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

The structure of intelligent control system (ICS) is analyzed, and the interrelations with conventional problems of the theory and practice of application of control systems are described. The analysis of the results of simulation of typical structures of intelligent control systems has allowed us to establish the following fact. The application of the technique of designing (presented in Part I), which is based on a fuzzy neural network (FNN), does not guarantee in general that the required accuracy of approximation of the training signal (TS) will be reached. As a result, under an essential change of external conditions, the sensitivity level of the controlled plant (CP) increases, which, on the whole, leads to a decrease in the robustness of the intelligent control system, and, as a consequence, to a loss of reliability (accuracy) of achieving the control goal. To eliminate the specified drawback of the neural network, a soft computing optimizer (SCO), which uses the technique of soft computing and allows one to eliminate the drawback, is applied, which results in an increase in the robustness level of the structure of the intelligent control system. The structure of the soft computing optimizer, which contains as a particular case the required configuration of an optimal fuzzy neural network, is considered. The main specific features of the functional operation of the soft computing optimizer and the stages of the process of designing robust knowledge bases (KB) of fuzzy controllers (FC) are described. The methodology of joint stochastic and fuzzy simulation of automatic control system based on the developed tool of the soft computing optimizer is discussed in order to test the robustness and to estimate the limiting structural capabilities of intelligent control systems. The efficiency of the control processes with application of the soft computing optimizer is demonstrated by particular typical examples (benchmarks) of models of dynamic controlled plants under the conditions of incomplete information about the parameters of the structure of the controlled plant and under the presence of unpredicted (abnormal) control situations. Examples of industrial application of robust intelligent control systems in actual control systems designed based on the soft computing optimizer are presented. Practical recommendations for improving the robustness level of intelligent control systems by using new types of computations and simulation are given

[1]  Desmond J. Higham,et al.  An Algorithmic Introduction to Numerical Simulation of , 2001 .

[2]  Udo Seifert.  Entropy production along a stochastic trajectory and an integral fluctuation theorem. , 2005, Physical review letters.

[3]  P Grigolini,et al.  Trajectory versus probability density entropy. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Fumihito Arai,et al.  Stochastic analysis of time-variant nonlinear dynamic systems. Part 1: The Fokker-Planck-Kolmogorov equation approach in stochastic mechanics , 1998 .

[5]  高橋 一樹,et al.  Soft computing optimizer Intelligent Control System , 2004 .

[6]  G. M. Ulanov,et al.  Informational Foundations of Qualitative Theory of Control Systems , 1978 .

[7]  S.V. Ulyanov,et al.  Quantum soft computing in control process design: quantum genetic algorithms and quantum neural network approaches , 2004, Proceedings World Automation Congress, 2004..

[8]  Desmond J. Higham,et al.  An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations , 2001, SIAM Rev..

[9]  Xuerong Mao,et al.  Numerical solutions of stochastic functional differential equations , 2003 .

[10]  K. Burrage,et al.  Numerical methods for strong solutions of stochastic differential equations: an overview , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Kazuo Yamafuji,et al.  Computational Intelligence for Robust Control Algorithms of Complex Dynamic Systems with Minimum Entropy Production , 1999, J. Adv. Comput. Intell. Intell. Informatics.

[12]  L. Litvintseva,et al.  SOFT COMPUTING OPTIMIZER FOR INTELLIGENT CONTROL SYSTEMS DESIGN: THE STRUCTURE AND APPLICATIONS , 2003 .

[13]  R. Spigler,et al.  Fast simulations of stochastic dynamical systems , 2005 .

[14]  Desmond J. Higham,et al.  Exponential mean square stability of numerical solutions to stochastic differential equations , 2003 .