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 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.

First Commercial Tiltrotor Takes Flight

With the maiden flight of the Bell/Agusta 609 Tiltrotor in 2003, a revolution in commercial air transport was launched.  Like a helicopter, the BA609 takes off and lands vertically.  Once aloft, the nine-passenger aircraft flies like a turboprop--at nearly twice the speed of a helicopter—allowing it to satisfy a wide range of missions, from executive transport to oil exploration, search and rescue, and emergency medical services.   Approximately 300 flight hours have been logged on the first two prototypes during parallel testing at Bell’s flight test facility in Texas and Agusta’s facility in Italy.  Pilots have probed handling qualities and flight loads at all corners of the flight envelope including speeds of 310 knots, altitudes up to 25,000 feet, maximum gross weight of 16800 lbs, and maneuvers to +3 g’s and –1 g’s, proving that the BA609 is one of the world’s most versatile and potentially useful commercial aircraft.

The BA609 program was confronted with numerous challenges associated with developing an innovative aircraft.  This paper presents the BA609 as a case study for integrating model-based design and simulation into a large scale systems engineering process.  Starting with a basic set of customer needs and wants, the BA609 development team used frequent and extensive simulations to develop an integrated air vehicle with a fly-by-wire flight control system that has performed well in flight test demonstrations.   An iterative design approach--using Simulink®, MATLAB®, Real-Time Workshop®, and Control Systems Toolbox™--yielded a set of flight control laws for low-workload control along with active structural loads protection.

David King is the Analytical Integration Leader for the BA609 Program at Bell Helicopter.  In this role, he has overall responsibility for Flight Technology and Systems Engineering on the BA609 program.  Mr. King has 19 years experience at Bell and Boeing.  His responsibilities have included handling qualities, control laws, autopilots, fly-by-wire systems, civil certification, and tiltrotor project management.  Mr. King graduated from MIT with an M.S. in Aeronautics and Astronautics and from Lehigh University with a B.S. in Mechanical Engineering.

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 talk 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.

Accelerated Simulation of Communication Waveforms Using the Parallel Computing Toolbox™

Developing new communication algorithms and waveforms typically requires significant engineering effort and extensive, time-consuming simulations. MATLAB and Simulink are often used for this type of work. Although these tools have proven valuable in developing and simulating waveforms, designs of even modest complexity can require long simulation times. The MathWorks now offers a parallel computing solution, the Parallel Computing Toolbox™, which has the potential to address this problem. This presentation presents a methodology and algorithm for significantly accelerating the simulation of communication waveforms using the Parallel Computing Toolbox running on a computer cluster.

Brendan Garvey received a B.S.E.E. from Arizona State University in 1985.  He has over 20 years of experience in simulation, digital design and FPGA design, primarily in the areas of digital communications, modems and DSP.  Brendan currently works for General Dynamics C4 Systems in Scottsdale, AZ., as an FPGA task lead doing modem design and development. He can be contacted at brendan.garvey@gdc4s.com.

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 trade-offs 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.

Digital Payload Modeling for Space Applications

In recent years in the commercial or government space businesses, there has been a movement away from RF analog payloads for pure “bent-pipe,” or “transponder” applications, and towards digital payloads that utilize digital signal processing (DSP) to accomplish the same goals while providing enhanced mission capability and flexibility.  Of particular interest is “on-orbit” reconfigurability, which allows a payload to be flown now, and other missions to be defined in the future.  These types of digital payloads provide a unique challenge to the DSP engineer.  The DSP engineer and support team needs to be able to design and model a digital payload in a software environment such as MATLAB, and turn around useable code quickly in order to respond to customer needs as business conditions change. 

Bradford Watson holds a B.S.E.E. from Metropolitan State College of Denver, an M.S.E.E. (signal processing emphasis) from the University of Colorado at Denver, and is a Ph.D. Candidate , ECE (signal processing emphasis), at the University of Denver.

He started with Lockheed Martin in 1997 designing ground support hardware, but soon moved into VHDL/FPGA work.  He gained experience with DSP algorithms nd applications to FPGAs in the late 1990s.  Watson worked at Lucent Technologies and Bell Labs during the telecom boom and bust (2000 – 2002), then returned to Lockheed Martin in 2002.  Since then, he has worked extensively as an algorithm developer, with emphasis on VHDL implementation for FPGAs and ASICs.  He has been part of two projects as the DSP lead (DCU and a classified program), and participated in several proposals for various government customers.

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 helps improve engineering productivity with safety-critical systems, including those that must meet DO-178B certification standards. A workflow will be presented 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.

Verification and Validation of Models and Code

Verification and validation has emerged as one of the most promising areas for improvement at organizations using Model-Based Design. New V&V methodologies and tools are now available to help address pressing problems with quality and development process inefficiencies. This presentation concentrates on V&V tools and techniques for verifying detailed component models and the code generated from those models. These techniques include formal analysis, test generation, requirements testing, and standards checking.

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 automatically generate C code from your Embedded MATLAB code. We 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
• How to generate C code from your Embedded MATLAB code directly from MATLAB desktop
• How to 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