Hybrid AI Control Architecture for Real-Time Adaptive Process Control
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Solution Overview
Problem
Existing process control systems are limited by their inability to dynamically reprogram and adapt, relying on fixed intelligence categories like PID controllers, Inference Engines, and Neural Networks, which are difficult to reconfigure and require specialized hardware and extensive training.
Innovation Solution
A system combining Probabilistic Reasoning, Inference Engine Logic, Neural Networks, and Evolutionary Computation Structures, utilizing dynamically modifiable hybrid engines with sensors and multi-core processors, Field Programmable Gate Arrays, and a library of pre-programmed functions to enable real-time management and interaction with both hardware and software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If fixed intelligence categories like PID controllers, Inference Engines, and Neural Networks are used, then system stability is maintained, but system adaptability deteriorates
Solution Approach 1:
The patent implements a dynamically reconfigurable control system where the intelligence category (PID, IEL, NN, or DAI) can be changed at runtime through a reconfiguration interface. This allows the system to adapt to different process requirements while maintaining stability through controlled transitions between modes, directly resolving the contradiction between fixed stability and adaptive versatility.
2Ease of operation
If Inference Engines with fixed fuzzy knowledge base are used, then ease of operation is improved, but device complexity increases due to difficulty in reprogramming
Solution Approach 1:
The patent creates a universal control appliance that can function as multiple intelligence categories (PID, IEL, NN, DAI) through software configuration rather than hardware specialization. The reconfiguration interface allows users to switch between different control paradigms without complex reprogramming, making the system universally applicable to various process control needs while simplifying the user experience.
3Manufacturing precision
If Neural Networks are used, then manufacturing precision is improved, but device complexity increases due to specialized hardware requirements
Solution Approach 1:
The patent replaces specialized neural network hardware with software-based neural network implementation on general-purpose processors. The neural network engine is implemented as executable code that can run on standard computing hardware, eliminating the need for dedicated neural network chips or specialized hardware while maintaining the precision benefits of neural network control.
4Adaptability or versatility
If multiple intelligence categories are integrated, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple intelligence categories (PID, IEL, NN, DAI) into a single integrated control appliance with a unified architecture. The hybrid engine combines probabilistic reasoning, inference engine logic, neural networks, and evolutionary computation structures into one cohesive system that can dynamically select and switch between different control paradigms, providing versatile adaptability without proportionally increasing system complexity.
Data Source
AI summary
A control apparatus providing a Dynamic Artificial Intelligence system, which employs data sets and software functions representing a plurality interactive software engine's, including Inference, Neural Net, State, and Proportional-Integral-Derivative (PID) Engines. These engines are implemented as a set of scheduled realtime monitors and callable functions with associated processes preformed within a system. Monitors dynamically estimates and determine the optimal control policy for the system and its sub-systems. Monitors utilize an iterative process of sub-steps “function calls’, until a convergence states exist. Functions and subfunctions dynamically estimate the desired value for operation at a respective state of the environment over a series of predicted environmental states; using a complex return of data sets to determine bounds to improve the estimated currently desired value; and producing updated estimates of optimal control policies. DAI further interacts in realtime with external events to modify control policies.