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model and controller simplification -凯发k8网页登录

order reduction of plant models and synthesized controllers

complex models are not always required for good control. optimization methods, including methods based on h∞, h2, and µ-synthesis optimal control theory, generally produce controllers with at least as many states as the plant model. model-order reduction commands help you to find less complex low-order approximations to plant and controller models.

functions

model reduction from normalized coprime factorization
simplified access to hankel singular value based model reduction functions
balanced model truncation via square root method
balanced stochastic model truncation (bst) via schur method
hankel minimum degree approximation (mda) without balancing
compute hankel singular values for stable/unstable or continuous/discrete system
modal form realization and projection
balanced model truncation via schur method
reduced order model
slow and fast modes decomposition

topics


  • in the design of robust controllers for complicated systems, model reduction fits several goals.


  • hankel singular values define the energy of each state in the system. model reduction techniques based on hankel singular values can achieve a reduced-order model that preserves important system characteristics.


  • model reduction routines are categorized into two groups, additive error and multiplicative error types.

  • approximate plant model by additive error methods

    reduce a model with balancmr and examine the resulting model error.

  • approximate plant model by multiplicative error method

    reduce a model with bstmr and examine the resulting model error.


  • modreal lets you reduce a model while preserving jω-axis poles.


  • modreal can be the best way to start when reducing large models.


  • compute a reduced-order model by truncating a balanced coprime set of a model.


  • simplify uncertain models built up from uncertain elements to ensure that the internal representation of the model is minimal.

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