Adaptive Sub-Modules for Nonlinear Control
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Solution Overview
Problem
Existing approaches to nonlinear adaptive control and predictive filtering face challenges in scalability and efficiency, particularly for systems with high degrees of freedom, as they struggle to account for kinematic changes and require extensive hardware resources, limiting their application in distributed systems.
Innovation Solution
A system design incorporating adaptive sub-modules with nonlinear components that generate scalar outputs, coupled via weighted connections, allowing for efficient adaptive control and predictive filtering by updating connection weights based on initial outputs, enabling adaptation to system dynamics and kinematics, and scalable across higher dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional nonlinear adaptive control methods are used for high degree of freedom systems, then control accuracy is improved, but device complexity and computational resources increase exponentially due to the curse of dimensionality
Solution Approach 1:
The system segments the complex nonlinear control problem into multiple adaptive sub-modules, each handling a specific aspect of the control task. Each sub-module contains a manageable number of nonlinear components with scalar outputs, avoiding the need to model the entire high-dimensional system as a single complex unit. This segmentation reduces the exponential complexity growth while maintaining overall control accuracy.
Solution Approach 2:
The patent transforms the high-dimensional control problem by introducing a new dimensional structure where multiple scalar-output nonlinear components work in parallel within adaptive sub-modules. Instead of requiring an exponentially large number of basis functions to tile the state space, the system uses a structured arrangement of simpler components that collectively handle the high-degree-of-freedom system through weighted combinations and adaptive learning.
2Measurement precision
If system models are made more accurate to account for kinematic changes and degradation, then control and prediction accuracy is improved, but the complexity of the model increases
Solution Approach 1:
The system incorporates dynamic adaptation mechanisms where the weights connecting nonlinear components are continuously adjusted based on observed system behavior. This allows the model to adapt to kinematic changes and degradation over time without requiring a statically complex model structure. The adaptive learning process enables the system to capture changing dynamics while maintaining a relatively simple underlying architecture.
Solution Approach 2:
The patent implements feedback mechanisms where system state information is fed back to the adaptive sub-modules, enabling real-time adjustment of control and prediction. This feedback loop allows the system to account for kinematic changes and degradation by continuously learning from actual system behavior, improving accuracy without proportionally increasing model complexity.
3Adaptability or versatility
If hardware resources are increased to support more complex nonlinear models, then adaptive control capability is improved, but cost and implementation difficulty increase
Solution Approach 1:
The adaptive sub-modules are designed as universal building blocks that can be instantiated multiple times with different configurations to handle various control tasks. Each sub-module contains nonlinear components with scalar outputs that can be combined through weighted connections to address different aspects of the control problem. This modular universal design reduces implementation difficulty compared to customizing complex models for each specific application.
Solution Approach 2:
The system uses multiple instances of standardized adaptive sub-modules rather than implementing a single large complex model. Each sub-module is a replicated unit with a manageable number of parameters, making them easier to manufacture and implement. The collective behavior of multiple copied sub-modules provides the necessary adaptive control capability without requiring any single component to be overly complex.
Data Source
AI summary
Methods, systems and methods for designing a system that provides adaptive control and adaptive predictive filtering using nonlinear components. A system design is described that provides an engineered architecture. This architecture defines a core set of network dynamics that carry out specific functions related to control or prediction. The adaptation systems and methods can be applied to limited areas of the system to allow the system to learn to compensate for unmodeled system dynamics and kinematics. Two types of adaptive modules are described which are configured to account for the unmodeled system dynamics and kinematics.