This content originally appeared on DEV Community and was authored by Tawhid
If you’re a control engineer looking to level up your coding game, Python is one of the best tools you can have in your toolbox. Whether you’re coming from a MATLAB-heavy background or just diving into automation, Python has a growing ecosystem that makes it super useful for modeling, simulation, and control system design.
And let’s be real, not everything needs a Simulink block. Sometimes a few lines of Python will do the job faster and cleaner. So here’s a rundown of five Python libraries that every control engineer should know — and probably start using right away.
1. control
This is the bread and butter for anyone doing classical control work in Python. The control library is basically the Python version of MATLAB’s Control Systems Toolbox.
You can model systems using transfer functions or state-space
Run time-domain and frequency-domain analysis
Simulate step, impulse, and custom input responses
Design controllers with root locus, Bode plots, Nyquist, etc.
Here’s a taste of what it looks like:
import control as ctrl
sys = ctrl.tf([1], [1, 2, 1]) # Simple second-order system
ctrl.step_response(sys)
If you’re transitioning from MATLAB, this library will feel super familiar.
2. scipy.signal
You’ve probably heard of SciPy if you’ve touched Python at all. But for control engineers, scipy.signal is gold. It’s great for:
- Signal processing
- Filtering
- Discrete system modeling
- Transfer function and state-space representations
- LTI (Linear Time-Invariant) systems
It doesn’t have everything that control has, but it’s tightly integrated with the rest of SciPy and NumPy, which makes it great for hybrid workflows.
from scipy import signal
system = signal.TransferFunction([1], [1, 3, 2])
t, y = signal.step(system)
Also, it’s very efficient and great for custom filter design and system identification.
3. matplotlib
Yeah, this one’s more general, but let’s be honest — if you can’t plot your system response, how do you know what’s going on?
matplotlib is Python’s standard plotting library and you’ll use it constantly to visualize:
- Step responses
- Bode plots
- Nyquist diagrams
- Root locus (you’ll need to code this or use helper libraries)
import matplotlib.pyplot as plt
plt.plot(t, y)
plt.title('Step Response')
plt.xlabel('Time (s)')
plt.ylabel('Output')
plt.grid(True)
If you’re a visual thinker, learning to wield matplotlib well will make your life easier. It’s super customizable once you get the hang of it.
4. numpy
Control systems are built on math, and numpy is the math engine of Python. Whether you’re handling matrices, doing eigenvalue calculations, or solving linear algebra problems, numpy is essential.
- Matrix multiplication
- Array manipulation
- Linear equations
- Eigenvalues and eigenvectors
- Numerical stability checks
Any time you’re building a state-space model or solving system equations, numpy is behind the scenes doing the heavy lifting.
import numpy as np
A = np.array([[0, 1], [-2, -3]])
eigvals, eigvecs = np.linalg.eig(A)
It’s fast, reliable, and the syntax becomes second nature pretty quickly.
5. sympy
This one’s a little different. sympy is for symbolic computation, kind of like what you’d use in MATLAB’s Symbolic Toolbox. It lets you work with equations algebraically instead of numerically.
- Derive transfer functions symbolically
- Simplify control laws
- Solve differential equations by hand (well, almost)
- Perform Laplace transforms, inverse Laplace, etc.
import sympy as sp
s = sp.symbols('s')
G = 1 / (s**2 + 3*s + 2)
It’s not a daily driver like numpy or scipy, but when you’re designing controllers from scratch or analyzing systems on paper, sympy is a great tool to have.
Final Thoughts
If you’re doing control work in Python, these five libraries will cover 90% of what you need. They’re powerful, well-documented, and play nicely with each other.
The best part? They’re all free and open-source, so you don’t have to worry about license servers going down five minutes before a deadline.
Start small. Build a simple system. Plot a response. Tune a controller. Once you get rolling, you’ll realize that Python isn’t just a cheap MATLAB alternative, it’s a full-on engineering powerhouse.
Let me know if you want a walkthrough of building a control loop using these libraries from start to finish. Happy coding.
This content originally appeared on DEV Community and was authored by Tawhid