Decentralized Signal-Flow Architecture for Resource Allocation
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Solution Overview
Problem
Current signal processing architectures often lose the powerful analytic tools derived from conservation principles, as they are far removed from the physics underlying their implementation, leading to inefficiencies in resource allocation and stability in decentralized systems.
Innovation Solution
A decentralized signal-flow architecture that allows agents to communicate multipoint-to-multipoint, enabling each agent to determine and adjust resource usage based on differences with other agents, using controllers and transmitters/receivers, and optimizing resource allocation through penalty functions to minimize a cost function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decentralized signal processing architectures are used to enable flexible resource allocation among multiple agents, then adaptability and versatility are improved, but loss of information occurs because agents cannot globally communicate resource usage status
Solution Approach 1:
The patent introduces virtual effort and virtual flow signals as intermediaries that carry resource usage information between agents. These virtual signals act as mediators that allow decentralized agents to indirectly exchange information about resource consumption without requiring direct global communication, thus resolving the information loss problem while maintaining allocation flexibility
Solution Approach 2:
The patent transforms physical resource usage parameters into conjugate effort-flow variable pairs that satisfy conservation relationships. By changing the parameter representation from direct resource measurements to conjugate variables with conservation properties, agents can infer global resource status from local measurements, eliminating information loss in decentralized architectures
2Stability of the object's composition
If conservation principles are applied to decentralized control systems, then stability is improved, but device complexity increases due to the need for conjugate effort-flow variable pairs and conservation law enforcement
Solution Approach 1:
The patent applies conservation principles universally across all resource types by formulating them as conjugate effort-flow variable pairs. This universal formulation allows the same stability-guaranteeing mechanism to be applied to different resources (energy, bandwidth, physical space) without increasing complexity for each specific case, as the mathematical structure remains consistent
Solution Approach 2:
The patent implements feedback mechanisms where agents adjust their resource usage based on the difference between desired and actual resource allocation, determined through conservation law enforcement. This feedback loop ensures stability by continuously correcting deviations from the optimal allocation state, while the modular feedback structure prevents excessive complexity
3Manufacturing precision
If global communication is implemented to achieve optimal resource allocation, then manufacturing precision is improved, but loss of time occurs due to the overhead of global information exchange
Solution Approach 1:
The patent segments the global resource allocation problem into local sub-problems that each agent can solve independently using conservation principles. By dividing the overall allocation task into localized decisions based on conjugate variable differences, the system achieves precise resource allocation without requiring agents to communicate globally, thus eliminating communication time overhead while maintaining allocation precision
Data Source
AI summary
Methods and supporting systems for allocating a resource among multiple agents are disclosed. Multipoint-to-multipoint communication is established among the agents, with each agent using an amount of a resource. A first agent receives information associated with the resource usage of a second agent and determines the difference in resource usage between itself and the second agent. Based in part on the resource usage difference, the first agent is controlled to modify its use of the resource.