Nonlinear Vehicle Control Policy Identification From Sensor Motion Data
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Solution Overview
Problem
Existing technologies lack effective methods for identifying nonlinear control policies governing the movement of groups of vehicles, such as enemy military vehicles, which is crucial for determining appropriate action plans.
Innovation Solution
A computing device accesses sensor data from various sensors observing vehicles, generates historical time and velocity data, and determines a nonlinear control policy as a weighted combination of predefined policies by minimizing a residual error term.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear control methods are used to analyze vehicle group movements, then the analysis process is simple and computationally efficient, but the methods fail to accurately capture and identify nonlinear control policies that actually govern the vehicles
Solution Approach 1:
The patent transforms the control policy identification problem by changing the mathematical parameters from linear to nonlinear models. It uses nonlinear regression analysis and polynomial fitting to capture complex vehicle group behaviors that linear methods cannot represent, thereby improving measurement precision of control policy identification.
Solution Approach 2:
The patent replaces traditional mechanical control analysis methods with computational intelligence approaches including neural networks and genetic algorithms. This substitution enables the system to handle nonlinear dynamics and identify complex control policies that were previously intractable with conventional mechanical control theories.
2Measurement precision
If complex nonlinear analysis methods are employed to accurately identify control policies, then the identification accuracy improves, but the computational time and processing requirements increase significantly
Solution Approach 1:
The patent performs preliminary data preprocessing and feature extraction before applying complex nonlinear analysis. It pre-processes sensor data to extract relevant motion parameters and characteristics, which reduces the dimensionality of the problem and accelerates subsequent computational steps without sacrificing identification accuracy.
Solution Approach 2:
The patent implements adaptive computational methods that dynamically adjust analysis depth based on data characteristics. It uses iterative refinement approaches where the computational effort is optimized by stopping when convergence criteria are met, thereby reducing unnecessary computational time while maintaining high precision in control policy identification.
Data Source
AI summary
A computer generates historical time and velocity data for vehicles based on data from sensor(s) observing the vehicles. The computer determines, based on the historical time and velocity data, a control policy that controls movement of the vehicles. The control policy is represented as a weighted combination of a set of predefined policies. Determining the control policy comprises calculating weights or parameters for a weighted combination of the set of predefined policies that minimizes a residual error term. The residual error term is computed based on a difference between the historical time and velocity data and predicted time and velocity data associated with the weighted combination of the set of predefined policies. The computer determines an action plan based on the determined nonlinear control policy. The computer transmits, to a machine, a control signal causing the machine to perform or simulate at least a part of the determined action plan.