Dual Model Vehicle Control Decoupling Non-Linearities
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Solution Overview
Problem
Model Predictive Control (MPC) schemes face difficulties in processes with non-linearities, such as cross-dependencies between parameters, making it challenging to calculate control inputs effectively for vehicles.
Innovation Solution
A method using two models, a rotation model and a translation model, to predict and decouple vehicle state parameters like heading and position, allowing for the determination of control inputs that minimize a cost function, thereby addressing non-linearities and inter-dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single model is used for MPC control of vehicle parameters, then the control scheme can be implemented, but it fails to address non-linearities and cross-coupling between parameters such as heading and position
Solution Approach 1:
The patent divides the vehicle control model into two separate models: a rotation model for predicting heading parameters and a translation model for predicting position parameters. This segmentation allows each model to handle specific non-linearities independently, improving control accuracy for each parameter while managing overall system complexity through modular structure.
2Productivity
If decoupling of models is implemented by providing predictions of the first parameter as input to the second model, then computational complexity is reduced, but inter-dependence between parameters must be carefully managed
Solution Approach 1:
The patent performs preliminary prediction of the heading parameter using the rotation model before using it as input for the position prediction in the translation model. This preliminary action allows the system to handle parameter inter-dependence in a structured sequence, improving computational efficiency while maintaining reliability by ensuring that dependent parameters are calculated in the correct order.
Data Source
AI summary
Embodiments of the present invention provide a method of controlling a vehicle, comprising predicting a first parameter of a vehicle state at each of a plurality of points in time in dependence on a first parameter of a current vehicle state and a first model associated with the vehicle, predicting a second parameter of the vehicle state at each of the plurality of points in time in dependence on a second parameter of the current vehicle state, the predicted first parameter of the vehicle state and a second model associated with the vehicle, and determining one or more control inputs for the vehicle at each of the points in time in dependence on the predicted first and second parameters of the vehicle state at each of the plurality of points in time and desired first and second parameters of the vehicle state at each of the plurality of points in time.