Ensemble Policy Control Using Genetic Evolution for Adaptive Systems
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Solution Overview
Problem
Existing control systems face challenges in achieving adaptive control performance due to inaccurate system identification and model parameter estimation, especially in complex control environments where model hypotheses often fail to hold true, leading to suboptimal control effects and limited adaptability.
Innovation Solution
A control system incorporating a genetic evolution module that randomly selects chromosomes from an evolution pool to generate ensemble policies for multiple function modules, enabling ensemble calculations and adaptive control signal generation, thereby improving adaptability and control efficiency in complex environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional model-based control is used, then control system simplicity is maintained, but adaptability deteriorates due to inaccurate system identification and model parameter estimation
Solution Approach 1:
The control system dynamically evolves the controller structure and parameters through genetic algorithms, transitioning from static model-based control to adaptive evolution-based control. The controller automatically adjusts its characteristics in response to environmental changes and system variations, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The controller performs self-identification and self-adjustment through genetic evolution, eliminating the need for accurate external system identification. The controller evolves its own parameters and structure based on performance feedback, achieving high adaptability without requiring complex external modeling processes.
2Measurement precision
If accurate system identification is performed, then control precision is improved, but the time and computational resources required increase
Solution Approach 1:
The genetic algorithm pre-evolves a population of controller parameters before actual control begins. This preliminary evolution prepares multiple candidate solutions in advance, so when control is needed, the system can quickly select from pre-computed options rather than performing time-consuming identification in real-time.
Solution Approach 2:
Traditional mechanical system identification methods are replaced with computational genetic algorithms. Instead of using physical experimentation and mathematical modeling to identify system parameters, the system uses evolutionary computation to directly evolve controller parameters that achieve desired performance, significantly reducing identification time.
3Productivity
If fixed model parameters are used, then controller design is simplified, but control performance deteriorates in changing environments
Solution Approach 1:
The controller parameters transition from fixed to dynamic through genetic evolution. The system continuously adapts parameters based on environmental feedback, maintaining high control efficiency across changing conditions. The evolutionary process ensures parameters remain optimized for current environmental states rather than being locked to historical conditions.
Solution Approach 2:
The genetic algorithm systematically varies controller parameters through mutation and recombination operations. This parameter exploration allows the system to find optimal settings for different environmental conditions, resolving the contradiction between using fixed parameters for simplicity and varying parameters for adaptability.
Data Source
AI summary
A system control method includes receiving a control task and randomly selecting a chromosome from an evolution pool according to the control task. The selected chromosome is decoded to obtain (N+1) ensemble policies, where the chromosome includes (N+1) gene fragments, and where N is a positive integer greater than or equal to 1. Each gene fragment can uniquely correspond to an ensemble policy, and each ensemble policy can uniquely correspond to a preset function. One ensemble policy is used for assigning a weight to a preset function that uniquely corresponds to the ensemble policy. The evolution pool can maintain two or more chromosomes. An ensemble calculation is performed according to weights assigned by the (N+1) ensemble policies to obtain an ensemble control output. A control signal is generated according to the ensemble control output, where the control signal is used for performing system control.