Balancing a Self Inverting Pendulum on a Cart using a NEAT neural network скачать в хорошем качестве

Balancing a Self Inverting Pendulum on a Cart using a NEAT neural network

The control problem of balancing a pole on a cart, often referred to as the inverted pendulum problem, is a classical problem in control theory and robotics. Here's the background: 🔧 The Problem Setup Imagine a pole hinged to a cart that can move left and right on a track. The pole starts in an upright (unstable) position. The goal is to apply horizontal forces to the cart to keep the pole balanced vertically (i.e., prevent it from falling over). 🧠 Why It's Important Classic Benchmark in Control Theory: The inverted pendulum is a standard benchmark for testing and demonstrating: Feedback control algorithms Stability analysis Real-time control Real-World Analogs: Rockets balancing during launch Segway and self-balancing robots Human posture and walking (biomechanics) Nonlinear, Unstable Dynamics: The system is inherently nonlinear and open-loop unstable — small disturbances grow unless actively corrected. This makes it challenging and interesting. 📘 Historical and Academic Context First studied in the early 20th century in physics and engineering. Formalized in the mid-20th century with the rise of modern control theory. Has been a core teaching example since the development of state-space control, PID control, optimal control (LQR), and modern AI techniques like reinforcement learning. 🧪 Control Techniques Used Linear Control: PID controllers Linear Quadratic Regulator (LQR) Nonlinear Control: Feedback linearization Sliding mode control Modern Approaches: Reinforcement learning Neural network controllers Model Predictive Control (MPC) 🔍 Why It's a Good Learning Tool Simple to model (with two main variables: angle and position) Easy to simulate and build physically Deep insights into the challenges of control system design: Sensing and state estimation Actuator limits and time delays Tradeoffs between stability and performance