Informatica

The aim is to develop simple for industrial use neuro-fuzzy (NF) predictive controllers (NFPCs) that improve the system performance and stability compensating the nonlinear plant inertia and time delay. A NF plant predictor is trained from real time plant control data and validated to supply a main model-free fuzzy logic controller with predicted plant information. A proper prediction horizon is determined via simulation investigations. The NFPC closed loop system stability is validated based on a parallel distributed compensation (PDC) approximation of the NFPC. The PDC can easily be embedded in industrial controllers. The proposed approach is applied for the real time air temperature control in a laboratory dryer. The improvements are reduced overshoot and settling time.

Due to high nonlinearities and time-varying dynamics of today's control systems fuzzy learning controllers find appliance in practice. The present paper proposes a method for the synthesis of the learning fuzzy controllers where an expert knowledge about a process is applied to form a learning mechanism that is used to acquire information for the knowledge base of the main fuzzy controller. According to the proposed method an expert knowledge is used to describe how the controller should learn to control rather than to control the process. The results of experiments on heating system and level/pressure system prove the practical relevance of the design strategy of a learning fuzzy controller.

This paper analyses coupled control of a nonlinear, time‐varying plant. Uncoupled and coupled direct adaptive controllers, based on fuzzy logics, are synthesized to control the water level and the air pressure in a closed tank. The satisfactory efficiency of the controllers is experimentally demonstrated running the plant under the different working conditions. Coupled fuzzy controllers are compared with the uncoupled fuzzy controllers.

The existing decomposition technology of cooperative developments of multidiscipline technical complexes (MTC) don't provide global optimality die to the imposiolity of solving the problem of developing principles of local project solutions made by a dreat number of specialists of different branches of science. This problem is supposed to be solved by means of controlling of real-time of MTC space structural-parametric synthesis in terms of hierarchically organized variety of assumed scheme-structural and technological solutions. The basis algorithm: 1) realization method for a variety of possible structural organizations of a complex technical system in the form of a network analyzer; 2) method combining synthesis combinatorial operations and parametric operations in search for short routes of the developed network analyzer.