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Owning Palette: Control Design VIs and Functions
Requires: Control Design and Simulation Module. This topic might not match its corresponding palette in LabVIEW depending on your operating system, licensed product(s), and target.
Use State-Space Model Analysis VIs to calculate properties of a given state-space model, such as observability, detectability, controllability, stabilizability, similarity transformations, model balance, and system Grammians.
The State-Space Model Analysis VIs do not support the Stochastic Systems VIs.
|CD Balance State-Space Model (Diagonal)||Balances the State-Space Model using a diagonal similarity transformation. This transformation reduces the ratios of rows and columns norms of the system matrix A, or of the matrix S defined by the following equation.
S = [A B| C 0]
|CD Balance State-Space Model (Grammians)||Calculates a Balanced State-Space Model based on Grammians. The resulting balanced transformation has identical controllability and observability diagonal Grammians.|
|CD Canonical State-Space Realization||Transforms the State-Space Model to a canonical form that Form Type specifies. This VI also returns the similarity Transformation Matrix that this VI uses to transform the given system.|
|CD Controllability Matrix||Calculates the Controllability Matrix of the State-Space Model. You can use the controllability matrix Q to determine if the given system is controllable. A system of order n is controllable if Q is full rank, meaning the rank of Q is equal to n. This VI also determines if the given system is stabilizable. A system is stabilizable if all the unstable eigenvalues are controllable.|
|CD Controllability Staircase||Calculates the controllable staircase similarity transformation of the State-Space Model. You can use the staircase representation to identify controllable and uncontrollable states by simple inspection of the A and B matrices of the transformed model.|
|CD Grammians||Calculates the controllability or observability Grammian of the State-Space Model for a stable system. The system can be continuous or discrete. You can use this VI to balance state-space models and to study controllability and observability properties of the system.|
|CD Observability Matrix||Calculates the Observability Matrix of the State-Space Model. You can use the observability matrix N to determine if the given system is observable. A system of order n is observable if N is full rank, meaning the rank of N is equal to n. This VI also determines if the given system is detectable. A system is detectable if all the unstable eigenvalues are observable.|
|CD Observability Staircase||Calculates the observable staircase similarity transformation of a State-Space Model. You can use the staircase representation to identify observable and unobservable states by simple inspection of the A and C matrices of the transformed model.|
|CD Similar State-Space Models||Compares two state-space models and determines if the models are equal (with the same A, B, C, and D values) or similar. |
Two state-space models are similar if you can use a similarity transformation matrix to transform the states of one model into the states of the other model linearly and within a certain tolerance.
|CD State Similarity Transform||Applies a similarity transformation on State-Space Model using the given Transformation Matrix (T).|