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Control Engineering · Course outline

From feedback fundamentals to a complete control design.

20 modules and 60 lessons in 4 stages. Every lesson has real Python practice that runs in your browser and a short quiz. The course is free and open to everyone. For engineering students and curious builders. Begin with basic algebra and Python variables, functions, and loops. Calculus, complex numbers, and matrices are introduced as they are used. Use the Python course foundations if programming is new to you.

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20 modules60 lessons20 guided practicals6 projects48 lesson hours
Before you begin: prerequisites, tools, and learning plan

For engineering students and curious builders. Begin with basic algebra and Python variables, functions, and loops. Calculus, complex numbers, and matrices are introduced as they are used. Use the Python course foundations if programming is new to you.

Plan about 3 to 5 hours each week. Lesson estimates include the guided practicals; project time is additional. Each lesson has a runnable example, a checked exercise, two quiz questions, and a downloadable notebook. Complete the practice and quiz to mark a lesson done.

Use Python, NumPy, SciPy, and Matplotlib in the browser, or install them in a local Jupyter environment. Project notebooks also use pandas. The first browser run downloads Python and the required packages. No MATLAB licence, paid service, or hardware is needed.

All required practical sessions use simulations in your browser. Optional physical work belongs on an approved low-voltage trainer with qualified supervision and independent protection.

Review Python foundations

Foundations

Modules 01 to 05

MODULE 01

Understand feedback

Identify the signals in a control loop and run your first feedback experiment.

  1. Signals, units, and the feedback loop
  2. Open loop and closed loop
  3. Practical: regulate a motor speed
3 lessons2h 25m
MODULE 02

Build the mathematical toolkit

Use exponentials, complex numbers, integration, and numerical error.

  1. Exponentials and complex poles
  2. Derivatives, integrals, and sampled data
  3. Practical: test your numerical solver
3 lessons2h 25m
MODULE 03

Model physical systems

Translate force, voltage, heat, and flow balances into dynamic models.

  1. Mechanical and electrical balances
  2. Thermal and liquid level models
  3. Practical: identify a first order plant
3 lessons2h 25m
MODULE 04

Use transfer functions

Work with Laplace models, block diagrams, and disturbance paths.

  1. From differential equation to transfer function
  2. Combine blocks and close the loop
  3. Practical: trace a disturbance
3 lessons2h 25m
MODULE 05

Measure time response

Turn rise time, overshoot, settling, and error into measurable requirements.

  1. First order response specifications
  2. Second order damping and overshoot
  3. Practical: build a response scorecard
3 lessons2h 25m

Classical control

Modules 06 to 10

MODULE 06

Analyse stability

Use pole locations and Routh tests to find stable gain ranges.

  1. Poles and internal stability
  2. Routh stability without solving roots
  3. Practical: find the stability boundary
3 lessons2h 25m
MODULE 07

Reduce steady error

Distinguish system type, tracking error, disturbances, and integral action.

  1. System type and the final value theorem
  2. Integral action and load rejection
  3. Practical: reject a load step
3 lessons2h 25m
MODULE 08

Design practical PID control

Tune PI and PID with noise, saturation, and anti-windup in mind.

  1. Tune proportional and integral terms
  2. Filter the derivative and prevent windup
  3. Practical: compare saturation recovery
3 lessons2h 25m
MODULE 09

Design with root locus

Follow closed-loop poles as gain changes and choose damping deliberately.

  1. Root locus rules and gain
  2. Choose gain from a damping target
  3. Practical: map gain to performance
3 lessons2h 25m
MODULE 10

Design in the frequency domain

Read Bode plots, reason about Nyquist, and add phase lead.

  1. Frequency response and Bode plots
  2. Margins, Nyquist, and delay
  3. Practical: add phase lead
3 lessons2h 25m

Digital & state space

Modules 11 to 15

MODULE 11

Sample and discretise

Understand aliasing, sample-and-hold behaviour, and digital implementation.

  1. Sampling, aliasing, and sensor filters
  2. Zero-order hold models
  3. Practical: choose a controller sample period
3 lessons2h 25m
MODULE 12

Implement discrete controllers

Use difference equations and test delay, quantisation, and timing.

  1. The z domain and difference equations
  2. Incremental PI and quantisation
  3. Practical: test a one-sample delay
3 lessons2h 25m
MODULE 13

Control with state space

Build matrix models, test controllability, and place feedback poles.

  1. States, inputs, and outputs
  2. Controllability and pole placement
  3. Practical: build a position servo
3 lessons2h 25m
MODULE 14

Estimate hidden states

Test observability and build an observer for an unmeasured velocity.

  1. Observability and sensor choice
  2. Luenberger observer dynamics
  3. Practical: observe velocity from position
3 lessons2h 25m
MODULE 15

Optimise control and estimation

Introduce LQR, Kalman filtering, and their model assumptions.

  1. Linear quadratic regulation
  2. Kalman prediction and correction
  3. Practical: combine a controller and estimator
3 lessons2h 25m

Applied control

Modules 16 to 20

MODULE 16

Identify and validate a plant

Estimate models from experiments and test them on unseen input sequences.

  1. Fit a discrete model with least squares
  2. Excitation, residuals, and validation
  3. Practical: identify, validate, then control
3 lessons2h 25m
MODULE 17

Handle nonlinear behaviour

Linearise around an operating point and examine where local models fail.

  1. Equilibria and local linearisation
  2. Lyapunov reasoning and nonlinear limits
  3. Practical: schedule gains across tank levels
3 lessons2h 25m
MODULE 18

Design for uncertainty and interaction

Use sensitivity, uncertainty sweeps, cascade loops, and simple decoupling.

  1. Sensitivity and robustness tradeoffs
  2. Cascade control and interacting loops
  3. Practical: stress-test uncertain plants
3 lessons2h 25m
MODULE 19

Plan constrained control

Introduce predictive control, real-time logic, and explicit fallback behaviour.

  1. Finite-horizon predictive control
  2. Real-time control logic and fault handling
  3. Practical: implement a constrained horizon
3 lessons2h 25m
MODULE 20

Complete an engineering design

Turn requirements, experiments, and reproducible evidence into a defensible project.

  1. Write requirements and acceptance tests
  2. Reproducible experiments and supervised bench work
  3. Practical: defend a complete controller design
3 lessons2h 25m
ABOUT THE LEARNING LAB

Free, open and run by a nonprofit.

This page is part of Control Engineering, a free course of 20 modules and 60 lessons in the engineering.fit( ) Learning Lab. The Learning Lab is built and run by engineering.fit( ), a nonprofit founded by Abdullateef Opeyemi Bakare that helps African engineering students gain research experience. You do not need an account or pay a fee to learn.

Course outline · Project lab · Visit engineering-fit.org