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  • 1
    Electronic Resource
    Electronic Resource
    An Adaptive Control Using Fuzzy Basis Function Expansions for a Class of Nonlinear Systems (1998)
    Springer
    Journal of intelligent and robotic systems 21 (1998), S. 257-275 
    Details
    ISSN: 1573-0409
    Keywords: fuzzy basis function ; adaptive control ; nonlinear systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract An adaptive control using fuzzy basis function expansions is proposed for a class of nonlinear systems in this paper. It is shown that two system uncertainty bounds are approximated in a compact set by using fuzzy basis function expansion networks in the Lyapunov sense, and the outputs of the fuzzy networks are then used as the parameters of the controller to adaptively compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to unknown system dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can be guaranteed to asymptotically converge to zero. Simulation results are provided to demonstrate the effectiveness, simplicity and practicality of the proposed control scheme.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007962117548
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Direct production of ultrafine amorphous Fe-P alloy particles by the plasma method (1995)
    Springer
    Journal of materials science 30 (1995), S. 2911-2920 
    Details
    ISSN: 1573-4803
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Ultrafine amorphous Fe-P alloy particles were directly synthesized by the plasma using cyclopentenyl iron and phosphorous trichloride as starting materials. The plasma gas greatly influenced the morphology, dispersion and composition.The particles were roughly spherical with a diameter of 40–200 nm and had the composition of Fe106P50. Elemental chlorine was found in the surface of the particle especially prepared under argon plasma conditions. It was bonded with phosphorus and carbon in Fe-P particles prepared in argon plasma and mainly with carbon in Fe-P particles deposited under a hydrogen plasma. Formation of Fe-P improves the stability of phosphorus in air. Phosphorus enrichment in the surface of Fe-P particles was also found. The particles were characterized by TEM, SEM, infrared-spectroscopy, X-ray photoelectron spectroscopy quantitative analysis, differential scanning colorimetry, inductively coupled plasma, X-ray diffraction and X-ray microprobe analysis. The formation mechanism of Fe-P amorphous particles was also discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00349663
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  • 3
    Electronic Resource
    Electronic Resource
    Automated fuzzy knowledge acquisition with connectionist adaptation (1996)
    Springer
    Neural computing & applications 4 (1996), S. 27-34 
    Details
    ISSN: 1433-3058
    Keywords: Fuzzy logic ; Genetic algorithms ; Knowledge acquisition ; Learning ; Neural networks ; Optimisation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper presents an automated knowledge acquisition architecture for the truck docking problem. The architecture consists of a neural network block, a fuzzy rule generation block and a genetic optimisation block. The neural network block is used to quickly and adaptively learn from trials the driving knowledge. The fuzzy rule generation block then extracts the driving knowledge to form a knowledge rule base. The driving knowledge rule base is further optimised in the genetic optimisation block using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413867
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  • 4
    Electronic Resource
    Electronic Resource
    An RBF neural network-based adaptive control for SISO linearisable nonlinear systems (1998)
    Springer
    Neural computing & applications 7 (1998), S. 71-77 
    Details
    ISSN: 1433-3058
    Keywords: Adaptive control ; Linearisable nonlinear system ; Lyapunov stability ; RBF neural network ; Uncertain dynamics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract An RBF neural network-based adaptive control is proposed for Single-Input and Single-Output (SISO) linearisable nonlinear systems in this paper. It is shown that a SISO nonlinear system is first linearised by using the differential geometric approach in the state space, and the linearised nonlinear system is then treated as a partially known system. The known dynamics are used to design a nominal feedback controller to stabilise the nominal system, and an adaptive RBF neural network-based compensator is then designed to compensate for the effects of uncertain dynamics. The main function of the RBF neural network in this work is to adaptively learn the upper bound of the system uncertainty, and the output of the neural network is then used to adaptively adjust the gain of the compensator so that the strong robustness with respect to unknown dynamics can be obtained, and the tracking error between the plant output and the desired reference signal can asymptotically converge to zero. A simulation example is performed in support of the proposed scheme.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413711
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  • 5
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    Direct production of ultrafine amorphous Fe-P alloy particles by the plasma method (1995)
    Springer
    Details
    Publication Date: 1995-06-01
    Print ISSN: 0022-2461
    Electronic ISSN: 1573-4803
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Published by Springer
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  • 6
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    Markovian real-time adaptive control of signal systems (1995)
    Elsevier
    Details
    Publication Date: 1995-08-01
    Print ISSN: 0895-7177
    Electronic ISSN: 1872-9479
    Topics: Computer Science , Mathematics
    Published by Elsevier
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