Pragmatic Strategies for Adopting Model-Based Design for Embedded Applications

When transitioning to Model-Based Design for embedded systems development, it is essential to consider an overall plan spanning people, development processes, and tools. A common sense approach when beginning any process improvement activity is to first identify the problem to be solved and then develop a plan to implement the solution. When transitioning to Model-Based Design, performing the transition in an iterative manner—do, learn, adjust, and repeat—has been shown to be most effective. The end goal is a development process where the model is the design, verification is done throughout the development process using simulation, and the implementation of the entire application onto target hardware is highly automated. Faced with design and organizational complexity, time, quality, and cost pressures, the transition is akin to changing a flat tire while moving down the highway. Choosing the right first steps is key to a successful transition. This presentation provides a set of practical strategies for determining the first steps when deploying Model-Based Design and code generation in production development processes.

A Model-Based Design and Testing Approach for Orion GN&C Flight Software Development

Joel Henry, Draper Laboratory, NASA/Johnson Space Center

The Orion Crew Exploration Vehicle (CEV) will be the first human spacecraft built by NASA in almost three decades and will be the first vehicle to perform both Low Earth Orbit (LEO) missions and lunar missions since Apollo. The awesome challenge of both designing a guidance, navigation, and control (GN&C) system for this vehicle and implementing it in flight software (FSW) is countered by the opportunity to take advantage of the latest improvements in software engineering processes and tools for Model-Based Design and testing. This session describes the FSW processes and tools prescribed by NASA’s Orion prime contractor, Lockheed Martin (LM), and the details surrounding how the joint NASA/LM GN&C design teams will be essentially “writing” the GN&C FSW by participating in this integrated design/implementation environment. The challenges of the process and lessons learned to date will be summarized, including the architecture design approach and modeling standards using MATLAB and Simulink for the GN&C executive and its algorithmic Computer Software Unit (CSU) components, unit-level and closed-loop testing simulation and test environments, and the test and verification of the code products generated from Real-Time Workshop.

Joel Henry is an engineer for Draper Laboratory at the NASA/Johnson Space Center in Houston, Texas. He is currently assisting the implementation of Model-Based Design for the Orion GN&C FSW development process. Joel has a background in mechanical engineering and computer science. He has a BSME from the University of Texas at Austin and is a Licensed Professional Engineer in the state of Texas.

Model-Based Design for Sensor Systems

Sensor systems are at the heart of modern C4ISR and unmanned systems. Development of these systems requires sensor characterization and calibration, design and deployment of embedded signal processing algorithms, and development of higher-level analysis capabilities. This session discusses how MATLAB^® and Simulink^® can be used for all stages of sensor system development. The case study used will be an optical video-tracking system; however, the design methodology can be extended to additional sensor types.

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 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.

Large Model Simulation in Simulink Using Legacy Code Tool

Dave Gutz, GE Aviation

Like others running multiple models in mixed environments, GE is enthusiastic about the capabilities of running legacy code in Simulink and with Simulink derived code. The Legacy Code Tool has made that process much easier for those new to Simulink. We share how we made the process work for some very large models using Simulink and discuss some of the pitfalls of this approach.

Dave Gutz is principal engineer for GE Aviation Aircraft Engines in Lynn, Massachusetts. He works throughout the company on special tasks related to control system development. He has degrees in mechanical engineering from Cornell and MIT.

New Capabilities for Aerospace Control System Design

MATLAB and Simulink are standard tools for control system design within the aerospace and defense industry. Over the past several releases, several capabilities have been introduced that aid in the control design process. In this session, you will learn about the new functionality for:

• Easy tuning and implementation of PID controllers in Simulink
• Estimating the frequency response of a Simulink model using simulation
• Taking model uncertainty into account when linearizing a Simulink model by leveraging a new block linearization configuration feature

The capabilities covered in this session will be useful for both casual and expert control designers.

Air Traffic Control Modeling with SimEvents

Air traffic management systems (ATMSs) are broad applications encompassing avionics, radar systems, and higher-level flight traffic optimization. NEXTGEN is the umbrella program within the U.S. for upgrading ATMSs. This session discusses how SimEvents® can be used to develop a simulation of an air traffic management system. This system monitors various statistics such as average delay for a flight, number of planes in a particular center or in an airport, length of landing and takeoff queues, and so forth. The demo uses a small-scale (three to four airport) model and a flight schedule of a few hundred flights to illustrate the modeling techniques. The demo also shows you how to scale this approach to a large-scale simulation of 20,000 flights and 194 airports. We also show you how to simulate different “what if” scenarios using this large-scale model.

Model-Based Design for High-Integrity 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.

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.

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.