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Yayın Design of a global extremum seeking algorithm for an omni-directional robot model(Romanian Soc Control Tech Informatics, 2017-06) Dinçmen, ErkinA global extremum seeking algorithm is developed for a mobile robot model where the aim is to find the location of the most powerful signal source among the others. In other words, the control problem is to seek the global extremum point of a performance function when there are local extremas. The locations of the signal sources and signal distribution characteristics are unknown, i.e. the gradient of the performance function is unknown. The control algorithm also doesn't use any position measurement of the mobile robot itself. Henceforth, the controller is suitable for the missions where the robot moves in an unknown terrain with no GPS signal and no inertial measurements. Only the signal magnitude should be measured via a sensor mounted on the robot during the motion. A gradient estimator is designed to determine the motion direction towards the extremum point. When a local extremum is found, the robot will continue its search for another extremum points. Once each extremums have been visited, the robot will compare the signal levels on each source and identify the global extremum i.e. the most powerful signal source. In the absence of any position measurements, the robot can move towards the global extremum by repeating its motion history backwards. In the literature, this is the first global extremum seeking algorithm that has been developed for an omni-directional mobile robot model. Via the simulation studies it has been shown that the control algorithm can seek and find both stationary and non stationary signal sources and it can find the global extremum point when there are local extremas.Yayın Extremum seeking dead-zone pre-compensator for an industrial control system(Walter De Gruyter GMBH, 2018-06-26) Dinçmen, ErkinPID type industrial controllers such as PI, PD, PID are mature control algorithms and they are intensively used in industry due to their simplicity and easily implementability. However, they start to fail when there is an unknown or unpredictable nonlinear behavior in the plant or actuator. In this paper, a novel compensation algorithm is proposed for PD type industrial control systems, which possess an unknown dead-zone nonlinearity. An extremum-seeking technique is utilized in the compensation algorithm. The aim is to propose a new, effective and robust compensator which can be added easily to an existing industrial controller without any need to change/retune the controller settings/parameters. It is shown that by adding the compensator to an existing PD control system, the sensitivity of the controller to the dead-zone nonlinearity is removed.Yayın Extremum seeking control of uncertain systems(Işık University Press, 2017) Dinçmen, ErkinExtremum seeking is used in control problems where the reference trajectory or reference set point of the system is not known but it is searched in real time in order to maximize or minimize a performance function representing the optimal behaviour of the system. In this paper, extremum seeking algorithm is applied to the systems with parametric uncertainties.Yayın Neural network steering control algorithm for autonomous ground vehicles having signal time delay(SAGE Publications Ltd, 2024-03) Dinçmen, ErkinAn adaptive neural network–based steering control algorithm is proposed for yaw rate tracking of autonomous ground vehicles with in-vehicle signal time delay. The control system consists of two neural networks: the observer neural network and the controller neural network. The observer neural network adapts itself to the system dynamics during the training phase. Once trained, the observer neural network cooperates with the controller neural network, which constantly adapts itself during the control task. In this way, an adaptive and intelligent control structure is proposed. Through simulation studies, it has been shown that while a proportional-integral-derivative type steering controller fails to perform its control task in case of steering signal delay, the proposed control algorithm manages to adapt itself according to the control problem and achieves reference yaw rate tracking. The robustness of the control algorithm according to the signal delay magnitude has been demonstrated by simulation studies. A rigorous Lyapunov stability analysis of the control algorithm is also presented.
