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Free Download Vehicle Suspension Control 4 PID tuning with AI - TD3
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.01 GB | Duration: 2h 19m
Master PID Tuning for Vehicle Suspension Control with State-of-the-Art TD3 Reinforcement Learning Algorithm in Python
What you'll learn
Get intuition in Backpropagation in Neural Networks
Apply Q-learning in Python in a simple grid-world.
Apply TD3 Reinforcement algorithm to suspension control
Get familiar with the PyTorch library
Requirements
Basics in Python
Description
Dive into PID tuning with AI using TD3 (Twin Delayed Deep Deterministic Policy Gradient) for vehicle suspension control! This hands-on course equips control systems engineers, robotics enthusiasts, and Python developers with practical skills to optimize half-car suspension models through reinforcement learning (RL).Start with the fundamentals: implement simple Q-learning in a grid world environment using Python and NumPy. You'll code agents that learn optimal policies step-by-step, building intuition for RL basics like value functions.Then, advance to real-world applications. Apply TD3, a state-of-the-art actor-critic algorithm, to tune PID controllers for a dynamic half-car model. Simulate vehicle suspension dynamics-handling bounce, pitch, and road disturbances. Line-by-line code explanations reveal how TD3 reduces overshoot and settling time in active suspension systems.Perfect for automotive engineering and PyTorch RL practitioners. No prior RL experience needed-just basic Python and control theory.What you'll get:Complete, runnable Python code for Q-learning and TD3-PID tuningHalf-car model simulations with visualizationsTips for deploying RL-tuned controllers in real-time systemsBoost your resume with AI-driven control systems expertise. Enroll now and transform theory into tunable, high-performance suspensions.In addition, we will apply a simple backpropagation algorithm to manually tune a small neural network. It will give you necessary intuition in how neural nets are structured, how they are trained, and eventually, how they are deployed.
Engineering students,Engineering professionals
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