Design of robust knowledge bases of fuzzy controllers for intelligent control of substantially nonlinear dynamic systems: II. A soft computing optimizer and robustness of intelligent control systems

Self-Organization of robust intelligent controller using quantum fuzzy inference

“…Since their appearance, FCs demonstrates their great applicability in cases when control object is ill-defined or it operates under unknown conditions, when traditional negative feedback based controller is failing [1,2]. Soft computing methodologies, such as genetic algorithms (GA) and fuzzy neural networks (FNN) had expanded application areas of FC by adding learning and adaptation features.…”

Self-Organized Intelligent Robust Control Based on Quantum Fuzzy Inference

“…The output result of application of this information design technology is a robust KB of the FC that allows the ICS to operate under various types of information uncertainty. Selforganized ICS based on soft computing technology can supports thermodynamic trade-off in interrelations between stability, controllability and robustness (Litvintseva et al, 2006). Remark.…”

“…For TS design (or for GA fitness evaluation) we use stochastic simulation system based on the control object model. More detail description of SCO is given in (Litvintseva et al, 2006). Figure 5 illustrates as an example the structure and main ideas of self-organized control system consisting of two FC's coupling in one QFI chain that supplies a self-organizing capability.…”

“…For more complicated control situations learning and adaptation methods based on BP-algorithms or iterative stochastic algorithms do not guarantee the required robustness and accuracy of control. The solution of this problem based on SCO of KB was developed (Litvintseva et al, 2006). For achieving of self-organization level in intelligent control system it is necessary to use QFI (Litvintseva et al, 2007).…”

“…12) different unforeseen control situations and compared control performances of FC1, FC2, and self-organized control system based on QFI with two FC's. The stochastic simulation of random excitations with appropriate probability density functions is based on nonlinear forming filters methodology developed in (Litvintseva et al, 2006). In modelling we are considered with developed toolkit different unforeseen control situations and compared control performances of FC1, FC2, and self-organized control system based on QFI with two FC's.…”