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    A Type-2 fuzzy planner with semi qualitative world model for Robocup domain
    (IEEE, 2007) Kavaklioglu, Can; Kaynak, Okyay
    The inherent uncertainty present in robotics in general and Robocup in particular demands the use of probabilistic methods. With its fuzzy constructs, Fuzzy Logic has been used as a solution for the current problems of robotics including uncertainty. Extending the use of Fuzzy Logic with Type-2 systems and high level world models should present new solutions to the robotics domain.
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    Direct Model Reference Adaptive Fuzzy Control of Networked SISO Nonlinear Systems
    (IEEE-Inst Electrical Electronics Engineers Inc, 2016) Khanesar, Mojtaba Ahmadieh; Oniz, Yesim; Kaynak, Okyay; Gao, Huijun
    This study presents a novel direct model reference fuzzy controller as applied to the control of a nonlinear system over a network subject to variable network induced time delay. The proposed method uses Pade approximation to cope with this condition. Unlike most approaches seen in the literature, which are mostly model based and necessitate the solution of a set of linear matrix inequalities, the proposed approach is online and can be applied to nonlinear systems with a little knowledge about the structure of the system and the values of its parameters. The stability of the proposed method is proved using an appropriate Lyapunov function. The approach is implemented and tested on a dc motor with nonlinear characteristics and nonlinear state-dependent disturbance. It is shown that it is capable of controlling the system over a network subject to variable network-induced time delay with bounded tracking error. In addition, the effect of packet losses is considered in the implementation part and it is seen that the system can be controlled under these conditions too.
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    Wheel Slip Regulation Using Fuzzy Spiking Neural Networks
    (IEEE, 2016) Oniz, Yesim; Kaynak, Okyay
    In this paper, a fuzzy spiking neural network structure is developed for the wheel slip regulation problem of an Antilock Braking System. Sliding mode control theory is utilized in the derivation of the update rules for the neural network's weights as well as the parameters of the fuzzy membership functions. Gaussian membership functions are used to convert the sensor readings into the neural networks inputs and the spike response model is employed to denote the effect of the incoming spikes on the postsynaptic membrane potential. The use of the Lyapunov stability method for the derivation of the parameter update rules leads to a stable system response even in the existence of external disturbances.