commit cc7b47b2ecd9ce50ddfe37db0d60ca95ddfbc85d
Author: judsonupchurch <judson.upchurch@gmail.com>
Date:   Sun Jan 5 15:46:07 2025 -0600

    Readme filled with brief lesson plan

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+# 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.