematics, science, and engineering applications 34, 35. The following is a list of some of these libraries

SciPy: A comprehensive library with submodules for various mathematical and scientific operations such as Fast Fourier Transform, interpolation, numerical integration, linear algebra, file input/output, optimization and curvefitting, statistics, and signal processing.

NumPy: Offers computational capabilities similar to Matlab which also overlaps with some of SciPy’s functionalities. It is best suited for fast array operations. Matlab users who wish to learn more about NumPy can refer to 36 for a detailed comparison between Matlab and NumPy.

Matplotlib: Enables highquality 2D plotting. It can be used to generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc.

SymPy: Used for symbolic programming. It is incorporated into a wide range of other libraries such as symbolic statistical modeling, multibody dynamics, linear circuit analysis, etc.

IPython: Provides an interactive shell similar to Matlab’s workspace which is ideal for troubleshooting, interactive data visualization, use of GUI toolkits, and parallel computing.

pandas and Scikitlearn: Offer an extensive suite of tools for predictive data analytics, modeling, and machine learning.

Python Control Systems Library: Provides functions and classes to systematically analyze and design feedback control systems along with a Matlab compatibility module for users more familiar with Matlab 37.
As mentioned earlier, Python has received an increased attention in the past few years, mainly due to the fact that while it is a generalpurpose, highlevel, and interactive programming language, it is also equipped with powerful features and addon libraries which place it at the same high level as specialized software such as Matlab. To catch up to its fast pace of growth and popularity and to familiarize the future generation, Python is increasingly being introduced in higher education curricula. In the past decade, majority of top CS departments in United States have switched to Python in their introductory computer programming courses 38. According to a comparative study in 39, among Python, C, and Matlab as the programming language of choice for engineering education, Matlab is determined to be more suitable compared to C, however, Python is selected as the best option due to its clarity and functionality. A similar conclusion about Python being the best choice for engineering education compared to Matlab and C is achieved in 2. Authors in40 discuss the motivation and the process of a transition from C to Python in the first computer programming course for Mechanical Engineering students and report a reduced failure rate among the students, among other observations. In the context of MRE education, Python has mainly been used as the programming language for opensource hardware such as Raspberry Pi 41, 42, which is typically used for teaching control engineering. Python has also been used in43 to develop internet tools to teach the fundamentals of control.
Java
While Java is not the first programming language environment that comes to mind in an MRE context, it has many properties that make it a very useful tool. The first of these is the language itself. Java is fully objectoriented, has a relatively clean syntax, executes quickly, and operates with no changes (usually) across different computers and operating systems. It can be applied to mathematically based problems (for example, dynamic simulation) as well as used for direct control of physical systems.
Java compiles to an abstract machine code that executes via a Java Virtual Machine (JVM). Thus, a compiled Java program can execute on any system for which a JVM exists. This guarantees that basic properties such as how integer and real numbers are stored, precision associated with different data types, etc., will not change from one environment to another.
Java is open source. The Java Development Kit (JDK) including, compiler, linker, etc. is available from 44. Commercially licensed versions are also available from Oracle. The two most popular Integrated Development Environments (IDE) are also open source, Netbeans 45 and Eclipse 46.
While the Java language itself provides very effective ways to organize complex problems, solving them often requires additional numerical math tools. For a wide range of problems, the appropriate tools are available through the open source Apache Commons Math Library 47 or its successor, the Hipparchus Math Library 48. There are also other smaller, more specialized libraries that can be found online.
Java is used as the computing language for the Advanced Placement Computer Science course and is also widely taught in lower division undergraduate courses in computer science as well as other programs. Because of this, there is a large amount of material on learning Java – so much that any listing here would barely scratch the surface. The material consists of books, online tutorials, videos, forums, etc., much of it freely available. In general, this material focuses on learning Java without going very deeply into the subject matter used for examples. For material that is more closely focused on the subject matter of this contribution, a welldocumented course on using Java for simulation of physical systems can be found at 49. A more extensive discourse on the use of Java in simulation has been produced under the auspices of the Open Source Physics project 50.
Modelica
“The Modelica Language is a nonproprietary, objectoriented, equationbased language to conveniently model complex physical systems containing, e.g., mechanical, electrical, electronic, hydraulic, thermal, control, electric power or processoriented subcomponents” 51.
Modelica is of particular interest to the mechatronics/robotics community because it provides dynamic simulation of threedimensional and constrained mechanical systems and energetically correct interactions between the mechanical systems and electrical systems, hydraulic, pneumatic, etc. It does this through an underlying mathematical base that is capable of solving differentialalgebraic equations (DAEs) and an underlying computing syntax based on equations rather than algorithmic “storeinto” statements. Most users do not interact with Modelica at that level, however, but rather make use of an extensive set of libraries for various physical domains combined with a graphical user interface.
Systems with constraints are known as “acausal”. For a system defined by a set of N firstorder, nonlinear differential equations, constraints add algebraic equations to the system equations. Acausal systems have fewer than N independent initial conditions because the algebraic constraints reduce the effective order of the system. In mechatronics, almost all mechanical systems of any interest have constraints which is one reason Modelica is so valuable.
The Modelica language is defined by an open source specification maintained by the Modelica Association [modelica.org]. The actual implementation is left to any organization, including both commercial and open source entities. The most important opensource implementation is OpenModelica 52.
Modelica has been heavily used in certain industries, but less so in collegelevel teaching and, probably, not at all at the high school level. Thus, there is not nearly as much instructional material available as there is for much more widely used languages such as Python or Java. There is, however, some useful material to cite. The Open Source Modelica Consortium53 has a short, introductory course, as well as a longer, more indepth course at 54. “Modelica by Example” is an indepth, online book on Modelica 55. It is also available for sale (on a “pay what you can” basis) in various eBook formats. A set of introductory slides that uses many examples from “Modelica by Example” is available at 56. Because Modelica is explicitly aimed at simulation of physical systems, any instructional material on Modelica is, by definition, also about simulation of physical systems.
Gazebo
Since its debut in 2007, ROS (Robot Operating System) has grown into the most prominent opensource middleware framework for robotics research and development in industry and academia. Likewise, the Gazebo Simulator has become increasingly popular, thanks in part to its robust integration with the ROS framework 57. Gazebo is an opensource 3D dynamic simulator that provides a framework for realistic simulation of multiple robots dynamically interacting with each other in complex environments and scenarios 58, 59. Gazebo comprises a suite of features which include highperformance physics engines (e.g. Open Dynamics Engine, Bullet, etc.), highfidelity graphical rendering of environments and ability to generate realistic sensory data from the environment 58. These features allow users to create very realistic simulated environments and scenarios to learn to operate and test their robot designs and control algorithms without the risk of damaging their physical robots. Users can either leverage the rich library of existing robot models available with the software (ranging from mobile aerial and ground robots to stateoftheart humanoid robots) or design their own robot models from scratch using the Unified robot description format (URDF). As mentioned earlier, Gazebo will be used in the second part of this work to simulate the closedloop performance of a robot manipulator.
In the context of MRE education, Gazebo and ROS have been used in several ways to help students learn core robotics concepts. A twopart introductory robotics course was presented in60 using both Gazebo and ROS. A graduate course on mobile robotics and robotic manipulation in the context of Industry 4.0 was developed in61 using both software. In 62, Gazebo and ROS were both applied in an advanced course on humanoid robotics. In robotics education, opensource simulation tools are particularly relevant because they address the challenge of hardware affordability, making it possible to design labs partially or entirely in simulation 61, 63. The integration of these opensource software into the curriculum requires that the students are provided with opportunities to learn how to use them. For instance, in 61, the lab included preparatory sessions where the students complete comprehensive tutorials on the software available online 64.
GNU Octave
GNU Octave 65 is a highlevel programing language for numerical computations with builtin plotting and visualization tools. Octave is an opensource software with syntax largely comparable to and compatible with MATLAB. It can be freely redistributed and/or modified under the GNU General Public License. It runs on multiple platforms including GNU/Linux, macOS, BSD, and Windows. Octave was primarily developed by John W. Eaton and was conceived in 1988 as companion software for a chemical reactor design textbook. It is an interpreted programming language designed to solve a variety of engineering problems involving the numerical solution of linear and nonlinear problems and ordinary differential equation problems. It also contains an extensive collection of packages that enhances its core functionality by introducing field specific features. Some of the packages relevant to MRE systems include the Control, Image Acquisition, Image, Optim, and the Arduino package which are used for control design and analysis, image acquisition and processing, optimization, and Arduino control, respectively. Octave also includes both a graphical user and a traditional commandline interface that provides greater flexibility for novice and advance users. It can also be used to write noninteractive userdefined programs and can call C, C++, and Fortran code within Octave using OctFiles or using MATLABcompatible MexFiles. For new users and developers Octave also provides an active Wikipage containing installation, development, and documentation resources, in addition to the builtin Octave documentation 66.
In engineering education, GNU Octave has been used as an alternative platform to provide a computational environment to process and visualize data. Its appeal is linked to the fact that it is a free and opensource licensed product that is highly compatible with the MATLAB programming language. Universities have utilized Octave to create line following and robot simulators and to process data from mobile robot projects 67, 68, 69. It has also been combined with open source hardware such as the Raspberry Pi to perform image processing tasks in robot arm sorting applications 70. One of the main challenges with Octave that educators have pointed out is the lack of repository libraries like MATLAB’s mature toolboxes 68. Nevertheless, Octave has emerged as a promising alternative tool to MATLAB without compromising performance and productivity 70.
Community Impressions of the Opensource Software
This section provides a summary of a recent survey conducted in order to understand the extent to which professionals in various engineering disciplines, such as mechanical engineering, electrical and computer engineering, and mechatronics and robotics engineering are familiar with and utilize the OSS. The survey was conducted online and was distributed to community members through personal invitations and posts on academic, technical, and professional email lists and social media. A total of 131 responses were received from various stakeholders comprising of 55 current engineering students, 59 engineering educators, 14 industry professionals, and 2 Other (a mathematics and a retired professor). Figure 1 below shows department affiliation/area of expertise distribution for each of the above categories.
The Other affiliation in all the categories above comprised mainly of individuals in the field of computer science.
The OSS considered in this survey included Python, Java, Modelica, GNU Octave, Gazebo, and family of C languages. Table 1 below shows the percentage of survey participants with no/slight familiarity with each of these OSS.

Python 
Java 
Modelica 
GNU Octave 
Gazebo 
Family of C Languages 
Students 
50% 
72.50% 
100% 
92.68% 
90.25% 
26.83% 
Educators 
35.41% 
66.67% 
93.62% 
61.70% 
87.24% 
20.41% 
Industry Professionals 
18.18% 
36.36% 
90.91% 
54.54% 
63.63% 
9.09% 
Other 
100% 
100% 
50% 
100% 
100% 
50% 
As can be seen from the results in Table 1 , majority of the survey participants are familiar with the family of C languages which are typically taught as freshman introduction to programming/computing courses and are mainly used for hardware interface. Considering the focus of this article on model simulation, analysis, and controller implementation, this category of the OSS is not considered here. Although the remainder of the OSS, as discussed in this work, have the potential for use in MRE applications, there seems to be a lack of familiarity with these software, especially Modelica, GNU Octave, and Gazebo.
In response to a question on how the participants learnt the above OSS, 54.79% of the students, 93.44% of the educators, 73.68% of the industry professionals, and 100% of the others described their learning experience as selftaught using a book, a Massive Open Online Course (MOOC), or trial/error, as opposed to learning through a formal required/elective course.
As for prior experience, 56% of educators and 75% of industry professionals have used the OSS in their teaching and careers, respectively. On the other hand, 68% of the educators and 66.67% of the industry professionals definitely plan to use the OSS in their respective sectors in the future, whereas 30% of the faculty and 25% of industry professionals do not have certain plans in this regard.
Finally, through two openended questions, the survey inquired about the challenges the participants have faced in learning/using the OSS and their perceptions on the potentials and benefits of the OSS. A summary of the participants’ responses is presented below in Table 2 . Repeated responses are eliminated for brevity.
Challenges of OSS 
Potentials and Benefits of OSS 

Difficulties in initial installation, package additions, and setup – Dependency on specific operating systems – Where/how/what to learn for a specific application – Compatibility with commercial PCs and software – Lack of official technical support and quality documentation – Specialized support for intermediate users – Lack of familiarity with proper online search towards troubleshooting specific problems – Initial learning curve – Version compatibility and frequent updates – Reliability of the online information – Lack of motivating examples in the classrooms – Lack of practice exercises – Multiple offshoots/flavors – Potentially higher maintenance costs – Plagiarism issues – Security of online forums and websites – Conflicts with campus IT – Lack of maintenance – Specialized nature of OSS – Difficulty gauging the maturity, stability, and level of community support for certain OSS – Managing virtual environments and versions of Python – Time commitment for selflearning – Lack of opensource hardware drivers – An OSS with Simulink capabilities – Lack of prior familiarity among the students and faculty – Lack of course specific material – Technical documentation in nonEnglish languages – Crossplatform issues – Difficulty and time commitment to apply the OSS in control systems – Lack of university and college support – Lack of backward compatibility – Unresolved bugs – Corporate disapproval to contribute to the opensource community – Lowquality user interface – Not suitable for short timeframe professional projects – Customer acceptance – Little emphasis on performance and reliability – Lack of highquality embedded compilers – Potential legal issues 
– Based on community learning and collaboration – No acquisition costs – Source code availability and customizability – Security, stability, and privacy – Acceptance as industry standard – Strengthened relationship between the user and the software – Accessibility for lowincome and unprivileged society groups – Applicability to a wider range of projects – Desirable among future employers – Speeding up the project due to the available libraries and online solutions – Cost and time savings for industry – Promoting and enabling innovation for a wider community – Promoting selflearning – Longterm accessibility, even after graduation – Incorporation of community feedback – Large number of online tutorials – No licensing hassles – Suitable for online education – Adaptability to certain applications – Potential to promote learning core skills – Lack of commercials and marketing noise – Avoiding subscriptionbased billing models – Plug and play design – Ease testing multiple methods without a high cost of integration – Lack of need for training new hires by the industry

As can be seen from the community survey results, while the OSS have numerous potentials and benefits, there are still several challenges that need to be addressed before their widespread adoption. Although the current work is not intended to address all these challenges, it provides a specialized focus on the application of the OSS in MRE. Developed by the MRE faculty and students for the MRE community, this twopart contribution facilitates ease of entry and can enable MRE educators and students who wish to utilize the OSS in their courses and projects. Such an exposure for the students can further develop their computational mindset and problemsolving skills and therefore, prepare them for the job market. Future work will provide a more detailed analysis of the survey results to build a roadmap for further overcoming the challenges and more widespread adoption of the OSS in higher education.
Motor Case Study
A DC motor is one of the most commonly used actuators for generating rotational and translational motions in MRE systems. Furthermore, DC motors are perfect examples of an MRE system due to their multidisciplinary nature. The DC motor dynamics are governed by linear differential equations, and therefore, familiarity with numerical simulation of DC motor dynamics can pave the way to study other MRE systems that the students and professionals may encounter in practice.
The circuit diagram below is a typical schematic of an armaturecontrolled DC motor.
In this DC motor, the armature voltage v, is the input to the system whereas the rotational displacement of the motor shaft θ, the angular velocity of the motor shaft ω = dθ/dt, and the armature current i are assumed to be the system outputs. Using Kirchhoff’s and Newton’s laws, the dynamics of this system can be described by the following set of differential equations:
where J is the moment of inertia of the motor shaft, b is the viscous friction coefficient of the motor shaft, K_{t} is the armature constant, L is the armature electrical inductance, R is the armature electrical resistance, and K_{b} is the backemf constant. The values of these parameters used here are obtained from the datasheet information and model identification tests conducted on a brushed servomotor, C40E500FE, from Advanced Motion Controls 72. These parameters are reported in Table 3.
Parameter 
J 
b 
K_{t} 
K_{b} 
R 
L 
V_{nom} 
Value 
0.0026 kg.m^{2} 
0.01 N.m.s/rad 
0.66 N.m/A 
0.66 V/rad/s 
2.62 Ω 
0.05 H 
24 V 
To conform to common numerical simulation algorithms required to solve the DC motor dynamics in Equation 1 , the equations first need to be converted to a set of firstorder ODEs as shown below:
These equations can also be written in a statespace form:
where x(t) = y(t) = [θ(t), ω(t), i(t)]^{T}, u(t) = v(t), and the matrices A, B, C, and D are:
DC Motor Model Simulation and Analysis
An important first step in the analysis and control design for any Linear TimeInvariant (LTI) system, including DC motors, is to analyze their timedomain behavior. To this end, the introduced OSS are used in this section to obtain the step response of the DC motor dynamics in Equation 1 . For each OSS, the important code snippets are introduced and explained. Furthermore, other softwarespecific tools which can be used for further analysis and control design of LTI systems are introduced. Full code scripts can be accessed from the Github repository of the article at 73.
Python
In order to obtain the step response of the DC motor, the governing ODEs can be directly solved in Python using solve_ivp command from the scipy.integrate library 74. The other library required for this simulation includes numpy (imported as np). The solve_ivp command syntax for solving the ODEs in Equation 2 or Equation 3 is:
GNU Octave
To obtain the step response of the DC motor to a constant armature voltage of 24 volts, two approaches are used in this section. The first approach uses Octave’s builtin function ode45 while the second approach implements the step function in Octave’s Control package to obtain the time response of the governing equations of the motor. For this example, it is assumed that the motor starts from rest.
For the first approach, the ode45 command syntax for solving the ODEs in Equation 2 orEquation 3 is:
[~,X] = ode45(@(t,x)motor.model(t,x,v),time,x0,options);
where time is the time vector for the simulation, x0 is the initial condition for the system, options are the solver options, and model is an instance method from the userdefined DCMotor object named motor which implements the ODEs in Equation 2 as shown below:
function xdot = model(obj,t,x,u) theta = x(1); omega = x(2); i = x(3); v = u; % armature voltage xdot = [omega, (obj.Kt*iobj.b*omega)/obj.J, ... (vobj.Kb*omegaobj.R*i)/obj.L]; end
The obj refers to the instance of the DCMotor class. To implement the statespace representation as in Equation 3 , the model method is defined as:
function [xdot] = model(obj,t,x,u) A = [0 1 0; 0 obj.b/obj.J obj.kt/obj.J;0 obj.kb/obj.L obj.R/obj.L]; B = [0;0;1/obj.L]; C = [1 0 0;0 1 0;0 0 1]; D = 0; xdot = A*x+B*u; end
For the second approach, Octave’s Control package 76 needs to be installed and then loaded. Once the package is available, the motor step response can be simulated using the following syntax:
ssModel = ss(A,B,C,D); [~,~,X] = step(ssModel*u,time);
where the ss function creates an LTI statespace representation of the motor and the step function determines the step response of the LTI system. The simulated state vector is then stored in the variable X. Plotting these state variables results in a figure identical to Figure 3 .
Similar to Python’s Control Systems Library, Octave’s Control Package provides other system descriptions for defining transfer functions and frequency response data models. It also features other useful control system design and analysis tools. A brief summary of some of the available controlrelated commands in both Python and Octave are shown in Table 4.

Python 
Octave 
System Descriptions 
tf, frd 
tf, frd 
Frequency Domain Plotting 
bode_plot, nyquist_plot, nichols_plot 
bode, nyquist, nichols 
Time Domain Simulation 
forced_response, impulse_response, initial_response, input_output_respones, phase_plot 
lsim, impulse, initial, ode45, quiver 
Control System Analysis 
stability_margins, pzmap, root_locus, sisotool 
margin, pzmap,rlocus, sisotool(not available) 
Control System Synthesis 
acker, h2syn, hinfsyn, lqr, place 
acker, h2syn, hinfsyn, lqr, place 
Modelica — Equation Mode
Using Modelica in its equation mode comes very close to just transcribing the dynamic equations and clicking “GO”. Below is the content of the “equation” section of the Modelica model:
der(theta) = omega; der(omega) = ((b * omega) + Kt * i) / J; der(i) = ((Kb * omega)  R * i + v) / L;
where der is derivativewithrespecttotime. Note that these are true equations, not computing statements. They can be written in any order and, within each equation, the terms can be rearranged as long as the original algebraic meaning is maintained.
These listings are from OpenModelica’s Connection Editor. OpenModelica was used for this case study and all Modelica examples to follow 52.
As with any problem definition, the parameters and initial conditions must also be specified. This is also straightforward in Modelica. The parameters are given by:
parameter SI.Resistance R = 2.62 " Ohm"; parameter SI.Voltage v = 24.0 " v"; parameter SI.ElectricalTorqueConstant Kt = 0.66 "N.m/A"; parameter SI.ElectricalTorqueConstant Kb = Kt "N.m/A, equal to Kt for consistent units"; parameter SI.MomentOfInertia J = 0.0026 "kg.m^2"; parameter SI.RotationalDampingConstant b = 0.01 "N.m.s/rad"; parameter SI.Inductance L = 0.05 " H";
The parameter designation means that the value cannot be changed during simulation. An interesting aspect of Modelica is that the units of these parameters can be specified –SI is a shorthand for “Modelica.SIunits”. Using units is optional, but very useful. If the units had not been specified, all of these parameters would have been of type “Real”. The part in quotes after the value is a comment; C++, Java style of commenting, “//”, can also be used. The units shown in the comment section were written in by the user and, so, must be consistent with the units as defined by Modelica. Having the ability to specify units for physical quantities is part of Modelica’s multiphysicalmedia modeling capability.
The initial values of the state variables are specified in a similar manner:
SI.Angle theta(start = 0.0) "Shaft angle, rad"; SI.AngularVelocity omega(start = 0.0) "Shaft angular velocity, rad/s"; SI.Current i(start = 0.0) "Current in circuit, A";
The interesting variant here is that for each of the state variables, the initial condition is given as a start value. In this case, the problem is a pure ODE (i.e., a causal problem) so the state variables all have independent initial conditions. If the problem were a DAE (DifferentialAlgebraic Equation, acausal problem) the initial conditions would not all be independent so the start values would only be suggestions.
Results from simulating this system match the results presented above with Python. As can be seen in this section, for a problem expressed in equation form, there is essentially no “programming” needed; just copy the equations, list the parameter values, and define the system variables.
Modelica — Graphical Modeling Mode
The Modelica Standard Library contains a large set of modeling components from a variety of physical media. This is probably the most common way of creating simulations in Modelica. For the DC motor simulation, this means that components are selected from the library, they are given parameter values, and then the simulation is run. Figure 4 shows the model for the DC motor.
This is a semirepresentational model in that some of the components look like their physical counterparts but others can be more abstract. The physical orientation of components on the diagram does not have any significance.
In some sense, the most important component in this diagram is the emf unit. It handles the energy conversion between electrical and rotationalmechanical domains. With the emf unit in place, this model can do proper energy/power bookkeeping across the whole system.
Each of the energy domains is identified by its connectors: blue and white squares for the electrical portion and black and white circles for the rotational section. There is an additional energy domain on the diagram, thermal, identified by red squares, but that is not used in this simulation. A rightclick on any of the components will bring up a menu for its parameters. These can be entered directly as numerical values, but it is often preferable to use named parameter variables (as above) and enter the parameter names. That is what is done in this model.
Again, simulation produces the previous results. The modeling at this high level of abstraction can be very efficient if the library contains the needed components. The Modelica Standard Library has a large selection of components so can often be used successfully for simulation of systems crossing multiple energy domains.
Java
As mentioned earlier, Java is a general purpose, objectoriented language and is thus a major abstraction level below either of the Modelica solutions. It requires much more detailed programming (real programming, not equations as in Modelica) in return for which it provides the full power of objectoriented programming and much higher speed.
General purpose languages do not usually include the specialized mathematical tools needed for dynamic system simulation. However, most of these languages have a wide variety of libraries available to fill that gap. For these examples, the Hipparchus library is used to supply the ODE solver 48. As an aside, it is a large library and can meet many other mathematical needs in Java programming. The Hipparchus library is a successor to the Apache Commons Math Library.
As with most ODE solvers, a callback structure is used where the derivative values of all of the state variables are computed in a function (“method” in Java) and a reference to that function is given to the solver so it can call the derivative function as needed by whatever solution algorithm is being used. In Java, the derivative method is embedded in a class which is what is given to the ODE integration system.
This is what that function looks like for the DC motor system:
public double[] computeDerivatives(double t, double[] y) { // ODE: d angle / dt = omega; d omega/dt = (torque  b omega)/J // States: 0  angle; 1  omega; 2  current omega = y[1]; // Angular velocity of shaft ic = y[2]; // Current in circuit vBack = Kb * omega; torq = Kt * ic; double [] dydt = new double[3]; // Construct derivatives of states dydt[0] = omega; // d theta / dt = omegga dydt[1] = (torq  b * omega) / J; // d omega / dt = (torque  damping) / inertia dydt[2] = (v  vBack  R * ic) / L; // di/dt = (voltage  backemf  R * current) / L return dydt; }
There are many ways to run a simulation using ODE solvers. In this case, a structure is used that is very convenient for problems where control will be applied. In this structure, the ODE solver is called within an event loop and simulated from one event to the next. This simulation does not actually have any control, although it would be natural to add angular velocity or angular position control. The event that is present is data logging, writing the results to a file for later plotting and/or analysis. The event loop code is:
while(t <= tf) { if(t >= (tNextLog  guard)) { tNextLog += dtLog; pw.format("%g %g %g %g %g %n", t, y[0], y[1], y[2], volt); } tNextSim = tNextLog; // Simulate until next event ODEStateAndDerivative finalState = dp853.integrate(ode, state, tNextSim); y = finalState.getPrimaryState(); t = tNextSim; state = new ODEState(t, y); // setup for next iteration }
Any number of events can be included in a loop like this. The simulation is run until the next event. These events are all time based. Conditionbased events (for example, a state variable crossing a boundary value) are trickier and are best handled inside the solver itself, although they can be done iteratively with a structure such as this one. The dp853 refers to the DormandPrince8(5,3) ODE integration algorithm 77. The results are written to a file and plotted using an opensource plotting tool named gnuplot 78. As noted above, one of the advantages of Java is execution speed. By comparison, the Java version is at least 10 times faster than the ModelicaEquations version to compile and execute.
Summary, Conclusions, and Future Work
This article is the first part of a twopart contribution focused on promoting the use of OSS such as Python, GNU Octave, Modelica, Java, and Gazebo in MRE education. The current status, limitations, and potentials of the OSS, as surveyed among the community members, are included in the paper. One of the challenges frequently raised in the survey was lack of specialized educational materials. To this end, a DC motor, one of the most versatile components in MRE systems, is considered to showcase the application of the OSS in model simulation and analysis. Important code snippets are given to demonstrate the structure of the required programs; furthermore, the capabilities and limitations of each of these software are reviewed. The second part of this work introduces the application of the OSS in controller implementation for a slightly more complicated dynamic system, i.e., a robot manipulator. The opensource nature of the introduced software makes them an attractive simulation, design, and analysis solution for a wider range of educational institutions that aim to complement and enrich the quality of education. Furthermore, increased utilization of the OSS in industry, among their other numerous potentials, necessitates the need for further familiarizing the students with these tools. The application showcases and review of the potentials and limitations of each OSS in this work could allow MRE community to make informed judgements about what software to choose for their specific application and consequently, facilitate a wider adoption of these OSS. The complete codes for the discussed examples, along with Matlab scripts which is included as a point of comparison, are provided freely on a GitHub repository to make it accessible to the community. There are still other challenges facing the applications of the OSS in higher education, as highlighted by community survey results. More papers, similar to this work, online webinars, and short courses offered by the MRE professionals for the MRE community could help overcome these challenges and reduce adoption barriers to widespread use of the OSS in MRE higher education.