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    Öğe
    Adaptive Cartesian space control of robotic manipulators: A concurrent learning based approach☆
    (Pergamon-Elsevier Science Ltd, 2024) Obuz, Serhat; Tatlicioglu, Enver; Zergeroglu, Erkan
    This work introduces a concurrent learning -based adaptive control design for end -effector tracking and the corresponding stability analysis for robotic manipulators. The presented controller is developed directly in Cartesian space, thereby removing the necessity for inverse kinematics calculations at the position level. The designed adaptive controller ensures global exponential tracking of the end -effector in Cartesian space. Moreover, the developed controller assures globally exponential convergence of uncertain dynamical parameters to their actual values without demanding persistence excitation conditions via a combination of a standard gradient -based adaptation with a novel integral concurrent learning component. The developed integral concurrent learning part operates both real-time output data and the most informative historical data gathered by employing the singular -value maximization algorithm (SVMA) to reduce the size of memory allocation. Lyapunov-based arguments are applied to ensure the exponential stability of the closed -loop system. Numerical studies are performed to depict the feasibility and performance of the proposed design.
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    Öğe
    Adaptive control of A class of nonlinear systems with guaranteed parameter estimation: A concurrent learning based approach
    (Wiley, 2024) Obuz, Serhat; Zergeroglu, Erkan; Tatlicioglu, Enver
    Recent advances in concurrent learning based adaptive controllers have relaxed the persistency of excitation condition required to achieve exponential tracking and parameter estimation error convergence. This was made possible via the use of additional concurrent learning stacks in the parameter estimation algorithm. However, the proposed concurrent learning components, that is, the history stacks, needed to be filled with selected values dependent on the actual system states. Therefore, the previously proposed concurrent learning adaptive controllers required the system to be stable initially for a finite time so that the corresponding history stacks can be filled (finite excitation condition). In this work, motivated to remove the finite excitation condition, a novel desired system state based concurrent learning adaptive controller is proposed. In order to remove the system state dependencies in the controller and estimation algorithms, a filtered version of the dynamics and a novel prediction error formulation have been designed. The overall exponential stability, parameter error convergence and boundedness of the system states during closed loop operations are ensured via Lyapunov based arguments. The main advantages of the proposed method are its dependence on the desired system states and the overall stability results that paved the way in removing the need for finite excitation condition. Numerical studies performed on a two link robotic device are also presented to illustrate the feasibility of the proposed method. Significant research is achieved by ensuring output tracking along with accurate identification of uncertain parametric uncertainties. In a novel departure from the existing literature, the need for persistency of excitation condition is eliminated. image
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    Öğe
    Robust Prescribed Time Control of Euler–Lagrange Systems
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Obuz, Serhat; Selim, Erman; Tatlicioglu, Enver; Zergeroglu, Erkan
    This article aims to develop a robust prescribed time controller for precise trajectory tracking for uncertain Euler-Lagrange systems with unknown time-varying disturbances without prior knowledge of their upper bounds. The control strategy involves utilizing a scaled transformation function to map the standard error system to a scaled error system. The presented controller is developed based on the scaled error system, incorporating state-dependent control gains and yielding a model-free controller structure. Distinguishing from previous methods, the designed controller takes a different approach by avoiding the direct multiplication of feedback terms with the estimated inertia matrix. The developed strategy mitigates the adverse effects of mismatches between the actual and estimated inertia matrices. A novel Lyapunov-based stability analysis is employed to establish fixed-time input-to-state stability within the prescribed time and to ensure the convergence of error signals to the origin. Experimental validation on a three-degree-of-freedom planar robot arm confirms the effectiveness of the proposed controller.