82 lines
4.3 KiB
Markdown
82 lines
4.3 KiB
Markdown
# Purpose
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The purpose of this repository/training course is to build a foundational understanding and skills for GNC topics
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including data acquisition and filtering, fusion algorithms, basic system controllers, and then touching on more realistic and
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advanced system controllers.
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# Control Systems Training Outline
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## **Phase 1: Foundations**
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### **Lesson 1: Reading Sensor Data**
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- **Purpose:** Learn to acquire raw accelerometer data from the Pixhawk and visualize it in MATLAB.
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- **Focus:** Connection setup, basic data sampling, and visualization.
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### **Lesson 2: Simple Data Filtering**
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- **Purpose:** Introduce filtering methods (e.g., moving average) to reduce noise and understand the impact of noise on data.
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- **Focus:** Compare raw vs. filtered data and understand why filtering is crucial.
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### **Lesson 3: Gyroscope Integration**
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- **Purpose:** Extend to gyroscope data to understand rotational velocity and its relationship with accelerometer data.
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- **Focus:** Data acquisition and logging for multi-sensor systems.
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### **Lesson 4: Complementary Filter**
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- **Purpose:** Combine accelerometer and gyroscope data to estimate orientation with a simple sensor fusion technique.
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- **Focus:** Practical introduction to basic sensor fusion concepts.
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### **Lesson 5: Introduction to Kalman Filtering**
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- **Purpose:** Implement a basic Kalman filter for 1D data to understand state estimation and uncertainty reduction.
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- **Focus:** Step-by-step breakdown of Kalman filter theory and implementation.
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## **Phase 2: Control Systems**
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### **Lesson 6: Simulink Basics**
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- **Purpose:** Familiarize with Simulink and model simple physical systems like a mass-spring-damper.
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- **Focus:** Basic modeling, simulation setup, and visualization in Simulink.
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### **Lesson 7: Implementing a PID Controller**
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- **Purpose:** Build a PID controller in MATLAB and Simulink to control a simulated system.
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- **Focus:** Understanding PID tuning parameters and their effect on system behavior.
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### **Lesson 8: Pixhawk PID Loop**
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- **Purpose:** Implement a PID controller on the Pixhawk for controlling a simple simulated or real system.
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- **Focus:** Transitioning from simulation to hardware-in-the-loop (HITL).
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## **Phase 3: Advanced Topics**
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### **Lesson 9: Extended Kalman Filter (EKF)**
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- **Purpose:** Implement an EKF for attitude estimation using accelerometer and gyroscope data.
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- **Focus:** Multi-dimensional Kalman filtering and advanced sensor fusion.
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### **Lesson 10: System Identification**
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- **Purpose:** Model a physical system by analyzing input-output data using system identification techniques.
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- **Focus:** Building accurate models for control design.
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### **Lesson 11: Sensor Fusion Algorithms**
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- **Purpose:** Explore and compare advanced sensor fusion algorithms (e.g., Madgwick, Mahony).
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- **Focus:** Implement and evaluate algorithms for orientation estimation.
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### **Lesson 12: Simulating a Control System with HITL**
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- **Purpose:** Integrate sensor fusion and control in a hardware-in-the-loop setup with Pixhawk and Simulink.
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- **Focus:** Bridging simulation and hardware for real-world applications.
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## **Phase 4: Advanced Applications**
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### **Lesson 13: 2D Control Systems**
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- **Purpose:** Simulate a 2D control system (e.g., a planar pendulum) using sensor fusion and PID.
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- **Focus:** Multi-axis control and enhanced visualization.
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### **Lesson 14: Multiple Input Single Output (MISO) Systems**
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- **Purpose:** Model and control systems with multiple inputs and a single output, such as balancing forces from different actuators.
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- **Focus:** Practical handling of MISO systems in MATLAB and Simulink.
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### **Lesson 15: Coupled Systems**
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- **Purpose:** Analyze and control systems with coupled dynamics (e.g., two masses connected by a spring).
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- **Focus:** Advanced modeling and simulation techniques.
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### **Lesson 16: Nonlinear Systems**
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- **Purpose:** Explore the challenges of controlling nonlinear systems, such as systems with friction or saturation.
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- **Focus:** Understanding limitations of linear control methods and introducing nonlinear approaches.
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### **Lesson 17: Model Predictive Control (MPC)**
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- **Purpose:** Introduce model predictive control and its application to advanced control problems.
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- **Focus:** Learn to anticipate future states and optimize control inputs.
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