Autonomous Decentralized Control System for Load Balancing
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Solution Overview
Problem
Current autonomous decentralized systems face challenges in deterministic operation and setting optimal Activity functions, leading to inefficient control in large-scale systems, particularly in mixed-machine environments where components with different performance characteristics are used.
Innovation Solution
A system comprising function blocks with evaluation functions, restraint condition fulfillment control, profit maximization control, and activation/shutdown problem solution control units, which operate based on evaluation functions and gradients to ensure deterministic and efficient operation of components, optimizing load balancing and resource allocation without relying on probability theory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional centralized control is used, then system control is simple and straightforward, but it becomes difficult to handle large-scale systems and cannot adapt to unpredictable disturbances in real-time
Solution Approach 1:
The patent divides the centralized control system into multiple autonomous decentralized control units, each capable of independent decision-making. This segmentation allows the system to scale while maintaining adaptability, as each unit can respond to local conditions without requiring centralized coordination for every decision.
Solution Approach 2:
The patent implements autonomous control where system components self-regulate based on local evaluation functions and constraints. Each control unit independently optimizes its operation without external intervention, enabling real-time adaptation to disturbances while simplifying overall system management through emergent coordination.
2Reliability
If stochastic control based on probability theory is used, then system requirements can be fulfilled eventually, but energy efficiency deteriorates due to inefficient intermediate processes
Solution Approach 1:
The patent implements continuous feedback mechanisms where control units monitor system state and adjust operations based on evaluation functions. This deterministic feedback loop ensures requirements are met while optimizing energy efficiency, as the system learns from past performance and makes informed decisions rather than relying on probabilistic outcomes.
Solution Approach 2:
The patent dynamically adjusts control parameters based on real-time system conditions and evaluation function results. By changing operational parameters deterministically rather than probabilistically, the system achieves reliable requirement fulfillment while minimizing energy consumption through optimized intermediate processes.
3Productivity
If load balancing is applied to mixed-machine systems with different performance characteristics, then resource utilization can be optimized, but determining appropriate balancing indices becomes unclear
Solution Approach 1:
The patent assigns different evaluation functions and constraints to control units based on their specific machine characteristics. Each unit optimizes locally according to its own performance profile rather than applying a uniform balancing index, which simplifies the determination process while maintaining overall system optimization through heterogeneous resource utilization.
4Adaptability or versatility
If autonomous decentralized control is implemented, then real-time adaptation to disturbances is improved, but system coordination and concert among components becomes challenging
Solution Approach 1:
The patent implements a universal evaluation function framework that all autonomous control units follow, despite their different local objectives. This universal structure ensures coordinated behavior and system stability while allowing each unit to adapt independently to local disturbances, resolving the tension between decentralization and coordination.
Data Source
AI summary
An object is to provide a system in which components can overall operate in concert with one another by using only functions about the components, without using the theory of probability. The system includes a plurality of function blocks correlated to one another. The plurality of function blocks each include: a storing unit including an evaluation function that is associated with its own function block; a profit maximization control unit for adjusting the operation level of its own function block by information about the evaluation function of another of the plurality of function blocks that has a relation with its own function block; an activation/shutdown problem solution control unit for adjusting a suppression of the operation level by profit maximization control based on an active state/shutdown state of its own function block; and a restraint condition fulfillment control unit for adjusting the operation level of its own function block based on a restraint condition that is to be fulfilled by its own function block and all of the plurality of function blocks together.