Event-Dependent Multicriteria Optimization for Cyber-Physical Control
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Solution Overview
Problem
Current computational methods for optimizing cyber-physical systems, such as water management systems, face challenges due to high computational complexity and the need for simplifications that lead to inaccurate results, requiring extensive computing power or the use of heuristic models which are not optimal.
Innovation Solution
The system captures each objective from a list of prioritized objectives as a bipartite objective function, with one part directly addressing the objective and another part ensuring that previous suboptimization results are not negatively affected, allowing for parallel processing of subproblems and reducing the need for constraints, thereby enabling more efficient and accurate global optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If computational programs use simplifications to reduce complexity, then computing time and resources are reduced, but the accuracy of optimization results deteriorates
Solution Approach 1:
The patent divides the complex multicriteria optimization problem into multiple event-dependent suboptimization problems, each corresponding to specific events in the cyber-physical system. By segmenting the problem temporally and event-wise, the system can process simpler subproblems sequentially or in parallel, reducing overall computational complexity while maintaining accuracy through the event-driven structure that preserves critical system behavior relationships.
2Measurement precision
If computational programs consider all variables and subproblems without simplification, then optimization accuracy is improved, but computing power requirements and complexity increase
Solution Approach 1:
The patent implements a dynamic optimization approach where the set of active objectives and constraints changes based on occurring events. The system dynamically activates or deactivates specific suboptimization problems depending on the current event state, allowing the computational program to focus only on relevant variables and constraints at each time step, thereby reducing complexity while maintaining comprehensive accuracy when events require it.
3Use of energy by moving object
If heuristic models are used to reduce computational requirements, then computing power and time are reduced, but solution optimality deteriorates
Solution Approach 1:
The patent introduces an event-dependent structuring as an intermediary layer between the physical system and the optimization algorithms. This intermediary structure organizes objectives and constraints into event-based subproblems, allowing exact optimization methods to be applied to each subproblem rather than using heuristic approximations on the full problem. The event structure acts as a mediator that reduces computational burden while preserving solution optimality through systematic decomposition.
Data Source
AI summary
A system for solving an event-dependent multicriteria optimization problem of at least one cyber-physical system, comprising a control device for controlling the at least one cyber-physical system, the control device controlling the cyber-physical system in dependence on a list of prioritized objectives by solving at least one event-dependent suboptimization problem is characterized in that each objective from the list of prioritized objectives is captured as an objective function, each objective function consisting of at least two parts, a first part of which relates to directly capturing the objective and a second part of which describes a condition under which each result of one of the preceding objectives of each of the preceding suboptimization problems is substantially not negatively affected.