Vehicle Side-Velocity Control for Low-Speed MPC Accuracy
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Solution Overview
Problem
Existing control schemes for vehicles, particularly at low speeds, suffer from reduced accuracy due to the influence of vehicle velocity, especially in Model Predictive Control (MPC) schemes, where velocity affects the accuracy of state estimation and control, leading to increased noise and errors in positioning and heading.
Innovation Solution
A control system that utilizes velocity data from lateral sides of the vehicle, specifically from wheel speeds of each axle, to determine control inputs, incorporating models that account for the movement of each lateral side, enabling improved control by avoiding reliance on vehicle velocity and converting these inputs into actuation commands for steering and longitudinal forces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Model Predictive Control (MPC) scheme is used to control vehicle motion, then control accuracy is improved at high speeds, but control accuracy deteriorates at low speeds due to velocity influence
Solution Approach 1:
The vehicle motion model is segmented into two independent lateral side models (first lateral side and second lateral side), each with its own velocity parameter. This segmentation allows the control system to handle low-speed conditions by treating each side's motion independently rather than relying on overall vehicle velocity, thereby resolving the contradiction between control accuracy and vehicle speed.
Solution Approach 2:
The patent applies local quality by using velocity data specific to each lateral side of the vehicle rather than a single overall vehicle velocity. Each lateral side model uses its own velocity parameter (velocity of first lateral side, velocity of second lateral side), allowing the control accuracy to be maintained locally at each side even when the overall vehicle velocity is low.
2Device complexity
If traditional velocity-based control models are used, then control simplicity is maintained, but control precision deteriorates at low speeds due to noise and errors
Solution Approach 1:
The control model is segmented into two independent lateral side models, each processing velocity data from its respective side. This segmentation improves state estimation accuracy by avoiding the noise and errors that occur in traditional velocity-based models at low speeds, while maintaining reasonable complexity through the use of separate but analogous model structures.
Data Source
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AI summary
Aspects of the present invention relate to a control system comprising one or more controllers, the control system comprising input means (625) to receive state data (444, 554) indicative of a current state of a vehicle and velocity data (446, 556, 558) indicative of a determined respective velocity of each of first and second lateral sides (310, 320) of the vehicle, memory means (617) storing data indicative of a model associated with the vehicle, processing means (615) configured to determine (730) a control input for the vehicle (110, 440, 550, 900) in dependence on the state data (444, 554) and the velocity data utilising a model indicative of movement of the vehicle in dependence on a respective velocity of each of the first and second lateral sides (310, 320) of the vehicle (110, 440, 550, 900), and output means (645) arranged to output one or more signals indicative of the control input for controlling the vehicle (110, 440, 550, 900).