Model Predictive Control for Autonomous Vehicle: An In-depth Guide

Fund Fellow Founders Model Predictive Control for Autonomous Vehicle: An In-depth Guide

In this article, JC Chia is addressing the Model Predictive Control (MPC) - a predictive control method used in autonomous navigation control algorithms, which can handle non-linear and complex vehicle dynamics and take multiple constraints to improve efficiency.

MPC computes control actions by minimizing a cost function based on the prediction of the vehicle states over a horizon, relying on vehicle models, references, and the current state. However, MPC is computationally complex and requires accurate models for the system to be controlled.

Summary:

Model Predictive Control (MPC) is an industry standard for autonomous navigation control algorithms.
MPC is a predictive control method that predicts the future states of the vehicle and plans its control actions accordingly.
MPC can handle non-linear and complex vehicle dynamics, allowing precise and accurate trajectory tracking.
MPC can take multiple constraints, leading to improved efficiency compared to other controllers.
MPC can incorporate uncertainties in the vehicle dynamics and environment into the control strategy, ensuring robust and reliable performance in changing conditions.
One of the most significant drawbacks of MPC is computational complexity.
MPC requires a substantial amount of computation to generate optimal control actions, making real-time implementation in embedded systems more challenging.
MPC relies on accurate models for the system to be controlled, which can be difficult to obtain.
MPC computes control actions by minimizing a cost function based on the prediction of the vehicle states from a vehicle model over a horizon.
MPC uses several critical elements, including states, control inputs, vehicle models, references, constraints, cost function, prediction horizon, and sample time.

Full Report: HERE (Medium)

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