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Yayın An emergency braking controller based on extremum seeking with experimental implementation(Springer Berlin Heidelberg, 2018-03-01) Dinçmen, Erkin; Altınel, TunçAn extremum seeking scheme is developed for maximizing the longitudinal tire forces of the road vehicles during emergency braking situations. If the road condition is known, then a conventional braking controller could generate required braking moment to track the slip set point which belongs to that road condition. However, estimating the road condition is not an easy task and it brings additional computation effort. Rather than that, a self optimization algorithm is presented in this paper without relying on road condition estimation. The developed controller searches optimum operation point for getting maximum friction force. Computer simulations show the effectiveness of the self optimization routine. To validate the real time applicability of the algorithm, an electromechanical braking test system is used for the experiments. Due to the limited measurements from the experimental system, force and moment observers are designed to calculate necessary control inputs for maximizing the friction potential, i.e. the braking force. Via the experimental study, it has been shown that the developed self optimizing controller is fast, accurate, and operable on a real braking system.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.Yayın A cooperative neural network control structure and its application for systems having dead-zone nonlinearities(Springer International Publishing Ag, 2022-03) Dinçmen, ErkinAn adaptive control structure utilizing two feed-forward neural networks (NN) is proposed to deal with systems having unknown nonlinearities. One of the networks is trained to mimic the nonlinear system dynamics. Its training will be repeated with periods in order to keep it an updated valid model of the system all the times since the parameters and/or nonlinearities of the system may change during time. The other network, which is the Controller NN, adapts itself continuously by collaborating with the Model NN. The stability-convergence analysis of both networks is performed via Lyapunov method. An example system is chosen to show the applicability of the control algorithm. This example system is created by combining a linear dynamics model with a dead-zone function to represent a nonlinear system to be controlled. It should be noted that the proposed control structure can be used in any nonlinear system without knowing the system dynamics. The only information required by Model NN is the training set consisting input-output data pairs of the system. The Model NN is trained offline with this training set, and afterward the Controller NN adapts its weights online continuously during the control task with the help of Model NN. The performances of PD and PID controllers are also given for comparison purposes.
