Vehicle Neural Network Control With Nonlinear H-Infinity Robustness
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Solution Overview
Problem
Existing vehicle control systems face challenges in achieving robustness guarantees while maintaining high performance, particularly in non-linear systems, with robust control methods ensuring stability but lacking in performance, and neural-network based control methods offering high performance but lacking robustness.
Innovation Solution
Implementing neural network control policies with non-linear H-infinity robustness guarantees, which involve modeling nonlinear dynamics using polynomial equations, solving Hamilton Jacobi Inequality, and projecting neural network outputs onto a defined set of allowable robust actions, ensuring both robustness and performance through a tunable robustness parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If robust control methods are applied to ensure system stability under disturbances, then stability is improved, but performance deteriorates when applied to non-linear systems
Solution Approach 1:
The patent merges robust control methods with neural network-based control by formulating a combined optimization framework. The robust control component ensures stability under disturbances while the neural network component handles non-linear tasks with high performance, resolving the contradiction between stability and performance in non-linear systems.
Solution Approach 2:
The patent introduces a tunable robustness parameter that allows dynamic adjustment of the trade-off between robustness and performance. By changing this parameter, the system can adapt to different operating conditions, achieving both stability guarantees and high performance on challenging non-linear tasks.
2Productivity
If neural-network based control methods are used for high performance on non-linear tasks, then performance is improved, but robustness deteriorates due to lack of robustness guarantees
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously monitors performance and adjusts control actions to maintain both high performance and robustness guarantees. The neural network policy is trained with feedback from both performance metrics and robustness constraints, ensuring reliable operation under disturbances.
3Adaptability or versatility
If a tunable robustness parameter is introduced to enable trade-off between robustness and performance, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent makes the robustness parameter dynamic and tunable, allowing the system to adapt to different operating conditions. This dynamic adjustment capability enables the system to optimize the trade-off between robustness and performance in real-time, improving adaptability while managing complexity through structured control architecture.
Data Source
AI summary
An example method of generating a neural network control policy for a vehicle includes obtaining a performance objective parameter associated with a control system of a vehicle, obtaining a policy optimizing algorithm corresponding to the performance objective parameter, defining a system state space, a control action space, and a disturbance space, each associated with the control system, generating a neural network control policy, based on the system state space, the control action space, and the disturbance space, wherein the neural network control policy has a non-linear H-infinity robustness guarantee, and automatically controlling at least one vehicle component according to the neural network control policy.