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.
- 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)
Might be useful:
Please note, none of the content belongs to fff.vc, this is simply a preview to an original source of data! You can find all the linked URLs blow.