Control Systems Training Outline

Phase 1: Foundations

Lesson 1: Reading Sensor Data

• Purpose: Learn to acquire raw accelerometer data from the Pixhawk and visualize it in MATLAB.
• Focus: Connection setup, basic data sampling, and visualization.

Lesson 2: Simple Data Filtering

• Purpose: Introduce filtering methods (e.g., moving average) to reduce noise and understand the impact of noise on data.
• Focus: Compare raw vs. filtered data and understand why filtering is crucial.

Lesson 3: Gyroscope Integration

• Purpose: Extend to gyroscope data to understand rotational velocity and its relationship with accelerometer data.
• Focus: Data acquisition and logging for multi-sensor systems.

Lesson 4: Complementary Filter

• Purpose: Combine accelerometer and gyroscope data to estimate orientation with a simple sensor fusion technique.
• Focus: Practical introduction to basic sensor fusion concepts.

Lesson 5: Introduction to Kalman Filtering

• Purpose: Implement a basic Kalman filter for 1D data to understand state estimation and uncertainty reduction.
• Focus: Step-by-step breakdown of Kalman filter theory and implementation.

Phase 2: Control Systems

Lesson 6: Simulink Basics

• Purpose: Familiarize with Simulink and model simple physical systems like a mass-spring-damper.
• Focus: Basic modeling, simulation setup, and visualization in Simulink.

Lesson 7: Implementing a PID Controller

• Purpose: Build a PID controller in MATLAB and Simulink to control a simulated system.
• Focus: Understanding PID tuning parameters and their effect on system behavior.

Lesson 8: Pixhawk PID Loop

• Purpose: Implement a PID controller on the Pixhawk for controlling a simple simulated or real system.
• Focus: Transitioning from simulation to hardware-in-the-loop (HITL).

Phase 3: Advanced Topics

Lesson 9: Extended Kalman Filter (EKF)

• Purpose: Implement an EKF for attitude estimation using accelerometer and gyroscope data.
• Focus: Multi-dimensional Kalman filtering and advanced sensor fusion.

Lesson 10: System Identification

• Purpose: Model a physical system by analyzing input-output data using system identification techniques.
• Focus: Building accurate models for control design.

Lesson 11: Sensor Fusion Algorithms

• Purpose: Explore and compare advanced sensor fusion algorithms (e.g., Madgwick, Mahony).
• Focus: Implement and evaluate algorithms for orientation estimation.

Lesson 12: Simulating a Control System with HITL

• Purpose: Integrate sensor fusion and control in a hardware-in-the-loop setup with Pixhawk and Simulink.
• Focus: Bridging simulation and hardware for real-world applications.

Phase 4: Advanced Applications

Lesson 13: 2D Control Systems

• Purpose: Simulate a 2D control system (e.g., a planar pendulum) using sensor fusion and PID.
• Focus: Multi-axis control and enhanced visualization.

Lesson 14: Multiple Input Single Output (MISO) Systems

• Purpose: Model and control systems with multiple inputs and a single output, such as balancing forces from different actuators.
• Focus: Practical handling of MISO systems in MATLAB and Simulink.

Lesson 15: Coupled Systems

• Purpose: Analyze and control systems with coupled dynamics (e.g., two masses connected by a spring).
• Focus: Advanced modeling and simulation techniques.

Lesson 16: Nonlinear Systems

• Purpose: Explore the challenges of controlling nonlinear systems, such as systems with friction or saturation.
• Focus: Understanding limitations of linear control methods and introducing nonlinear approaches.

Lesson 17: Model Predictive Control (MPC)

• Purpose: Introduce model predictive control and its application to advanced control problems.
• Focus: Learn to anticipate future states and optimize control inputs.