Information-thermodynamic uncertainty in intelligent self-organized fuzzy control systems and extractable work for thermodynamic control force from hidden quantum information

The quantum self-organization algorithm model of wise knowledge base design for intelligent fuzzy controllers with required robust level from information-thermodynamic viewpoint considered. Background of the model is a new model of quantum inference based on quantum genetic algorithm and design optimal structure of quantum neural network. Quantum deep machine learning toolkit applied developed platform titled as «Quantum Computing Optimizer of Knowledge Base» ($QCOptKB^{TM}$). Quantum algorithm applied on line for the quantum correlation’s type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing ($SCOptKB^{TM}$ toolkit). Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (stability, controllability and robustness) as new control design laws discussed. The smart control design with guaranteed achievement of these trade-off interrelations is the main goal for quantum self-organization algorithm of imperfect KB. Sophisticated synergetic quantum information effect introduced: a new robust smart knowledge base can be created on line from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation. Moreover, the new force control law from quantum thermodynamic described: with extracted hidden quantum information from classical control signal states (on micro-level) possible to design in on-line new control force that can produce on macro-level more value work amount than the work losses on the extraction of this amount of hidden quantum information. Within the toolkit of classical intelligent control based on soft computing the achievement of the similar synergetic information effect is impossible. Benchmarks of intelligent cognitive robotic control applications considered.