The structure design of robust advanced autonomous or integrated control systems for unpredicted control situations is the corner stone of modern control theory and systems. The degree to which a control system deals successfully with above difficulties depends on the *intelligent* level of advanced control system. We will to solve the algorithmic complexity problems in advanced control system design with *unconventional sophisticated* methods of computational intelligence. Main result of quantum computing is exponential (or quadratic) speed-up in comparison to classical computation and solutions of problems. We are described structure of information design technology of intelligent self-organized fuzzy controllers based on quantum search algorithm model as quantum fuzzy inference (QFI).

On purpose to practical applications we investigate the problem of global robust knowledge base (KB) design for fuzzy controllers in unpredicted control situations when the classical solution is unknown. One of the most important elements of information Hi-Tech of intelligent control system’s design is the development of the methodology and the corresponding industrial software/hardware toolkit for testing and evaluating the robustness level of the designed structures. For this case the robustness is considered as a measure of sensitivity to various external and internal random perturbations acting both on the CO and in the measurement channels or control loops. The topicality of the solution to this problem is dictated by practical control tasks addressed by many researchers.

An increase in the complexity of the structures of CO and difficulties in predicting unexpected (unpredicted — unforeseen) control situations only stresses the topicality of this problem and draws attention to it. Such problems are referred to as the so-called *“System of Systems Engineering”* problem that studies in a general form complex structures of automatic control systems with different levels and scales of integration and/or priority data exchange between subsystems in order to establish global (necessary and sufficient) conditions for reliable autonomous (or collective) operations of the CO in external environments. The ability of automatic control system to response adequately to one or another change in the parameters of the external environments that is not given in advance in design process the automatic control system characterizes the level of learning, adaptation and robustness of processes, and the ability to learn and adapt itself. Frequently, the methods of the robust control theory are not able to solve general control problems under the presence of uncertainty described in the form of a certain stochastic process with definite (in general, unknown) statistical characteristics of probability density functions.

## 1. KB Soft Computing Optimizer

The main point in the design of intelligent control systems is the stage of knowledge extraction and forming the corresponding KB. Thus, the design of a KB with a required robustness level (under unpredicted control situations) allows one to establish in a general form the correspondence between the conditions of functioning of the CO and the required robustness level of the intelligent control system. In Step I of developed design technology, we focus the main attention on the description of particular results of KB design and simulating intelligent control systems with essentially nonlinear CO with a randomly time-dependent structure and control goals. In this case, the aim of this Step is to determine the robustness levels of control processes that ensure the required reliability and accuracy indices under the conditions of uncertainty of the information employed in decision-making (*learning* situations). For Step I, we are considered the methodology of joint stochastic and fuzzy simulation of automatic control systems based on the developed tools of unconventional methods as the soft computing optimizer with the aim to test the robustness of developed KB and estimate the limiting structural capabilities of intelligent control systems. The efficiency of control processes with application of the soft computing optimizer was demonstrated by particular typical examples (Benchmarks) of models of dynamic CO under the conditions of information uncertainty about the parameters of the CO and under the presence of unpredicted (abnormal) control situations (for details, see the Section Overview & Tutorial, Simulation results).

The introduction of the soft computing technology based on genetic algorithms and fuzzy neural networks in design process has extended the field of efficient applications of fuzzy controllers by using new control functions in the form of learning and adaptation. However, it is very difficult to design a globally appropriate and robust structure of the intelligent control system. This limitation is especially typical of unpredicted control situations when the CO operates in unsharp or changing conditions (a failure of sensors or noise in the sensor system, the presence of a time delay in control signals or measurement, a sharp change in the structure of CO or its parameters, etc.).

## 2. KB Quantum Computing Optimizer

For Step 2, the description of the strategy of robust structure’s design of an intelligent control system based on the technologies of quantum and soft computing is given. The developed strategy allows one to improve the robustness level of fuzzy controllers under the specified unpredicted or weakly formalized factors for the sake of forming and using new types of self-organization processes in the robust KB with the help of the quantum computing methodology. A particular solution of a given problem is obtained by introducing a generalization of decision making strategies in models of fuzzy inference in the form of a new QFI on a finite set of fuzzy controllers designed in advance. The model of QFI is a new type of the quantum search algorithm on the generalized space of structured data and based on the methods of quantum computing theory it allows one to solve efficiently control problems that could not be solved earlier at the classical level. The developed approach is first applied in the theory and practice of intelligent control systems.

In the proposed model of the quantum algorithm for QFI the following actions are realized: (1) the results of fuzzy inference are processed for each independent fuzzy controller; (2) based on the methods of quantum information theory, valuable quantum information hidden in independent (individual) KBs is extracted; and (3) in on-line, the generalized output robust control signal is designed in all sets of KB of the fuzzy controller. In this case, the output signal of QFI in on-line is an optimal signal of control of the gain’s schedule of the PID controller, which involves the necessary (best) qualitative characteristics of the output control signals of each of the fuzzy controllers.

Thus we are implementing the self-organization principle (for details see pdf). The fundamental structure of QFI-model and its software toolkit in the design processes of the KB for robust fuzzy controllers in on-line, as well as a system for simulating robust structures of fuzzy controllers, are described. The efficiency of applying QFI is illustrated by a particular example of simulation of robust control processes by an essentially nonlinear dynamic CO with randomly time-dependent structure. Therefore, the domain of efficient functioning of the structure of the intelligent control system can be essentially extended by including the property of robustness, which is very important characteristic of control quality. The robustness of the control signal is the background for maintaining the reliability and accuracy of control under uncertainty conditions of information or a weakly formalized description of functioning conditions and/or control goals.

## 3. Implementation of Design Technology

Applied purposes of our developed design technology are the know-how implementation of the following control performance: (i) ensure the requested level of intelligent control robustness in unpredicted control situations with KB self-organization using the min-entropy principle relative to quantum knowledge; and (ii) support the reliability of advanced control systems in conditions of industrial disturbances with optimal thermodynamic trade-off between stability, controllability and robustness. Quantum control algorithm of KB self-organization that has developed for the task solution in item (i) is the background for the industrial applications of the optimal trade-off solutions in item (ii). Another applied purpose is the design principle realization of robust intelligent control: Development of industrial wise intelligent controllers with physically realized simple structure for complex control objects in unpredicted control situations.

The ultimate applications of quantum control strategies may include CO as smart macro- and micro-electromechanical systems, intelligent sensor systems (with compressing of redundant data information processing and advanced decision making), intelligent robotics and mechatronics, quantum informatics, computer science, AI security communication and information systems, including quantum algorithm modeling system for robust intelligent control design in nanotechnologies. Background for the realization of abovementioned goals and industrial applications is the R&D results of our information design technology (see detail description in pdf).