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Adaptive Neuro controller for a Precise Maneouvring of Underwater Vehicle

IFAC Proceedings Volumes 2000年第21期33卷 173-178页

作者： Zoran Vukić Bruno Borović Boris Tovornik Department of Control and Computer Engineering in Automation University of Zagreb Control Engineering Department Brodarski institute d.o.o. Institute of Automation University of Maribor Faculty of Electrical Engineering and Computer Science

A neuro controller for high precision manoeuvring of underwater vehicles require special attention to a number of factors including thruster and vehicle’s nonlinearities, couplings which exist between various degrees of freedom as well as effects of the sea currents. The neuro control system for underwater vehicle maneouvring described here is based on the conventional controller supported with the so called adaptive neural network.

关键词： neural networks underwater vehicle trajectory guidance

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Regularization and validation of neural network models of nonlinear systems

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e & i Elektrotechnik und Informationstechnik 2000年第1期117卷 24-31页

作者： I. Petrović M. Baotić N. Perić Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation University of Zagreb Zagreb Croatia

A characteristic feature of the neural network models is the large number of parameters. A model offering many parameters usually gives rise to problems, and the variance contribution to the modeling error might be very high. Therefore, it is crucial to find the model with the optimal number of parameters. In this paper two techniques of selection of the optimal number of model parameters are described and compared: explicit and implicit regularization techniques. Model validation forms the final stage of an identification procedure with the aim of assessing objectively whether the identified model agrees sufficiently well with the observed data. In this paper the reliability of the correlation-based validation tests and the χ2-test is analyzed.

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Pole Placement controller Design Based on Fuzzy Process Model

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IFAC Proceedings Volumes 2000年第28期33卷 219-224页

作者： Robert Cupec Nedjeljko Perić Ivan Petrović Department of Control and Computer Engineering in Automation Faculty of Electrical Engineering and Computing university of Zagreb Unska 3 10000 Zagreb Croatia

An explicit self-tuning controller based on the Takagi-Sugeno fuzzy model of the process is proposed. The fuzzy model is represented as a linear regression model whose parameters are functions of some of the process variables. Such a model can be considered as a linear time-varying model whose parameter values are known at every moment. The pole placement design procedure modified for time-varying systems is applied to obtain the polynomial controller parameters that provide the desired closed-loop poles. The proposed algorithm is very simple, and thus suitable for on-line controller design in adaptive control systems.

关键词： fuzzy control fuzzy modeling adaptive control

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A Knowledge-Base Generating Fuzzy-Neural controller

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IFAC Proceedings Volumes 2000年第28期33卷 207-212页

作者： Ivan Petrović Kristijan Maček Nedjeljko Perić Department of Control and Computer Engineering in Automation Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia

This paper presents a modification to the Kandadai and Tien’s learning algorithm for tuning a fuzzy-neural controller that is able to automatically generate a knowledge base. Tuning is based on reinforcements from a dynamical system, thus giving a pseudosupervised learning scheme using error backpropagation. Originally, a weak reinforcement in the form of a binary failure signal was assumed which proved to be insufficient in terms of steady-state error. Therefore, a continuous reinforcement signal is applied enabling the system to correct the error as well as decreasing the overall control effort in the learning phase.

关键词： expert systems intelligent control fuzzy logic neural networks

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Model structure selection for nonlinear system identification using feedforward neural networks

Model structure selection for nonlinear system identificatio...

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International Joint Conference on Neural Networks (IJCNN)

作者： I. Petrovic M. Baotic N. Peric Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation University of Zagreb Zagreb Croatia

The majority of nonlinear models based on neural networks are of the black-box structure. A nonlinear system can be nonlinear in many different ways, thus the nonlinear black-box model structure must be very flexible. This means that it must have many parameters. A model offering many parameters usually creates problems, and the variance contribution to the error might be high. For a particular identification problem, only a subset of the parameters may be necessary, and the main topic in nonlinear system identification is how to select a model structure that describes the system dynamics with the minimum number of parameters. This paper discusses nonlinear input-output models that are suitable for implementation of feedforward neural networks. The proposed model structures were tested and compared using the identification procedure of a pH process. The results indicated that a simplest model structure can satisfactorily represent the investigated process.

关键词： Nonlinear systems Neural networks Feedforward neural networks Nonlinear dynamical systems control engineering computing computer networks Predictive models Automatic control Nonlinear control systems automation

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Robust stabilization of uncertain delayed systems: application to model-following systems

Robust stabilization of uncertain delayed systems: applicati...

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SICE Annual Conference

作者： K. Uahchinkul J. Ngamwiwit S. Phoojaruenchanachai Department Control Engineering Faculty of Engineeting ReCCIT KMITL Thailand Computer Automation Technolgy Laboratory NECTEC NSTDA Thailand

ISBN: (纸本)078039805X

In this paper, we present the theoretical development to stabilize a class of uncertain time-delay system and its application to model-following systems. The system under consideration is described in state space model containing state delay, uncertain parameters and disturbance. The main idea is to reduce the state of system into an equivalent one, by employing generalized transformation, which is easier to analyze its behavior and stability. Then, the min-max approach is employed to find the stabilizing control law. After that, a class of model-following system is introduced for controlling the error between the model and process. With the extended theorem, the suitable control law that guarantees model tracking is derived. Finally, two numerical simulations are illustrated to show the algorithm for applying the proposed theorems and the effectiveness of the designed control law in stabilizing the controlled systems.

关键词： Robustness Delay systems

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Adaptive Fuzzy Ship Autopilot for Track-Keeping

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IFAC Proceedings Volumes 2000年第21期33卷 129-134页

作者： Jasmin Velagic Zoran Vukic Edin Omerdic University of Sarajevo Faculty of Electrical Engineering Skenderija 70 BH-71000 Sarajevo Bosnia and Herzegovina fax : (+387 71) 654 972 University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation Unska 3 HR-10000 Zagreb Croatia fax : (+385 1) 612 98 09 University of Tuzla Faculty of Electrical and Mechanical Engineering Franjevacka 2 BH-75000 Tuzla Bosnia and Herzegovina

We develop in this paper an adaptive fuzzy gain autopilot for ship track-keeping. This autopilot is composed of Sugeno fuzzy type autopilot in an ordinary feedback loop and adjustable scaling factors mechanism in an additional feedback loop. The adjustment mechanism represents a fuzzy controller that changes scaling factors of the base fuzzy autopilot. The control system for the track-keeping is completely described. For the track-keeping problem, the maneuver of way-point turning and ship guiding through a complex path (trajectory) are presented. The influence of sea current and wave disturbances on track-keeping performance was also considered. We first present simulation results obtained by the Sugeno fuzzy type autopilot. Then, we compare those results with ones obtained by an adaptive fuzzy autopilot.

关键词： Adaptive fuzzy control track-keeping fuzzy gain concept disturbance rejection ship control

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Application of a neural predictive controller in boost converter input current control

Application of a neural predictive controller in boost conve...

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IEEE International Symposium on Intelligent control (ISIC)

作者： I. Petrovic M. Baric A. Magzan N. Peric Fac. of Electr. Eng. Zagreb Univ. Croatia Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation University of Zagreb Zagreb Croatia College of Electrical Engineering Polytechnics College of Zagreb Zagreb Croatia

The application of a nonlinear predictive controller in the cascade control structure of a boost converter is investigated. The neuro-predictive controller is realized as a nonlinear optimizer, using the Levenberg-Marquardt unconstrained optimization procedure. For the prediction of future process responses, two MLP neural networks are used. One network is used for modeling the converter dynamics and the other for online estimation of the converter's input voltage. This structure ensures good system performance in all operating regions and inherent compensation of ripples in the converter's input current caused by variations of the input voltage. The advantages of the proposed control structure are demonstrated through experimental comparison with a linear GPC with manually adjusted feedforward compensator.

关键词： Current control Voltage control control systems Automatic control Pulse width modulation Switches Rail transportation Current measurement Electric current control Application software

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Comparative analysis of Mamdani and Sugeno type fuzzy autopilots for ships

Comparative analysis of Mamdani and Sugeno type fuzzy autopi...

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1999 European control Conference, ECC 1999

作者： Vukic, Z. Velagic, J. University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering in Automation Unska 3 ZagrebHR-10000 Croatia University of Sarajevo Faculty of Electrical Engineering Skenderija 70 SarajevoBH-71000 Bosnia and Herzegovina

ISBN: (纸本)9783952417355

In this paper a comparative analysis of Mamdani and Sugeno type fuzzy autopilots for ships is given. Both autopilots have two inputs: the heading signal and the yaw rate signal, and only one output: command rudder angle. Input variable fuzzification, fuzzy associative memory (FAM) rules, and output set defuzzification are described. The control system for the course-keeping composed of the nonlinear ship, the steering gear servo system, fuzzy autopilot and wave disturbance is described. Both autopilots are compared in three different sailing conditions: without wave disturbance, with wave disturbance and with wave disturbance and wave filtering. The notch filter was used for filtering of second-order wave disturbances. The influence of wave disturbance on course-keeping performance was analyzed. Even though the obtained performance in simulations are very similar, authors prefer Sugeno type fuzzy autopilot, due to less strain of the steering gear servo system and faster and more accurate heading obtained. © 1999 EUCA.

关键词： Ships

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Friction compensation methods in position and speed control systems

Friction compensation methods in position and speed control ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： D. Stajic N. Peric J. Deur Faculty of Electrical Engineering and Computing Department or Control and Computer Engineering in Automation University of Zagreb Zagreb Croatia

Friction appears in the bearings and reduction gear of controlled electrical drives and affects the quality of the position, speed or force control. The servosystem performance can be improved by implementation of acceleration feedback. For the reason that acceleration measurement is often either not possible or practical, the implementation of acceleration estimation by an observer or differentiation of speed is proposed. Another approach to friction influence compensation is based on the disturbance observer. Its advantage is that it can be applied generally to the estimation of various disturbances. This paper deals with these friction compensation methods which are not based on friction model, but on the compensation of friction as a disturbance. The efficiency of these methods in a servodrive with Stribeck friction is compared by computer simulation and experimentally on a servosystem laboratory model.

关键词： Friction Velocity control Force control Servosystems Acceleration Accelerometers Gears Feedback computer simulation Laboratories

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