Best Practices for Establishing a Model-Based Design Culture

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 theoretical benefits of this approach. In this session, we introduce the concepts of Model-Based Design, highlight some of its benefits, and discuss in detail 10 best practices for adapting Model-Based Design across an organization. These best practices have been gleaned from successful and not-so-successful transformations to Model-Based Design at companies from a variety of industries.

Model-Based Design for GNC-Based Systems

The use of Simulink and other MathWorks products provides a good platform for Model-Based Design, which improves productivity and provides an enhanced development and test environment. Using Model-Based Design, engineers can detect a greater number of bugs and deliver more efficient and error-free code than possible with traditional methods. Model-Based Design is especially effective in meeting the real-time challenges involved in developing guidance control systems and often provides remarkable productivity numbers as compared with non-model-based programs. This session presents lessons learned, including code-generation techniques, from recent experience with the Long Range Land Attack Projectile (LRLAP) program. A description of interfaces between Simulink and selected configuration and requirements management tools will also be presented.

James Craft was the Integration and Test Lead for the LRLAP/DDG-1000 Program at Lockheed Martin. He is currently working in the Systems Engineering Architecture Development Team for the Medium Extended Air Defense System (MEADS). In this role, he has overall responsibility for all system interface definitions and interface requirements. Craft has over 25 years experience as a systems and software engineer, mostly at Lockheed. His responsibilities have included embedded real-time systems, radar processing, GNC applications, and model-based development. Craft has an M.S. in Software Engineering from National University and an undergraduate degree from Mansfield University.

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.

Developing Communications and ISR Systems Using MATLAB and Simulink

A video surveillance UAV application will provide 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 will be 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 will serve to illustrate the ease with which Simulink enables multidomain modeling.

Nate Taylor, NASA Kennedy Space Center

The Launch Control System (LCS) project at Kennedy Space Center is designing a new control system for monitoring and launching the next generation of NASA manned launch vehicles. Simulations of ground support equipment and the launch vehicle systems are required throughout the life cycle of the LCS to test software, hardware, and procedures and to train the launch team. The simulations of the ground support equipment at the launch site are being developed using Simulink and the Real-Time Workshop Embedded Coder™. The code generated from the Simulink models is executed using a NASA-developed simulation engine called Trick. The interfaces to the executable simulations are being implemented as custom blocks in the Simulink models. These blocks allow the model developer to instrument the model for both the Simulink development environment and the Trick execution environment.

Nathan (Nate) Taylor is the Simulation Lead for the Launch Control System project at the Kennedy Space Center. Taylor has worked for NASA for 21 years as a computer engineer, developing control system software for the first 9 years and developing simulation systems for the last 12 years. He has a BEE from the Georgia Institute of Technology.

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 will be 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 will also be presented.

Introduction to Parallel Computing with MATLAB

This master class will show you how the new products and features for MATLAB enable you to take advantage of recent advances in computer hardware, from multiprocessor machines to computer clusters. You will learn how to utilize multiple cores in your desktop machine through the new parallelism capabilities of MATLAB and Parallel Computing Toolbox™. We will also introduce the use of MATLAB Distributed Computing Server™ on a computer cluster to speed up your algorithms and handle larger data sets.

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

In this workshop, we will 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

R2008a included a major update to object-oriented programming in MATLAB, enabling easier development and maintenance of large applications and data structures. Using engineering examples, this master class will demonstrate 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 will introduce new concepts and tools for effective verification and validation based on model analysis techniques. You will learn how to:

• Verify that your models meet requriements 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