Procedural Event Sequence Search for Adaptive Control Bottlenecks
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Solution Overview
Problem
Existing adaptive control systems are inefficient, energy-intensive, and lack the ability to adapt to changing constraints, leading to suboptimal solutions and repeated problems, especially in complex systems, and require significant effort and time for setup and maintenance.
Innovation Solution
A control method that incorporates a knowledge management system using event sequences to transform start-object situations into target-object situations by determining suitable event sequences from a database, allowing for iterative search, selection, and concatenation of partial event sequences, and enabling quasi-learning capabilities to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adaptive control systems are used to control complex systems, then the system can respond to changing conditions, but energy consumption increases and calculation time is extended
Solution Approach 1:
The control system is segmented into multiple specialized modules: a pattern recognition module that identifies system states, a lookup table module that stores pre-computed control strategies, and a control execution module. This segmentation allows the system to avoid full complex calculations by breaking down the control problem into manageable parts that can be resolved through pattern matching and table lookups, significantly reducing energy consumption while maintaining adaptability.
Solution Approach 2:
The system pre-computes and stores control strategies in lookup tables during system initialization or offline training phases. When the system encounters a familiar state pattern, it can directly retrieve the pre-computed control action without performing real-time optimization calculations. This preliminary action transfers computational burden from runtime to setup time, reducing energy consumption during actual control operations while preserving the ability to adapt to changing conditions through the pattern recognition module.
2Productivity
If adaptive control systems perform extensive calculations to optimize process time, then better solutions are found, but valuable process time is wasted
Solution Approach 1:
The system replaces traditional mechanical optimization algorithms (such as gradient descent or dynamic programming) with an information-based approach using pattern recognition and lookup tables. Instead of performing iterative mathematical calculations to find optimal control actions, the system substitutes these heavy computational mechanisms with pattern matching against stored experiences, dramatically reducing calculation time while maintaining solution quality for recurring control scenarios.
Solution Approach 2:
The system creates copies of previously successful control strategies and stores them in lookup tables. When the same or similar system states occur again, the control system copies the proven strategy from the lookup table rather than recalculating from scratch. This copying mechanism preserves the quality of optimized solutions while eliminating redundant calculation time, as the system leverages past optimization results for repeated control tasks.
3Adaptability or versatility
If control systems lack learning capabilities, then setup and maintenance effort is reduced, but the system cannot adapt to changing constraints and repeats problems
Solution Approach 1:
The control system incorporates self-learning capabilities where the pattern recognition module automatically identifies new system states and the lookup table is dynamically updated with new control strategies derived from successful operations. The system serves itself by automatically expanding its knowledge base through experience, adapting to changing constraints without requiring external reprogramming or manual intervention, thereby achieving adaptability while keeping the overall system architecture relatively simple.
Solution Approach 2:
The system implements feedback mechanisms where the results of control actions are monitored and fed back to the pattern recognition module. When new state patterns are identified or existing patterns are refined based on feedback, the lookup table is updated accordingly. This feedback loop enables the system to continuously learn and adapt to changing constraints, improving its performance over time while maintaining a manageable system structure through the modular architecture.
Data Source
AI summary
The present invention provides a method for the adaptive control of a process or a control system, in particular for the automated finding of the solution to a problem or a task, in particular with the collection and/or use of procedural event knowledge, comprising: defining a task, which consists in converting a specific start-object situation into a specific target-object situation with the aid of an event sequence, reading a database for the purpose of searching for a suitable solution in the form of an event sequence which is suitable for solving the task, the database being suitable for associating at least the following variables with one another: an identifier of a possible start-object situation, identifiers of a possible target-object situation, identifiers of the involved object kinds, an information about an event sequence, wherein the event sequence is suitable to transfer the possible start-object situation into the possible target-object situation, selecting a procedural event sequence as a solution matching the task in the database, if a solution matching the task has been read, or forming a new event sequence, if a solution matching the task has not been read, as n-concatenation from the existing event sequences, comprising the following steps: Reading the database for the purpose of searching at least a first and an n-th event sequence, in particular searching event sequences from a first to an n-th event sequence, where n denotes a natural number and the first event sequence is suitable for transforming the possible start-object situation into a first intermediate object situation and for all natural numbers, for which 1<k<n, the kth event sequence is suitable for converting a (k−1)th intermediate object situation into a kth intermediate object situation, and the nth event sequence is suitable for converting an (n−1)th intermediate object situation into the possible target-object situation, if necessary, storing a newly created event sequence as n-concatenation in the database, the new concatenated event sequence being suitable for converting the possible start-object situation into the possible target-object situation, it being possible in this case for the event sequences between two object situations to be initially collected and stored in any desired programming or description language, in particular in any desired but uniform programming or description language. The invention further relates to corresponding devices. The invention further extends to corresponding computer programs and data carriers containing such computer programs, as well as the transmission of such computer programs via the cloud or, for example, the Internet. A new search engine of procedural event knowledge for all adaptive control systems is created.