Simulation-Based Control Parameter Tuning Beyond Local Optima
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Solution Overview
Problem
Existing control systems face challenges in efficiently tuning control parameters, especially for systems with multiple parameters, as exhaustive search algorithms become infeasible and gradient descent algorithms often converge at locally optimal points, preventing thorough exploration of the parameter space.
Innovation Solution
An automated method is introduced to tune control parameters using a simulation system that orders vector control parameters before simulation, employing search algorithms like evolutionary adaptive heuristics to iteratively select and optimize parameter sets based on performance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search algorithm is used to tune control parameters, then all possible parameter combinations can be evaluated, but the computational complexity becomes infeasible for systems with multiple parameters
Solution Approach 1:
The patent segments the high-dimensional parameter search space into multiple lower-dimensional subspaces by grouping related parameters together. This allows the exhaustive search to be performed on smaller subsets of parameters rather than the entire parameter set, significantly reducing computational complexity while maintaining optimization accuracy.
Solution Approach 2:
The patent transforms the parameter tuning problem from a direct high-dimensional search into a multi-stage process that adds temporal and hierarchical dimensions. By organizing parameter search across multiple iterations and levels, the system reduces the effective dimensionality at each search stage, making exhaustive search feasible for systems with many parameters.
2Productivity
If gradient descent algorithm is used to tune control parameters, then computational efficiency is improved, but the algorithm may converge at locally optimal points and fail to find globally optimal settings
Solution Approach 1:
The patent applies preliminary randomization techniques to the gradient descent algorithm by introducing random perturbations and multiple random restarts before the optimization process begins. This preliminary action helps the algorithm escape from local optima by exploring different regions of the parameter space before convergence, thereby improving the likelihood of finding globally optimal settings while maintaining computational efficiency.
Solution Approach 2:
The patent makes the gradient descent algorithm dynamic by adapting its search behavior based on the optimization progress. The system dynamically adjusts parameters such as learning rate, search step size, and exploration intensity during the tuning process, allowing it to balance between exploitation of promising regions and exploration of new areas, thus avoiding premature convergence to local optima.
Data Source
AI summary
Embodiments of the present disclosure relate to a method of automated tuning of control parameters. In some implementations, the method may include obtaining, from a search algorithm, one or more parameter sets that determine how a controller responds to an environment with at least one changing variable. In these and other implementations, at least one of the parameter sets may include a vector parameter that includes a vector of values. In these and other implementations, a value selected from the vector of values for the vector parameter during operation of the controller may be based on the at least one changing variable. In some implementations, the method may include ordering the vector of values for the vector parameter of the parameter sets and simulating at least one operation of the controller using the parameter sets with the ordered vector of values for the vector parameter.