Course Overview

Control Algorithms - From PID to Model Predictive Control

Course Overview

Control algorithms are the brain behind autonomous systems, robots, drones, and industrial processes. This course takes you from classical PID control to advanced Model Predictive Control (MPC), giving you the tools to design, tune, and implement controllers for real-world applications.

Learning Outcomes

By the end of this course, you will be able to:

✅ Understand control system fundamentals and stability analysis
✅ Design and tune PID controllers for various systems
✅ Implement state-space control methods
✅ Apply Model Predictive Control (MPC) to complex systems
✅ Simulate and validate controllers in Python/MATLAB
✅ Implement control algorithms on real hardware

Prerequisites

Basic calculus and linear algebra
Python programming fundamentals
Understanding of differential equations (helpful)

Course Structure

📚 Chapters

Chapter	Topic	Description
1	Introduction to Control Systems	Open-loop vs closed-loop, stability, transfer functions
2	PID Control Fundamentals	P, PI, PD, PID controllers and their characteristics
3	PID Tuning Methods	Ziegler-Nichols, Cohen-Coon, auto-tuning techniques
4	State-Space Representation	Modern control theory, state variables, controllability
5	Full State Feedback Control	Pole placement, LQR optimal control
6	Model Predictive Control (MPC)	Fundamentals, constraints, optimization
7	Advanced MPC Techniques	Nonlinear MPC, robust MPC, adaptive MPC
8	Real-World Applications	Drone control, robot arms, autonomous vehicles

🧪 Hands-On Labs

Lab	Topic	Skills
1	Control System Simulation Setup	Python, scipy, control library
2	PID Controller Implementation	Discrete PID, anti-windup
3	PID Tuning on a DC Motor	Real-world tuning techniques
4	State-Space Controller Design	Observer design, pole placement
5	LQR Controller Implementation	Optimal control, cost function design
6	MPC for a Linear System	Quadratic programming, constraints
7	MPC for Autonomous Driving	Path tracking, obstacle avoidance
8	Final Project: Drone Altitude Control	Complete control system design

Tools & Software

Python with numpy, scipy, control, cvxpy
MATLAB/Simulink (optional)
Simulation environments (custom or Gazebo)

Control System Diagram

graph LR
    R[Reference] --> S((+))
    S --> C[Controller]
    C --> P[Plant/System]
    P --> Y[Output]
    Y --> M[Sensor]
    M --> S
    
    style C fill:#C9B59C,color:#fff
    style P fill:#A89478,color:#fff

Why Study Control Algorithms?

Control algorithms are essential for:

🤖 Robotics: Motion control, trajectory tracking
🚁 Drones: Stabilization, autonomous flight
🚗 Autonomous Vehicles: Lane keeping, adaptive cruise control
🏭 Industrial Automation: Process control, quality assurance
🎮 Gaming: Physics simulations, AI behavior

Schedule

Week	Content
1-2	Control fundamentals + PID basics
3-4	PID tuning + State-space intro
5-6	State feedback + LQR
7-8	MPC fundamentals + advanced MPC
9-10	Applications + Final project