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Glossary (Control Design and Simulation Module)
A |
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| Ackermann | A technique for placing poles in a system model. Use the CD Ackermann VI to implement this technique. |
| actuator | A physical device that applies the control action to the plant. |
| auto-covariance | A measure of how closely a value of a stochastic process, such as noise, varies with the subsequent value of that process. |
B |
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| balanced system model | A system model with identical controllability and observability diagonal Grammians. Use the CD Balance State-Space Model (Diagonal) VI and the CD Balance State-Space Model (Grammians) VI to balance a state-space system model. |
| Bode plot | A plot that shows the gain and phase margins of a system model for a common frequency range. Bode plots show how close a system model is to instability. Use the CD Bode VI to create a Bode plot for a system model. |
C |
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| CGD | Common Graph Description. The format the Simulation Model Converter uses to store each system, subsystem, block, and line from a model developed in The MathWorks, Inc. Simulink® simulation environment. |
| constraints | See inequality constraints. |
| continuous model | A dynamic system model that represents real-world signals, which vary continuously with time. You characterize a continuous model by differential equations. See also discrete model. |
| controller | A device that regulates the operation of a dynamic system. |
| cost | The scalar value that results from solving a cost function. |
| cost function | A performance measure you want to minimize when designing optimal parameters. A cost function is a functional equation that maps a set of points in a time series to a single scalar value. |
D |
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| design | See optimal design. |
| direct feedthrough | A relationship between a function input and a function output in which the function uses the input at the current step to calculate the output at the current step. See also indirect feedthrough. |
| discrete model | A dynamic system model that represents signals that are sampled at discontinuous intervals in time. You characterize a discrete model by difference equations. See also continuous model. |
| distributed parameter model | A physical model that you can describe with partial differential equations. See also lumped parameter model. |
| dynamic system | A system whose behavior varies with time. |
| dynamic system model | A differential or difference equation that represents the behavior of all or part of a dynamic system. |
E |
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| empirical modeling | A modeling technique in which you use experimental data to define a dynamic system model. See also physical modeling. |
F |
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| feedback cycle | A cycle in which data flow originates from an output of a function or subsystem and terminates as an input of the same function or subsystem. See also indirect feedthrough. |
H |
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| HIL | Hardware-in-the-loop. A simulation configuration in which you test a controller implementation with a simulated system. See also RCP. |
I |
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| indirect feedthrough | A relationship between a function input and a function output in which the function does not use the input at the current step to compute the output at the current step. See also direct feedthrough. |
| inequality constraints | Any restrictions you place on how the optimal design process determines optimal parameter values. You can define inequality constraints for the control action, the output, the rate of change of the control action, and the rate of change of the output. |
| Input Node | A collection of input terminals attached to the Simulation Loop. Use the Input Node to configure simulation parameters programmatically. See also Output Node. |
L |
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| linear model | A dynamic system model that obeys the principles of superposition and homogeneity. See also nonlinear model. |
| linearize | A procedure that approximates the behavior of a nonlinear model. See also trim. |
| lumped parameter model | A physical model you can describe with an ordinary differential equation. See also distributed parameter model. |
M |
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| model | See dynamic system model. |
N |
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| nonlinear model | A dynamic system model that does not obey the principles of superposition or homogeneity. See also linear model. |
O |
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| offline | A simulation configuration in which you use software to simulate the controller and the system you want to control. No hardware is involved in an offline simulation. |
| optimal design | The process of selecting parameter values that maximize a measure of performance. |
| Output Node | An output terminal on the Simulation Loop. Use the Output Node to view any errors the simulation diagram generates. See also Input Node. |
P |
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| parameter bounds | Any restrictions you place on possible parameter values during the optimal design process. The optimal design process does not consider any parameter values outside the bounds you define. |
| parameter design | See optimal design. |
| parameter mesh | A set of points that defines the distribution patterns of sets of parameter values and the number of sets to generate. |
| period | The amount of time in which a discrete linear Simulation function must complete. |
| physical modeling | A modeling technique in which you use the laws of physics to define a dynamic system model. See also empirical modeling. |
| plant | A dynamic system whose behavior you want to observe, replicate, or manipulate. |
R |
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| RCP | Rapid control prototype. A simulation configuration in which you test plant hardware with a software model of the controller. See also HIL. |
S |
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| simulation diagram | A LabVIEW diagram that allows you to use Simulation functions within a Simulation Loop or simulation subsystem. A simulation diagram, like other LabVIEW diagrams, has the following semantic properties: The order of operations is not completely specified by the user. The order of operations is implied by data interdependencies. A function can execute only after all necessary inputs have become available. Outputs are generated after a function completes execution. |
| Simulation Loop | The structure that executes the simulation diagram over multiple time steps. |
| skew | The amount of time by which you want to delay the execution of a discrete linear Simulation function. |
| SQP | Sequential Quadratic Programming. A general-purpose numerical optimization algorithm. |
| subsystem | A section of a simulation diagram you represent with a single icon instead of multiple Simulation functions and wires. |
T |
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| time-invariant model | A dynamic system model whose parameters do not change with time. |
| time-variant model | A dynamic system model whose parameters change with time. |
| trim | A procedure that searches for the values of states and inputs that produce output and/or state derivative conditions you specify. See also linearize. |