What is the ctrlsys library used for?

ctrlsys is a high-performance C11 library with Python bindings used for control system synthesis, analysis, and identification, including solving Riccati and Lyapunov equations.

Why does the skill recommend using .copy() on arrays?

Many ctrlsys routines are optimized to modify input arrays in-place to save memory; using .copy() ensures your original data matrices are preserved.

Does this skill support discrete-time systems?

Yes, many ctrlsys routines support both continuous and discrete-time systems through the DICO parameter (e.g., DICO='D' for discrete).

Can this skill help with subspace system identification?

Yes, it provides specific guidance on using the IB prefix routines, such as ib01ad for preprocessing and ib01bd for estimating system matrices from data.

Which routine should I use for LQR design?

For continuous-time systems, use the sb02md routine to solve the Algebraic Riccati Equation (ARE) and determine the optimal gain.

ctrlsys Control Theory & Systems Engineering

Name: ctrlsys Control Theory & Systems Engineering
Author: jamestjsp

byjamestjsp

0•

Data Science & ML

Provides expert guidance on control system design, analysis, and identification using the ctrlsys library.

This skill empowers Claude with deep knowledge of the ctrlsys C11 library and its Python bindings, specialized for control theory applications. It bridges the gap between high-level engineering requirements—such as LQR/LQG synthesis, H-infinity design, and Kalman filtering—and the specific, optimized routines required to solve them. By providing naming conventions, task-to-routine mapping, and critical implementation warnings regarding Fortran-order arrays and in-place modifications, it ensures robust development of control systems and mathematical models.

Key Features

01Expert guidance on system identification using MOESP and N4SID subspace methods

02Step-by-step workflows for model reduction and balanced truncation

03Comprehensive guides for system analysis including controllability and observability

04Best practices for handling Fortran column-major arrays and in-place data safety

050 GitHub stars

06Task-to-routine mapping for LQR, LQG, and H-infinity controller design

Use Cases

01Designing and implementing optimal controllers for complex industrial dynamical systems

02Performing model order reduction on high-dimensional state-space systems for real-time simulation

03Identifying state-space matrices (A, B, C, D) from experimental sensor data

Key Features

01Expert guidance on system identification using MOESP and N4SID subspace methods

02Step-by-step workflows for model reduction and balanced truncation

03Comprehensive guides for system analysis including controllability and observability

04Best practices for handling Fortran column-major arrays and in-place data safety

050 GitHub stars

06Task-to-routine mapping for LQR, LQG, and H-infinity controller design

Use Cases

01Designing and implementing optimal controllers for complex industrial dynamical systems

02Performing model order reduction on high-dimensional state-space systems for real-time simulation

03Identifying state-space matrices (A, B, C, D) from experimental sensor data