New Control System Toolbox 7.0 delivers a powerful suite of capabilities

The Control System Toolbox provides tools for systematically analyzing, designing, and tuning linear control systems. You can specify a linear model of your system, plot its time and frequency responses to understand how the system behaves, tune the controller parameters using automated and interactive techniques, and verify performance requirements, such as rise time and gain/phase margins. Workflow-based graphical user interfaces (GUIs) guide you through each step of the analysis and design process.

In R2006a the integration of Control System Toolbox, Simulink Control Design and Simulink Response Optimization greatly simplifies and streamlines the task of compensator design.

Key Features

Ways to optimize your memory space and achieve faster code performance

Is your MATLAB program running slow? Do you have multiple iterative loops (for and while loops) in your program that handle matrices and arrays?

for and while loops that incrementally increase the size of a data structure each time through the loop can adversely affect performance and memory use. Repeatedly resizing arrays often requires that MATLAB spend extra time looking for larger contiguous blocks of memory and then moving the array into those blocks. You can often improve on code execution time by preallocating the maximum amount of space that would be required for the array ahead of time.

How to perform array preallocation?

Consider the following MATLAB code. The code below creates scalar variable a and b. Then gradually increases the size of a and b in a for loop instead of preallocating the required amount of memory at the start.

The code above takes average 0.23 seconds.

Change the first 2 lines to preallocate a 1-by-8000 block of memory for a and b initialized to zero. This time there is no need to repeatedly reallocate memory and move data as more values are assigned to a and b in the loop.

With this modification, the code takes only 0.0013 seconds (over hundred times faster). Preallocation is often easy to do. In this case it was only necessary to determine the right preallocation size and add two lines.

What if the final array size can vary?

One approach is to use the upper bound on the array size and cut the excess after the loop:

The average run time of the code above is 0.15 seconds without array preallocation and 0.05 seconds with array preallocation.

For detail information on Array Preallocation, please kindly refer to the URL below:
http://www.mathworks.com/access/helpdesk/help/techdoc/matlab_prog/f8-793781.html

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Applying Statistical Methods using MATLAB

This two-day course provides an introduction to statistical tools in MATLAB and the Statistics Toolbox. Topics include data file input and output, handling large and incommensurate data sets, computing descriptive statistics, statistical plotting and visualization, fitting distributions to data, bivariate and multivariate regression, random number generators, simulation, and basic inferential methods. Examples and exercises cover a cross-section of application areas in science, engineering, and finance.

Applying Finite State Machine Modeling with STATEFLOW

This one-day workshop provides an understanding of how to use Stateflow to model finite-state machine theory and supervisory logic. The course discusses how to interact with Simulink, and graphically build flow diagrams and functions. Code generation and sending data out of Stateflow are briefly mentioned in this course as well.

SIMULINK S-Functions for System Algorithm Modeling

This is a hands-on one-day workshop that provides an extensive coverage on Simulink S-functions, with examples and exercises that comprehensively employ the features to custom system and algorithm development. The course emphasizes S-function concepts and methodologies. The course targets intermediate and experienced users with exposure to Simulink and MATLAB.

Implementing FPGA for Signal Processing using SIMULINK

This is a one-day hands-on workshop to learn how to develop signal processing algorithm for FPGA device using system level tools such as Simuink. The basics of using the Signal Processing Blockset in SIMULINK to analyze and design a signal processing system will also be covered.

Clarkson University sought to unify the freshman curriculum across their engineering departments by standardizing on software that students would use throughout their studies and, eventually, in industry.

The software tools selected would also need to engage students in a way that helps them determine which engineering discipline to pursue.

In the past, individual faculty members used various software tools to teach ES100: Introduction to Engineering Use of the Computer.

"Because professors recommended their own software, it was difficult to ensure that engineering students would acquire uniform knowledge of a particular tool or language," says Carroll.

Supporting many software products also increased costs for the university in terms of maintenance, training, and system resources. Because the software was also required for out-of-class homework assignments, students had to purchase licenses for each instructor-selected software package.

"Knowledge of MathWorks tools is widely sought after by those engineering faculty teaching upper-level courses and performing research. After speaking to our recent graduates, it became even clearer how widely adopted MathWorks tools are in industry."

By standardizing on MathWorks tools, Clarkson University has prepared first-year students-regardless of engineering discipline-for upper-level classes that require MATLAB, Simulink, and related products.

"By standardizing on MathWorks tools, all of our freshman engineering students are exposed to the same topics and obtain the knowledge they need to function at a high level," says Carroll.

Although MathWorks tools are used across all of the engineering disciplines, they are most prevalent through the electrical engineering courses, beginning with the ES100 prerequisite.

In one of Carroll's ES100 computer lab lectures, his students solve a set of equations that determine the maximum power transfer to a resistive load. They begin by writing a script using MATLAB, apply MATLAB functions, such as max(), and perform indexing to determine the resistive load.

"If my students had to numerically analyze the possible combinations by hand, they would get pretty frustrated," explains Carroll. "MATLAB connects the students to the topic and lets them explore various possibilities quickly."

In addition to the lecture component, an integrated lab experience enables students to use MATLAB and the Data Acquisition Toolbox with engineering equipment and instruments. In the lab, students learn how to design, build, test, and document simple circuits, such as a keyless entry system.

"By using MathWorks tools in our lab setting, students can determine whether they prefer the more hands-on aspects of engineering or are more interested in the underlying analysis and design," says Carroll.

In EE251: Dynamical Systems, sophomores apply class concepts using MATLAB and Simulink to model electrical, mechanical, thermal, and fluid systems.

In the dynamical systems course, Carroll typically assigns a fun class project such as the "Electric Dukes of Hazzard," in which students "build" an electric car.

In EE321: Systems and Signal Processing, juniors use the Control System and Signal Processing toolboxes to explore conceptual, analytical, and computational issues in communications, signals processing, and controls.

In EE401: Digital Signal Processing, seniors use Simulink with the Signal Processing Blockset (formerly the DSP Blockset) to develop DSP applications. They also use Real-Time Workshop to generate embedded code from Simulink models. They then implement the code in real time using the Embedded Target for TI C2000 DSP.

Awarded a three-year Course, Curriculum, and Laboratory Improvement grant from the National Science Foundation, Clarkson's Wallace H. Coulter School of Engineering will further enhance its multidisciplinary lab project courses using MathWorks tools.

• Control System Toolbox
• Data Acquisition Toolbox
• Embedded Target for TI C2000™ DSP
• MATLAB®
• Real-Time Workshop®
• Signal Processing Blockset
• Signal Processing Toolbox
• Simulink®