Semi-Truck Predictive Control Using Dynamic Models and QP
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Solution Overview
Problem
Autonomous driving systems face challenges in accurately predicting the complex movements of semi-trucks due to additional degrees of freedom from trailer attachments, leading to difficulties in generating precise control commands for safe and efficient navigation.
Innovation Solution
An in-vehicle control system that uses a dynamic model based on Lagrangian mechanics and quadratic programming to predict the semi-truck's response to control commands, allowing for real-time generation of optimized steering and acceleration commands to follow a desired trajectory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dynamic model based on Lagrangian mechanics and quadratic programming is used to predict semi-truck movements, then control precision and trajectory following accuracy are improved, but computational complexity and processing time increase
Solution Approach 1:
The system pre-computes and stores the dynamic model parameters and quadratic programming matrices offline before real-time operation. This preliminary preparation allows the online controller to directly apply pre-calculated matrices without performing complex computations in real-time, thus maintaining high prediction accuracy while reducing online computational burden
Solution Approach 2:
The system dynamically updates the dynamic model parameters based on real-time vehicle state measurements and operating conditions. By adapting the model parameters online while using pre-computed structural frameworks, the system maintains accuracy for varying truck configurations without requiring complete re-computation of the control algorithm
2Measurement precision
If a dynamic model based on Lagrangian mechanics and quadratic programming is used to predict semi-truck movements, then control precision and trajectory following accuracy are improved, but real-time processing speed decreases
Solution Approach 1:
The quadratic programming matrices and dynamic model parameters are pre-computed offline and stored for rapid retrieval during real-time control. This allows the system to achieve high control precision by using accurate dynamic models without performing time-consuming matrix operations during the critical real-time control cycle
Solution Approach 2:
The system replaces complex real-time numerical optimization with pre-computed analytical solutions based on Lagrangian mechanics. By substituting general-purpose iterative solvers with dedicated pre-computed control laws, the system achieves both high precision and fast processing speed
Data Source
AI summary
Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.