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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-control-systems-2-autonomous-cars-360-tracking/
课程评论：没有评论
**课程信息** * **课程名称：** 应用控制系统 2：自动驾驶汽车（360度追踪） * **讲师：** Mark (航空航天与机器人工程师) * **课程概述：** 本课程是“应用控制系统 1：自动驾驶汽车：数学 + PID + MPC”的直接延续。在第一门课程中，我们使用线性化方法（小角度近似）将非线性模型转换为线性时不变（LTI）模型，这简化了模型预测控制（MPC）算法的应用，但限制了其功能。 在本课程中，我们将移除此简化，并展示如何通过将非线性系统转化为线性参数变（LPV）形式，来应用线性 MPC 控制器，从而使自动驾驶汽车能够追踪任意二维轨迹。 此外，课程还将讲解如何利用二次规划求解器（如 qpsolvers & quadprog）来为自动驾驶汽车应用 MPC 约束，以确保车辆的速度、加速度和转向角保持在实际允许的范围内。 * **课程亮点：** * 实现自动驾驶汽车在二维平面上追踪任意轨迹。 * 理解 LPV（线性参数变）方法如何将非线性系统应用于线性 MPC 控制。 * 学习使用二次规划求解器处理控制问题中的约束。 * 掌握的控制系统知识具有普遍适用性，可用于多种工程系统。 * **先修课程：** 应用控制系统 1：自动驾驶汽车：数学 + PID + MPC * **授课语言：** 英语 (提供免费预览视频)
How do you make autonomous cars track a general trajectory on a 2D plane and how do you make sure that the velocities, accelerations and steering wheel angles of the autonomous cars stay within their realistic minimum and maximum values?My name is Mark. I'm an Aerospace & Robotics Engineer and in this course, I will give you intuition, Mathematics and Python implementation for all that.This course is a direct continuation to the course "Applied Control Systems 1: autonomous cars: Math + PID + MPC. In the previous course, the Model Predictive Control (MPC) algorithm only allowed the autonomous cars to change lanes on a straight road. We applied a small angle approximation to convert our nonlinear model to linear time invariant (LTI). It made our lives easier but it also restricted our Model Predictive Control algorithm.In this course however, we will remove that simplification and I will show you how you can apply a linear MPC controller to a nonlinear system by putting it in a Linear Parameter Varying form first. With this highly popular technique, your car will be able to track a general 2D trajectory.In addition, you will learn how to use quadratic solvers such as qpsolvers & quadprog to apply MPC constraints to autonomous cars. In most control problems, you have to consider constraints in order to keep your system within reasonable values.The knowledge that you get from this course is universal and can be applied to so many systems in control systems engineering.Take a look at some of my free preview videos and if you like what you see, then ENROLL NOW, and let's get started. Hope to see you inside!
