State Controller Feedback Matrix Optimization Under Uncertainty
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Solution Overview
Problem
Existing control systems for highly automated and autonomous vehicles face challenges due to uncertainties in initial states and parameters, leading to time-consuming and costly testing and adjustment phases, especially with shortening development cycles.
Innovation Solution
A computer-implemented method for designing a state controller using stochastic optimization, which involves receiving a state space model and solving an optimization problem to determine the feedback matrix, accounting for uncertainties and reducing verification/validation efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If nominal models are used for controller design, then the controller can be designed with simple models, but uncertainties lead to time-consuming and costly testing and adjustment phases
Solution Approach 1:
The patent applies preliminary action by incorporating uncertainty analysis and stochastic optimization into the controller design phase itself. Instead of dealing with uncertainties during testing and adjustment, the method performs probabilistic model checking and optimization beforehand to generate controllers that are robust against uncertainties from the start, thereby eliminating time-consuming post-design testing iterations
Solution Approach 2:
The patent transforms the controller design from a deterministic parameter selection process to a stochastic optimization process. By changing parameters from fixed nominal values to probability distributions and using probabilistic constraints, the method directly optimizes controller parameters to handle uncertainties, reducing the need for later adjustments
2Reliability
If extensive testing is performed to account for uncertainties, then controller robustness improves, but development cycles lengthen and costs increase
Solution Approach 1:
The patent replaces physical testing and mechanical validation processes with computational probabilistic model checking and stochastic optimization. Instead of building multiple prototypes and testing them extensively, the method uses automated verification algorithms and probabilistic constraints to mathematically guarantee robustness, dramatically accelerating development while maintaining reliability
Solution Approach 2:
The patent enables the controller design process to self-validate through probabilistic constraints and verification algorithms. The optimization framework automatically checks whether proposed controllers satisfy robustness requirements against uncertainties, eliminating the need for external extensive testing and manual adjustment processes
3Loss of time
If stochastic optimization is used in design phase, then verification/validation effort is reduced, but computational complexity increases
Solution Approach 1:
The patent segments the complex verification and validation process into distinct computational components: probabilistic model checking, stochastic optimization, and constraint verification. By dividing the overall task into these manageable segments that can be executed algorithmically, the method reduces manual verification effort while organizing computational complexity into structured, automated steps
Data Source
AI summary
A computer-implemented method for designing a state controller with stochastic optimization. The method includes receiving a state space model for describing a system to be controlled, wherein the state space model comprises a system matrix, a state vector which contains one or more state variables, an input matrix, and an input variable vector, wherein the input variable vector is based on the state vector and a feedback matrix which describes the state controller, and the one or more state variables are described on the basis of one or more probability distributions. The method further includes describing an optimization problem which includes a cost function which is calculated at least using the system matrix, the feedback matrix, an initial state, and the input matrix, and solving the optimization problem in order to determine the entries of the feedback matrix.