Robust Quantum Controllers: Quantum Information -- Thermodynamic Hidden Force Control in Intelligent Robotics based on Quantum Soft Computing

For complex and ill-defined dynamic control objects that are not easily controlled by conventional control systems (such as P-[I]-D-controllers) -- especially in the presence of fuzzy model parameters and different stochastic noises -- the System of Systems Engineering methodology provides fuzzy controllers (FC) as one of alternative way of control systems design. Soft computing methodologies, such as genetic algorithms (GA) and fuzzy neural networks (FNN) had expanded application areas of FC by adding optimization, learning and adaptation features. But still now it is difficult to design optimal and robust intelligent control system, when its operational conditions have to evolve dramatically (aging, sensor failure and so on). Such conditions could be predicted from one hand, but it is difficult to cover such situations by a single FC. Using unconventional computational intelligence toolkit, we propose a solution of such kind of generalization problems by introducing a self-organization design process of robust KB-FC that supported by the Quantum Fuzzy Inference (QFI) based on quantum soft computing ideas [1-3].