## Documentation Center |

Robust Control Toolbox™ software offers several algorithms for model approximation and order reduction. These algorithms let you control the absolute or relative approximation error, and are all based on the Hankel singular values of the system.

Robust control theory quantifies a system uncertainty as either *additive* or *multiplicative *types.
These model
reduction routines are also categorized into two groups: *additive
error* and *multiplicative error* types.
In other words, some model reduction routines produce a reduced-order
model `Gred` of the original model `G` with
a bound on the error
,
the peak gain across frequency. Others produce a reduced-order model
with a bound on the relative error
.

These theoretical bounds are based on the "tails" of the Hankel singular values of the model, i.e.,

(3-1) |

where *σ _{i}* are
denoted the

(3-2) |

where *σ _{i}* are
denoted the

**Top-Level Model Reduction Command**

Method | Description |
---|---|

Main interface to model approximation algorithms |

**Normalized Coprime Balanced Model Reduction
Command**

Method | Description |
---|---|

Normalized coprime balanced truncation |

**Additive Error Model
Reduction Commands **

Method | Description |
---|---|

Square-root balanced model truncation | |

Schur balanced model truncation | |

Hankel minimum degree approximation |

**Multiplicative Error Model Reduction Command**

Method | Description |
---|---|

Balanced stochastic truncation |

**Additional Model Reduction Tools**

Method | Description |
---|---|

Modal realization and truncation | |

Slow and fast state decomposition | |

Stable and antistable state projection |

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