Automated Generation of Code for a Discrete-Event Simulation Architecture from Simulink

Jon Maram, Lead Engineer at Millennium Engineering and Integration Company (MEI)

Customers of Millennium Engineering and Integration have faced requirements to develop missile defense simulations in discrete-event-based development environments as well as in Simulink^®. Historically, these requirements have led to an interest in porting models developed and validated within Simulink into code compatible with the discrete-event-based environment. Doing so was found to require time-consuming and labor-intensive development of model-specific API layers, in a process that needs to be repeated whenever the interfaces with a model are changed.

To accelerate this process, MEI developed a customized interface based on the code generation capabilities of Simulink, which completely automates the generation of code for an end-to-end scenario within the discrete-event-based environment, and automates the build process and the creation of run-time configuration files. This interface has been validated with a number of Simulink test cases, and with several end-to-end missile defense scenario simulations that MEI has developed.

Jon Maram is a lead engineer at MEI and is currently leading the development of code generation capabilities from Simulink to event-based targets. Jon has 28 years of experience in the development of advanced measurement systems and associated software for aerospace and defense applications, as well as development of software models and simulations for these applications. Jon received a bachelor’s degree in biophysics from the University of California at Berkeley and a master’s degree in physics from the University of Oregon.

Modular Infrastructure for Rapid Flight Software Development

Craig Pires, Systems Analyst at NASA Ames Research Center

NASA Ames Research Center is currently developing processes and infrastructure for generating flight software for the support of the LADEE Mission as well as future small spacecraft missions. The basis of these processes is to employ automatic software generation from system dynamics modeling tools such as Simulink. These techniques have been successfully used in a number of DoD space and flight applications, and have the potential to greatly improve software development cost, time, and reliability.

Initial models are developed and control systems defined using the dynamic modeling tools. Software is then automatically generated from the models and downloaded and run on real-time processors for processor-in-the-loop (PIL) and hardware-in-the-loop (HIL) simulations. In parallel, Mission Operations software and interfaces are developed and used with the simulations to assist in test and debug, while also gaining experience and confidence in the tools. Likewise, automated scripts can be reused in each of these steps for software verification and validation. The latest efforts have been to integrate these models into a Flight Ready C&DH Executive while still retaining the modular design of the models.

Using this approach, the NASA Ames team recently demonstrated closed-loop control of a 6 DOF Hover Test Vehicle. This HTV and others are being used to further refine and optimize processes and tool options. In this talk, we will discuss the software development approach and highlight our experiences and lessons learned from implementing the software for the test vehicle.

Craig Pires is the Avionics and Flight Software Command and Data Handling Lead for the LADEE Lunar Mission, currently scheduled to launch in 2012. He has worked on flight simulation for over 15 years using Full-Mission Flight Simulators to investigate flight dynamics and increase flight safety. He helped establish the NASA Ames Small Spacecraft effort to develop new methods to drastically reduce mission costs and development time.

Model-Based Design: Best Practices and ROI

The transition to Model-Based Design requires careful management, both to demonstrate its short-term benefits and to establish a culture that enables the full realization of the value of this approach. In this session, we introduce the concepts of Model-Based Design, discuss how best to capture and quantify the return on investment, and discuss some best practices for adopting Model-Based Design across an organization.

Using Physical Modeling Tools to Design Power-Optimized Aircraft

A number of initiatives are underway to make tomorrow’s aircraft more efficient while reducing aircraft emissions. Projects such as the Power-Optimized Aircraft and the Clean Sky Joint Technology Initiative are focused on finding more efficient ways of transporting power throughout an aircraft while improving the environmental impact of air transport. Efforts like these require an optimized system design. This session focuses on achieving this goal by modeling a flight actuation system and performing tradeoff studies that can lead to more realistic requirements and optimized system performance.

Using MATLAB for Data Acquisition, Instrument Control, and Data Analysis

Learn how you can use MATLAB^® to acquire and analyze data, control measurement hardware, and build standalone applications. A MathWorks engineer will show how performing these tasks in one software tool can save you time and reduce costs. See how you can:

• Acquire, analyze, and visualize live data in MATLAB
• Use MATLAB with data acquisition boards, oscilloscopes, RS-232 devices, signal analyzers, signal generators, and other equipment

Developing Communications and ISR Systems Using MATLAB and Simulink

A video surveillance UAV application provides attendees with an example of the integrated design and modeling of three subsystems in a single development environment. An antenna pointing control subsystem, a video imaging subsystem, and a communications link are jointly modeled in Simulink with several components implemented as Embedded MATLAB^® blocks. Real-world tradeoffs of control loop response, platform motions, bit error rates, and video processing complexity serve to illustrate the ease with which Simulink enables multidomain modeling.

Solving Data Analysis Challenges Using MATLAB and Statistics Products

Engineers often have significant quantities of data that need to be analyzed. Complicating the need to rapidly analyze the data are anomalies (drop-outs, sensor failures, etc.), which often lead to manual and laborious tasks to discover, categorize, and deal with missing or bad data. An example application is presented in order to demonstrate how MATLAB and statistics add-on products can be used to improve data quality and enhance understanding of the data through quantitative statistical methods.

Model-Based Design for Safety-Critical Systems

MathWorks products enable Model-Based Design, which improves engineering productivity with safety-critical systems, including those that must meet DO-178B certification standards. This session presents a workflow to demonstrate how MathWorks tools can be used for requirements validation, algorithm design, traceability, code generation, test generation, formal methods verification, and processor in-the-loop testing. Interfaces to requirements management and configuration management tools are also presented.

Optimizing Simulation Performance in Simulink

This master class covers a variety of techniques that you can use to increase the simulation performance of your Simulink models. Two case studies are covered: one that models a hybrid system and one that models a discrete system. The first example explores concepts such as making “quick checks,” profiling execution times, integrating with MATLAB, and dealing with continuous time solvers. The second example addresses additional performance considerations for discrete systems such as leveraging frames and managing visualizations. The presentation ends with a discussion about speeding up batch runs of simulations by taking advantage of advances in hardware, such as multicore machines and computer clusters, and about running very long simulations.

Embedded MATLAB: Designing Embeddable Algorithms and Automatically Generating C Code with MATLAB

In this master class, we showcase new capabilities of MathWorks products that enable you to generate C code from your Embedded MATLAB code. You will learn about these capabilities by going through an example for the design of a video processing system. Through demonstrations, you will learn how to:

• Create and modify your MATLAB algorithms to be compliant with the Embedded MATLAB language subset
• Generate C code from your Embedded MATLAB code directly from MATLAB desktop
• Call your Embedded MATLAB code as a new block within Simulink to integrate and simulate your algorithm as part of a larger system model

Introduction to Object-Oriented Programming in MATLAB

MATLAB includes object-oriented programming capabilities that enable easier development and maintenance of large applications and data structures. Using engineering examples, this master class demonstrates how to define classes and work with objects, highlighting the benefits of this programming approach over traditional procedural techniques. Features covered include class definitions, properties, property attributes, methods, method attributes, and inheritance. No knowledge of object-oriented programming is required.

New Concepts and Tools for Effective Verification and Validation Based on Model Analysis

Verification and validation is critical for implementation of Model-Based Design in production programs. This master class introduces new concepts and tools for effective verification and validation based on model analysis techniques. You will learn how to:

• Verify that your models meet requirements and modeling standards
• Prove correctness of the generated code and trace this information back to the model
• Use automation and tools to aid with design reviews and document generation