WB4 Intelligent Control: Theory

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Validation of Adaptive-Critic Based Infinite Time Optimal Neuro Control for Distributed Parameter Systems

Authors:

Padhi R., University of Missouri-Rolla, USA

Balakrishnan N.S., University of Missouri-Rolla, USA

Randolph T., University of Missouri-Rolla, USA

ABSTRACT

Recently the necessary conditions of optimality for distributed parameter systems described in discrete domain have been developed, followed by the synthesis of the infinite time optimal neuro-controllers in the framework of adaptive-critic design. In this paper, we validate this synthesis methodology by comparing it with two other different approaches already established in the literature.

Key Words. Distributed parameter systems, infinite dimensional systems, partial differential equations, optimal control, dynamic programming, neural networks, adaptive-critic synthesis.

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Optimization of a fuzzy logic controller using genetic algorithms

Authors:

Kanarachos A., National Technical University of Athens, Greece

Koulocheris D., National Technical University of Athens, Greece

Vrazopoulos H., National Technical University of Athens, Greece

ABSTRACT

The scope of this paper is to present an optimised fuzzy logic controller used in suspension system for ground vehicles. The vehicle system is described by linear differential equations subject to many types of road irregularities. The fuzzy logic rules are optimised such that the maximum value of vertical and rotary acceleration of vehicle body at the passengers seats are minimised from the view point of ride comfort under the geometrical constraints of the car. The simulation results show that the proposed fuzzy logic controller improved the vehicle ride comfort.

Key Words. Fuzzy logic controller, optimisation, genetic algorithms, semi-active suspension systems

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Adaptive Mode Transition Control of Nonlinear Systems using Fuzzy Neural Networks

Authors:

Rufus F., Georgia Institute of Technology, USA

Vachtsevanos G., Georgia Institute of Technology, USA

Heck B., Georgia Institute of Technology, USA

ABSTRACT

An adaptation scheme is proposed for the online customization of mode transition controllers designed off-line via the method of blending local mode controllers. It consists of the desired transition trajectory model, the active plant model and the active controller model, which is the mode transition controller. The latter two models are initially off-line trained and online adapted via structure/parameter learning. The control sensitivity matrix and the one-step-ahead predictive output of the active plant model are used to adapt the parameters of the mode transition controller such that the desired transition trajectory is tracked. The proposed adaptation scheme is illustrated for a hover to forward flight mode transition control of a helicopter encountering parametric changes and wind disturbances.

Key Words. Adaptive tracking control, fuzzy neural networks, mode transition control, kaczmarz's algorithm.

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Stability Analysis of Takagi-Sugeno Fuzzy Systems with Linear Input-Output Submodels

Authors:

Dvorakova R., Czech Technical University, Czech Republic

Husek P. - Czech Technical University, Czech Republic

ABSTRACT

This paper presents a method analyzing stability of Takagi-Sugeno fuzzy systems with linear input-output submodels in the consequents of rules. This method can be used for stability analysis of a Takagi-Sugeno fuzzy model of a plant and for closed-loop system, where both the plant and the controller are represented by Takagi-Sugeno fuzzy systems. It will be shown that the problem of stability analysis of such a system can be transformed to robust stability analysis of a polynomial with polynomic structure of its coefficients. Stability of such polynomials is tested by the Modified Jury criterion, the Modified Routh criterion or the Hurwitz criterion together with Sign-decomposition. A necessary condition for stability of the Takagi-Sugeno closed loop systems is obtained.

Keywords. Takagi-Sugeno fuzzy systems, stability analysis, polynomials, polynomic uncertainty

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Improved Training of Multilayer Feedforward Neural Networks for Large Input Vectors

Authors:

Caleanu C.-D. - University Politehnica Timisoara, Romania

L. Petropoulakis, University of Strathclyde, Scotland

ABSTRACT

In this paper, a new fuzzy controller for inferring multiplayer feedforward neural networks learning rate is presented. The key issue is using relative values of a performance attribute as fuzzy controller inputs, resulting in an increased generality of fuzzy learning rate adaptation and a faster training algorithm. Experimental results demonstrate improvements in terms of generalization capability and of learning speed in both large pattern recognition and data processing tasks.

Key Words. Neural nets, fuzzy control, training, algorithm.

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