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1

A class of nonlocal integrodifferential equations via fractional derivative and its mild solutions

Wang J., Yan X., Zhang X. H., Wang T. M, Li X. Z.

Opuscula Mathematica

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2011

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Vol. 31, no. 1

119-135

EN

In this paper, we discuss a class of integrodifferential equations with nonlocal conditions via a fractional derivative of the type: [formula]. Some sufficient conditions for the existence of mild solutions for the above system are given. The main tools are the resolvent operators and fixed point theorems due to Banach's fixed point theorem, Krasnoselskii's fixed point theorem and Schaefer's fixed point theorem. At last, an example is given for demonstration.

2

Real-time experiments with discrete sliding-mode control enhanced by AI components

Sha D. H., Bajić V. B., Li X. Z., Wang X.

Journal of Applied Computer Science

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2000

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Vol. 8, nr 2

99-117

EN

The paper presents some experimental results of a real-time implementation of discrete sliding mode control combined with a fuzzy-logic damper block. The hybrid real-time algorithm developed is aimed at applications to discrete systems. The sliding mode pan of the solution is based on system input and output measurements. The algorithm combines an integral action, a nonlinear output feedback, an adjustable sliding mode, and a fuzzy logic damping control block. Design of the sliding mode controller is based on the asymptotic reaching law method. The determination of the integral gain and the coefficients of the sliding mode hyper-plane are made by a pole assignment procedure. The paper presents both the simulation results and the results of laboratory real-time control experiments. The real-time control experiments are made with the motor speed control system using an DSP based hardware (TMS230C31/60 MHz). The results obtained demonstrate the robustness and superiority of the proposed solution in the motor speed control, as compared to the solution based on the optimally tuned PI controller.

3

Neural network control with fuzzy logic compensation

Li X. Z., Bajić V. B., Sha D. H., Wang H. Y.

Journal of Applied Computer Science

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2000

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Vol. 8, nr 2

31-44

EN

This paper presents a new AI based control strategy. A dynamic neural network is used to identify the plant on-line. The control signal is then calculated iteratively according to the responses of a reference model and the identified neural model of the process. A fuzzy logic block with four very simple rules is added to the loop to improve the overall loop properties. This synergetic AI control paradigm is tested via simulation. The results demonstrate that the proposed control strategy provides better disturbance rejection and tracking properties of the control loop than those achieved by an optimally tuned PID controller.