Python bindings for SLICOT - Subroutine Library in Control Theory

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  • License Expression: BSD-3-Clause
    SPDX License Expression
  • Author: SLICOT Contributors
  • Tags control , systems , linear , lapack , blas , riccati , lyapunov
  • Requires: Python >=3.11
  • Provides-Extra: test
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Project description

SLICOT

PyPI version Build Status License: BSD-3-Clause

Python bindings for SLICOT (Subroutine Library In COntrol Theory) - numerical routines for control systems analysis and design.

Installation

pip install slicot

Features

  • 600+ routines for control systems
  • State-space methods: Riccati, Lyapunov, pole placement
  • Model reduction: Balance & Truncate, Hankel-norm
  • System identification: MOESP, N4SID
  • NumPy integration: Column-major arrays

Quick Start

import numpy as np
import slicot

# Controllability analysis
A = np.array([[1, 2], [3, 4]], order='F')
B = np.array([[1], [0]], order='F')

a_out, b_out, ncont, z, tau, info = slicot.ab01md('I', A, B.flatten(), 0.0)
print(f"Controllable dimension: {ncont}")

Column-Major Arrays

SLICOT uses Fortran conventions:

A = np.array([[1, 2], [3, 4]], order='F')  # Required!

License

BSD-3-Clause. See LICENSE.

Project details

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  • License Expression: BSD-3-Clause
    SPDX License Expression
  • Author: SLICOT Contributors
  • Tags control , systems , linear , lapack , blas , riccati , lyapunov
  • Requires: Python >=3.11
  • Provides-Extra: test
Classifiers

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2.0.0

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This version

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