Brake-To-Steer Yaw Control Using Model Predictive Braking
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Solution Overview
Problem
Existing vehicle steering and braking control methods fail to actively reduce yaw error based on the driver's intended yaw movement, especially when traditional methods like pressure tables based on velocity and lateral acceleration are used.
Innovation Solution
A model predictive control (MPC) system determines the brake pressure required to minimize or eliminate yaw error by comparing the driver's intended yaw rate with the actual yaw rate, using a vehicle model and predictive bicycle model to generate precise control actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional pressure tables based on velocity and lateral acceleration are used to determine brake pressures, then the control method is simple to implement, but the yaw error is not actively reduced and the vehicle does not achieve the intended yaw movement
Solution Approach 1:
The patent implements a feedback mechanism where the actual yaw rate is continuously measured and compared with the desired yaw rate to generate a yaw error signal. This error signal is then fed into the MPC controller to adjust brake pressures in real-time, ensuring that the vehicle's actual yaw movement matches the driver's intended yaw movement. This closed-loop feedback approach resolves the contradiction by maintaining high yaw control accuracy while keeping the control logic systematic and implementable.
Solution Approach 2:
The patent uses a predictive model (bicycle model) to anticipate the vehicle's future yaw behavior based on current state variables. The MPC controller proactively adjusts brake pressures before yaw errors fully develop, rather than merely reacting to them. This preliminary action enables precise yaw control while maintaining a structured control approach that is feasible to implement.
2Manufacturing precision
If model predictive control is used to determine brake pressure to minimize yaw error, then the yaw control accuracy is improved, but the computational complexity and system complexity increase
Solution Approach 1:
The patent replaces complex mechanical steering systems with an electronic control system that uses model predictive control. The MPC algorithm, implemented in software/firmware, substitutes for traditional mechanical linkages and hydraulic systems. This substitution achieves precise yaw control through computational methods while reducing the need for complex mechanical components, particularly in brake-to-steer configurations where the steering column is disconnected from the steering rack.
Solution Approach 2:
The patent transforms the control problem from adjusting mechanical steering parameters to optimizing brake pressure parameters. By changing the control variable from steering angle to brake pressure, the system achieves precise yaw control through a different physical domain. The MPC controller continuously optimizes brake pressure parameters based on the predictive model, achieving high accuracy while using a standardized braking system rather than a custom steering system.
3Reliability
If brake-to-steer is used in failed steering systems, then the vehicle can still achieve lateral control, but the yaw rate achieved is less than normal and driver input is limited
Solution Approach 1:
The patent introduces the MPC controller as an intermediary between the driver's steering input and the brake system. The controller translates the driver's intended yaw movement into optimized brake pressure commands, acting as a mediator that maximizes the effectiveness of brake-to-steer control. This intermediary function compensates for the inherent limitations of brake-to-steer by intelligently distributing brake forces to achieve the desired yaw rate, thereby maintaining driver control effectiveness even in failed steering systems.
Solution Approach 2:
The patent changes the control parameter from direct steering angle (which is unavailable in failed systems) to inferred desired yaw rate. The MPC controller uses the driver's steering input to estimate the intended yaw movement, then optimizes brake pressures to achieve this inferred target. This parameter transformation allows the system to maintain full control effectiveness by working with available information rather than being limited by the failed mechanical connection.
Data Source
AI summary
Disclosed is a number of variations that may include a method, system, or computer product useful in determining an intended yaw or yaw rate that a driver desires using a model, comparing the yaw or yaw rate with the actual vehicle yaw or yaw rate to determining a yaw error or yaw rate error, using the yaw error or yaw rate error in a model predictive control to determine the brake pressure required to minimize or reduced to zero the yaw error or the yaw rate error.