Model Predictive Chassis and Driveline Control for Tire Force Stability
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Solution Overview
Problem
Current motor vehicle control systems face challenges in maintaining vehicle stability and maximizing tire force generation in complex driving scenarios while minimizing cost, complexity, and calibration efforts, and reducing reliance on control interventions like traction control and stability systems.
Innovation Solution
A system comprising sensors and actuators managed by a control module that processes real-time data to optimize tire force distribution, using inertial measurement units, Semi Active Damping Suspension sensors, GPS, and other sensors to generate control actions that maximize vehicle stability, handling, and steerability, and reduce the need for interventions from systems like traction control and stability control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If model predictive control is used to optimize tire force distribution and vehicle stability, then vehicle stability and handling are improved, but system complexity and computational requirements increase
Solution Approach 1:
The control system is segmented into multiple independent program code portions (first through sixth program code portions) that handle specific tasks separately: obtaining vehicle state information, generating desired dynamic output, estimating actuator actions, generating control action constraints, generating reference control action, and integrating all inputs to generate optimal control action. This modular segmentation reduces overall system complexity while maintaining the sophisticated MPC functionality.
Solution Approach 2:
The system performs preliminary actions by pre-establishing control action constraints based on vehicle state information and estimated actuator actions before generating the final optimal control action. The feed-forward controller also pre-generates reference control actions based on desired transient response characteristics, allowing the integration step to focus on optimizing rather than computing from scratch, thus reducing real-time computational complexity.
2Force
If multiple sensors and actuators are integrated to maximize tire force generation, then force generation capability is improved, but cost and system complexity increase
Solution Approach 1:
The control module serves multiple functions through a single integrated system: it obtains vehicle state information from various sensors, estimates actions of multiple actuators (driveline and chassis), generates control constraints, produces reference control actions, and integrates all inputs to generate optimal control signals. This multi-functional approach consolidates what would otherwise require separate control systems for each function, reducing overall complexity while maximizing tire force generation capability.
Solution Approach 2:
The control module acts as an intermediary that receives inputs from multiple sensors (IMUs, wheel speed sensors, steering position sensors, etc.), processes them through the MPC framework, and coordinates outputs to multiple actuators. This intermediary role unifies the complex interactions between sensors and actuators into a single management point, simplifying the integration architecture while enabling comprehensive control of tire force generation.
3Manufacturing precision
If feed-forward control is used to achieve desired transient response characteristics, then response precision is improved, but system complexity increases
Solution Approach 1:
The system merges feed-forward control and feedback control into a single integrated control module. The feed-forward controller generates reference control actions based on desired transient response characteristics, while the feedback controller (sixth program code portion) integrates these reference actions with actual vehicle state information and control constraints to generate the final optimal control action. This merging allows the system to achieve precise transient response without requiring separate complex control systems.
4Measurement precision
If comprehensive vehicle state monitoring is implemented to enable optimal control, then control accuracy is improved, but cost and complexity increase
Solution Approach 1:
The control module is designed to handle multiple sensor inputs and processing functions within a single unified system. It obtains vehicle state information from various sensors (IMUs measuring orientation and acceleration, wheel speed sensors, steering position sensors, etc.), processes this information through estimation and constraint generation, and uses it for optimal control decision-making. This multi-functional design consolidates comprehensive monitoring capabilities without proportionally increasing system complexity.
Data Source
AI summary
A system for managing chassis and driveline actuators of a motor vehicle includes a control module executing program code portions that: cause sensors to obtain vehicle state information, receive a driver input and generate a desired dynamic output based on the driver input and the vehicle state information, and then estimate actuator actions based on the vehicle state information, generate one or more control action constraints based on the vehicle state information and estimated actuator actions, generate a reference control action based on the vehicle state information, the estimated actions of the one or more actuators and the control action constraints, and integrate the vehicle state information, the estimated actuator actions, desired dynamic output, reference control action and the control action constraints to generate an optimal control action that falls within a range of predefined actuator capacities and ensures driver control of the vehicle.