Vehicle Motion Control Calibration Using Deep-Q Selection
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Solution Overview
Problem
Current vehicle motion control systems face high computational burdens and tuning complexity due to numerous control parameters and changing vehicle dynamics over time, requiring improved real-time control selection and calibration methods that reduce resource usage, enhance reliability, and mitigate component deterioration.
Innovation Solution
A system utilizing a deep-Q network (DQN) with sensors and actuators to measure and alter vehicle dynamics, generating a vehicle dynamical context through recurrent convolutional neural networks (RCNN) and MPC modules, which selects appropriate calibrations based on performance indices and rewards to optimize actuator commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model predictive control systems are used to manage vehicle motion control, then vehicle performance in longitudinal and lateral acceleration, pitch, roll, and yaw can be controlled, but computational burden increases significantly
Solution Approach 1:
The control system is segmented into multiple independent MPC controllers, each optimized for specific driving conditions (e.g., acceleration, braking, cornering). The system divides the complex control problem into manageable segments that can be evaluated and selected based on current vehicle state, reducing the computational burden of evaluating all parameters simultaneously while maintaining comprehensive motion control capability.
Solution Approach 2:
Multiple MPC controllers are pre-configured with different control strategies and parameters before runtime. During operation, the system selects from these pre-prepared controllers based on current conditions rather than computing optimal control parameters in real-time, significantly reducing computational burden while maintaining control reliability.
2Adaptability or versatility
If the number of actuators is increased to enhance vehicle control capabilities, then system functionality is improved, but tuning complexity increases significantly
Solution Approach 1:
Each MPC controller is tuned for specific local operating conditions and actuator configurations. Instead of creating a single complex controller that must handle all actuator combinations, the system uses multiple specialized controllers, each optimized for particular actuator states and vehicle dynamics, thereby reducing overall tuning complexity while maintaining versatility.
Solution Approach 2:
The modular MPC controller architecture provides universality by enabling the same control framework to handle multiple actuators and driving conditions through a standardized selection mechanism. The system can adapt to different actuator configurations and vehicle dynamics by selecting appropriate pre-tuned controllers rather than requiring separate tuning for each scenario.
3Measurement precision
If control parameters are tuned to optimize vehicle performance, then control precision is improved, but tuning process becomes labor intensive
Solution Approach 1:
Multiple MPC controllers are pre-tuned offline for various driving conditions and vehicle states. This preliminary tuning action eliminates the need for time-consuming real-time parameter adjustment, as the system simply selects from pre-optimized controllers during operation, maintaining control precision while dramatically reducing tuning time.
Solution Approach 2:
Instead of manually tuning each control parameter for every scenario, the system creates copies of the MPC controller framework with pre-configured parameters for different conditions. These copied controllers inherit the optimized structure but have condition-specific parameters pre-set, reducing the labor-intensive nature of parameter tuning while maintaining precision.
4Adaptability or versatility
If multiple MPC controllers are used to handle different use cases, then system adaptability is improved, but computational resources are consumed
Solution Approach 1:
The system segments the control space into distinct operating regions (e.g., acceleration, deceleration, cornering, straight-line driving). Each MPC controller is responsible for a specific segment, and the system only activates the relevant controller for current conditions. This segmentation maintains adaptability across different use cases while reducing computational resource consumption by avoiding simultaneous evaluation of all controllers.
Solution Approach 2:
The system performs preliminary classification of driving conditions to determine which MPC controller is appropriate before executing control calculations. This preliminary action enables the system to select from multiple controllers without computing all of them, maintaining adaptability while conserving computational resources by activating only the necessary controller.
Data Source
AI summary
A system for real-time control selection and calibration in a vehicle using a deep-Q network (DQN) includes sensors and actuators disposed on the vehicle. A control module has a processor, memory, and input/output (I/O) ports in communication with the one or more sensors and the one or more actuators. The processor executes program code portions that cause the sensors actuators to obtain vehicle dynamics and road surface estimation information and utilize the vehicle dynamics information and road surface estimation information to generate a vehicle dynamical context. The system decides which one of a plurality of predefined calibrations is appropriate for the vehicle dynamical context, generates a command to the actuators based on a selected calibration. The system continuously and recursively causes the program code portions to execute while the vehicle is being operated.