状态空间模型

LTI 模型的状态空间表示

状态空间中模型的表示不是唯一的。坐标变换会生成包含的矩阵不同但动态特性相同的状态空间模型。要获得状态空间模型的最小实现，或转换分析和控制设计的标准形式，可以使用状态坐标变换。

坐标变换对于缩放病态模型也很有用。状态空间模型的正确缩放对于精确计算至关重要。例如，如果一个动态系统的状态向量中有两个状态，其单位分别为光年和毫米，则模型的缩放效果就会很差。这种相差极大的单位可能会将非常大和非常小的条目引入 A 矩阵中。在计算过程中，矩阵中大小条目混合使用可能会破坏模型的重要特性，并导致不正确的结果。

函数

`balreal`	Balanced state-space realization
`prescale`	Optimal scaling of state-space models
`modalreal`	Compute modal state-space realization (自 R2023b 起)
`compreal`	Compute companion state-space realization (自 R2023b 起)
`ss2ss`	State coordinate transformation for state-space model
`ssequiv`	Equivalence transformation for state-space models (自 R2023b 起)
`xperm`	Reorder states in state-space models
`xsort`	Sort states based on state partition (自 R2020b 起)
`xelim`	Eliminate states from state-space models (自 R2023b 起)
`augstate`	Append state vector to output vector
`ctrb`	状态空间模型的可控性
`obsv`	Observability of state-space model
`gram`	Controllability and observability Gramians
`augoffset`	Map offset contribution to extra input channel (自 R2024a 起)
`dss2ss`	Convert descriptor state-space model to explicit form (自 R2024a 起)
`fixInput`	Fix value of some inputs and delete them (自 R2024a 起)

主题

State-Space Realizations
A state-space model can be expressed in an infinite number of realizations. Common forms, sometimes called canonical forms, include modal, companion, observable, and controllable forms.
Scaling State-Space Models
When working with state-space models, proper scaling is important for accurate computations.
Scaling State-Space Models to Maximize Accuracy
This example shows that proper scaling of state-space models can be critical for accuracy and provides an overview of automatic and manual rescaling tools.
Use Linearization Offsets to Help Compare Nonlinear and Linearized Responses
Use offsets from linearization to facilitate the comparison of the nonlinear and linearized responses of a Simulink model. (自 R2024a 起)

状态空间模型

函数

主题

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