Control Design

Description Topics

• Model-Based Design
• Modeling and Simulation
• Control System Software Design
• Embedded Software Testing and Implementation

Modeling and Simulation

To effectively design an embedded control system and accurately predict its performance, designers must understand the behavior of the entire system in which the control system will reside. MATLAB and Simulink form the core environment for Model-Based Design for creating accurate, mathematical models of physical system behavior. The graphical, block-diagram paradigm of the MathWorks environment lets you drag-and-drop predefined modeling elements, connect them together, and create models of dynamic systems. These dynamic systems can be continuous-time, multi-rate discrete-time, or virtually any combination of the three. You can create custom model elements or reuse legacy code-based models by incorporating C, Fortran, or Ada code directly into the modeling environment.

Hierarchical models of complex control systems can be built using Simulink. Click on image to see enlarged view.

The modeling environment is hierarchical and self-documenting. System structure and function can be clearly expressed by grouping model elements in virtually any combination, allowing large teams to work concurrently on the design. Libraries of hierarchical elements can be quickly created, allowing those elements to be reused easily by other members of the design team or on subsequent designs. Fully integrated into the environment is the capability to graphically model event-driven systems using state charts, truth tables, and flow diagrams. Specialized capability for mechanical and electrical power systems allows models of these systems to be constructed using modeling elements that correspond directly to the structure of the physical system, avoiding the need to express them as mathematical equations. If prototype or actual physical systems are available and input/output data can be acquired from them, mathematical models can also be created using system identification techniques.

A state machine, shown here as a Stateflow model, describes event-driven systems. Click on image to see enlarged view.

As soon as a hierarchical element of the model is constructed, that element can be simulated. Simulation allows specification, requirements, and modeling errors to be found immediately, rather than waiting until later in the design effort. As the model becomes larger, through the addition of hierarchical elements or by increasing the complexity of existing ones, the designer can continue to find and correct errors during simulation by using the model coverage, performance profiling, and interactive debugging features. When the physical system model is specified to the required level of detail and simulation has shown the model to be accurate, the control system can be designed.

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