DEVELOPMENT OF AN ADAPTIVE CONTROL MODEL BASED ON QUANTUM GENETIC ALGORITHMS FOR TECHNICAL COGNITIVE SYSTEMS

Abstract

systems, optimize production processes, and ensure system flexibility
in the face of uncertainty and variability. Production lines and technical systems
are often subject to random influences that can reduce their efficiency and product
quality. It is also necessary for the system to be able to make fast and effective
decisions, optimize resources, and adapt to changes that occur from time to time.
We propose an adaptive control model based on technical cognitive systems,
which utilizes quantum genetic algorithms to address these issues. With the help
of quantum computing and genetic algorithms, systems are optimized, which
increases the efficiency of production processes. The model helps save energy
resources and accelerate the decision-making process in multidimensional systems.
As a result, production systems become more efficient and flexible, which improves
product quality and maximizes resource use.