Modeling + state space systems + PID + Model Predictive Control + Python simulation: lateral control for autonomous cars
InstructorMark Misin Engineering
Coupon
The curriculum provides deep mathematical exploration of MPC cost functions and the gradients of quadratic vector-matrix forms.
Learners apply control principles to diverse physical models, including water tanks and magnetic train systems via Python.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide illustrates Model Predictive Control by displaying Y-distance trajectories, steering angles, and yaw dynamics across multiple synchronized graphs.
This block diagram illustrates a feedback control loop featuring a controller and a plant system that regulates volume based on mass flow rate.
A top-down 3D simulation view illustrating an autonomous vehicle executing a lane-changing maneuver along a red dashed path on a multi-lane road.
This simulation dashboard visualizes system dynamics through a primary spatial plot alongside time-series charts for displacement, velocity, acceleration, and horizontal error.
The focus on mathematical depth may feel excessive for those seeking a more practical, tool-oriented approach to PID control.
One signal suggests the course lacks a clear roadmap or high-quality visual animations to aid understanding of motion dynamics; notably, this feedback predates the displayed update date, so it is uncertain if recent changes have addressed these structural concerns.
The content aligns with the requirements of engineering students through calculus and vector-matrix prerequisites.