Information Design Technology of Robust Integrated Fuzzy Intelligent Control Systems based on Unconventional Computational Intelligence: Quantum Control Algorithm of Robust KB Self-Organization

Extended abstract Engineering conventional methods of advanced control theory and the technique design of automatic control systems were formed in the past century. In particular, the background and foundations of stochastic learning, adaptive and self-organization control of complex dynamic systems in general, with time-dependent (variable) structure under information uncertainty conditions were developed. The next step in this direction was the principle's development of simulation and design of fuzzy control systems under uncertainty conditions that take into account the individual specific features of the behavior of chosen trajectories without sharp defined model description of the control object (CO). This design methodology was based on the fuzzy set theory, linguistic approximation and fuzzy inference (L.A. Zadeh and others) for developing robust knowledge bases (KB) of intelligent fuzzy controllers. Within the framework of the specified methodology of control laws design based on physical approaches (information– thermodynamic and quantum–relativistic methods of describing CO and control processes), in the mid 1980s, the background of the design technique of intelligent control systems was developed. The problem of modern advanced control In complex and essentially nonlinear dynamic models of CO with weakly formalized structure and random parameters, it is quite difficult with conventional design methods to determine an optimal structure of an automatic control system, in which, e.g., a conventional proportional–integral–differentiating (PID) controller is employed at the lower (executive) level. Especially, this difficulty reveals itself in design problems of the structures of automatic control systems in the presence of random noise different in its nature and under information uncertainty about the control goals. Computational intelligence is one of an effective toolkit for fuzzy modeling system in design technology of robust intelligent control systems. We have developed a new quantum fuzzy modeling system (QFMS – see in details, section Overview, Quantum Modeling System) based on a new computational intelligence paradigm as quantum computing technology for design of self-organization robust KB in unpredicted control situations. Computation, based on the laws of classical physics, leads to different constraints on information processing than computation based on quantum mechanics. Quantum computers hold promise for solving many intractable problems. But, unfortunately, there currently exist no algorithms for " programming " a quantum computer. Calculation in a quantum computer (like calculation in a conventional computer) can be described as a marriage of quantum HW (the physical embodiment of the computing machine itself, such as quantum gates and the like), and quantum SW (the computing …