Yazar "Tatlicioglu, Enver" seçeneğine göre listele

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    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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    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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    Adaptive control of BLDC driven robot manipulators in task space
    (Wiley, 2024) Unver, Sukru; Selim, Erman; Tatlicioglu, Enver; Zergeroglu, Erkan; Alci, Musa
    In this study, task space tracking control of robot manipulators driven by brushless DC (BLDC) motors is considered. Dynamics of actuators are taken into account and the entire electromechanical system (i.e. kinematic, dynamic, and electrical models) is assumed to include parametric/structured uncertainties. A novel adaptive controller is designed and the stability of the closed loop system is ensured via novel Lyapunov type tools. To demonstrate performance and applicability of the proposed method, a simulation study is conducted using the model of a two degree of freedom, planar robotic manipulator driven by BLDC motors.
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    Adaptive fuzzy logic with self-adjusting membership functions based tracking control of surface vessels
    (Pergamon-Elsevier Science Ltd, 2022) Tatlicioglu, Enver; Yilmaz, Bayram Melih; Savran, Aydogan; Alci, Musa
    Tracking control of surface vessels is aimed where the dynamical model includes uncertainties. In an attempt to design a broadly applicable control methodology, a model independent strategy is pursued. The dynamical uncertainties are modeled via a fuzzy logic network. In the design of the controller, proportional derivative feedback was made use of in conjunction with self-adjusting fuzzy logic compensation term obtained by adaptively updating control representative value matrix along with centers and widths of the membership value vector. Stability of the closed loop system is investigated through novel Lyapunov-based arguments and semi-global practical tracking is guaranteed. Numerical simulation studies are performed that support theoretical findings and demonstrate the effectiveness of the proposed method.
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    Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators
    (Elsevier, 2021) Yilmaz, B. Melih; Tatlicioglu, Enver; Savran, Aydogan; Alci, Musa
    With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning is designed. The stability is investigated via Lyapunov type techniques where global uniform ultimate boundedness of closed loop system is guaranteed. Numerical simulation results obtained from a two degree of freedom robot manipulator model and experiments performed on a robot manipulator demonstrate the efficacy of the proposed control methodology. (C) 2021 Elsevier B.V. All rights reserved.
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    Adaptive robust control of marine vehicles with periodic disturbance compensation: an observer-based output feedback approach
    (Springer Japan Kk, 2022) Kurtoglu, Deniz; Bidikli, Baris; Tatlicioglu, Enver; Zergeroglu, Erkan
    This work concentrates on output feedback trajectory tracking control of marine vehicles with dynamical uncertainties. The system under consideration, due to the natural response of oceanic waves, is also subject to periodic external disturbances. The output feedback structure of the proposed controller algorithm is established via a novel nonlinear model-free observer in conjunction with a Fourier series expansion-based periodic disturbance estimator. Lyapunov-based arguments have been utilized to prove the stability of the closed loop system and the convergence of tracking and unmeasured state observation errors under the closed loop operation. Performance demonstration and viability of the proposed method are realized via comparative numerical simulations.
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    A composite adaptive tracking controller for dynamically positioned surface vessels with only position measurements
    (Pergamon-Elsevier Science Ltd, 2022) Aktas, Unal; Tatlicioglu, Enver; Zergeroglu, Erkan
    This work presents an alternative solution to the tracking control of dynamically positioned surface vessels having uncertainties associated with their dynamical model while using only vessel's position measurements. Specifically, a surrogate state filter in conjunction with a composite parameter estimation algorithm is applied to ensure semi-global asymptotic convergence of the states of the vessel to their respective desired values. As opposed to its solely gradient based counterparts, the proposed algorithm utilizes an update law that is composed of a gradient update law driven by position tracking error and a least squares update law driven by the prediction error. This enables the proposed controller to compensate for the model uncertainties in a relatively shorter period of time. Rigorous analysis based on Lyapunov type approaches are applied in order to ensure the stability and boundedness of the closed loop system. Comparative simulations performed on the model of a surface vessel are presented in order to illustrate the tracking, and parameter estimation performance of the proposed controller/adaptation algorithm.
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    Desired model compensation-based position constrained control of robotic manipulators
    (Cambridge Univ Press, 2022) Gul, Samet; Zergeroglu, Erkan; Tatlicioglu, Enver; Kilinc, Mesih Veysi
    This work presents the design and the corresponding stability analysis of a desired model-based, joint position constrained, controller formulation for robotic manipulators. Specifically, provided that the initial joint position tracking error signal starts within some predefined region, the proposed controller ensures that the joint tracking error signal remains inside this region and approaches to zero asymptotically. Extensive numerical simulations and experimental studies performed on a two-link direct-drive planar robot are provided in order to illustrate the effectiveness and feasibility of the proposed controller.
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    Inverse optimal adaptive output feedback control of a class of Euler-Lagrange systems: A nonlinear filter based approach
    (Sage Publications Ltd, 2022) Aksoy, Orhan; Zergeroglu, Erkan; Tatlicioglu, Enver
    In this paper, we present an inverse optimal tracking controller for a class of Euler--Lagrange systems having uncertainties in their dynamical terms under the restriction that only the output state (i.e. position for robotic systems) is available for measurement. Specifically, a nonlinear filter is used to generate a velocity substitute, then a controller formulation ensuring a globally asymptotically stable closed-loop system while minimizing a performance index despite the presence of parametric uncertainty, is proposed. The stability proof is established using a Lyapunov analysis of the system with proposed optimal output feedback controller. Inverse optimality is derived via designing a meaningful cost function utilizing the control Lyapunov function. Numerical simulations are presented to illustrate the viability and performance of the derived controller.
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    Observer based output feedback repetitive learning control of robotic manipulators
    (Wiley, 2024) Dogan, Kadriye Merve; Tatlicioglu, Enver; Zergeroglu, Erkan; Cetin, Kamil
    This work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model-free joint velocity observer design. The stability of the closed-loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer-controller couple. When compared with the relevant past works, the main innovation established by this work is the elimination of the need for velocity measurements from the family of saturation function based repetitive controllers which is an application-oriented control methodology introduced to deal with periodic disturbances. image
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    Output tracking control of an aircraft subject to additive state dependent disturbance: an optimal control approach
    (Polska Akad Nauk, Polish Acad Sciences, 2021) Tanyer, Ilker; Tatlicioglu, Enver; Zergeroglu, Erkan
    In this paper, model reference output feedback tracking control of an aircraft subject to additive, uncertain, nonlinear disturbances is considered. In order to present the design steps in a clear fashion: first, the aircraft dynamics is temporarily assumed as known with all the states of the system available. Then a feedback linearizing controller minimizing a performance index while only requiring the output measurements of the system is proposed. As the aircraft dynamics is uncertain and only the output is available, the proposed controller makes use of a novel uncertainty estimator. The stability of the closed loop system and global asymptotic tracking of the proposed method are ensured via Lyapunov based arguments, asymptotic convergence of the controller to an optimal controller is also established. Numerical simulations are presented in order to demonstrate the feasibility and performance of the proposed control strategy.
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    A Passivity-based Decomposing Method for Operational Space Control of Kinematical Redundant Tele-operation Systems
    (Romanian Soc Control Tech Informatics, 2021) Cetin, Kamil; Tatlicioglu, Enver
    In the passivity-based decomposing method, a bilateral tele-operation system is virtually decomposed into 2 sub-systems (shape/locked) to ensure coordination between the master and slave robots and to provide a general referenced motion of the closed-loop bilateral tele-operation along with the passivity of the master and slave robots. So far, the passivity-based decomposing methods in the literature have been studied only for the joint-space control of tele-operation systems with kinematical similar master and slave robots. in this study, a passivity-based decomposing method is proposed for operational space control of bilateral tele-operation systems with kinematic redundancy in the slave robot. The main objectives of the proposed method are to ensure operational space coordination between the robots' end-effector trajectories and to achieve a referenced general movement of the closed-loop tele-operation system. in addition, the kinematic redundancy of the slave robot, which usually complicates the control problem, is turned into an advantage, and secondary tasks are designed for the slave robot. Moreover, experiments are carried out to validate the achievement of the proposed method using a kinematical redundant tele-operation setup.
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    Periodic disturbance estimation based adaptive robust control of marine vehicles
    (Pergamon-Elsevier Science Ltd, 2021) Kurtoglu, Deniz; Bidikli, Baris; Tatlicioglu, Enver; Zergeroglu, Erkan
    Tracking control of marine vessels in the presence of parametric uncertainty and additive periodic disturbances is considered. For optimal estimation of environmental forces, periodic disturbance estimation method inspired from Fourier series expansion have been applied. Stability of the closed-loop system and the convergence of the tracking error under the closed-loop operation are established via Lyapunov based arguments. Simulation studies are provided to support the theoretical results and the effectiveness of the proposed method.
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    Position Tracking Constrained Adaptive Output Feedback Control of Robotic Manipulators
    (Asme, 2022) Gul, Samet; Zergeroglu, Erkan; Tatlicioglu, Enver
    This work presents the design and the corresponding stability analysis of a model-based, joint position tracking error constrained, adaptive output feedback (OFB) controller for robot manipulators. Specifically, provided that the initial joint position tracking error starts within a predefined region, the proposed controller algorithm ensures that the joint tracking error remains inside this region and asymptotically approaches zero, despite the lack of joint velocity measurements and uncertainties associated with the system dynamics. The constraint imposed on the position tracking error term ensures predictable overshoot for the overall system and enables a predetermined transient performance. The need for the joint velocity measurements is removed via the use of a surrogate filter formulation in conjunction with the use of desired model compensation. The stability and the convergence of the closed-loop system are proved via a barrier Lyapunov function (BLF)-based argument. Extensive numerical simulations and experimental studies performed on a two-link, direct-drive robotic manipulator are provided to illustrate the feasibility and effectiveness of the proposed method.
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    REPETITIVE CONTROL OF ROBOTIC MANIPULATORS IN OPERATIONAL SPACE: A NEURAL NETWORK-BASED APPROACH
    (Acta Press, 2022) Cobanoglu, Necati; Yilmaz, B. Melih; Tatlicioglu, Enver; Zergeroglu, Erkan
    This work tackles the control problem for robotic manipulators with kinematic and dynamical uncertainties where the end-effector robot is required to perform repetitive tasks. Specifically, a neural network-based estimator and an adaptive component have been fused with a repetitive learning controller-based update rule to compensate for the uncertainties in the robot dynamics and parametrically uncertain kinematics. The closed-loop system stability and tracking of periodic desired operational space position vector are ensured via Lyapunov-type analysis. Experiment results obtained from a planar robotic manipulator are presented to demonstrate the feasibility of the proposed control methodology.
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    Robust backstepping control of robotic manipulators actuated via brushless DC motors
    (Springernature, 2024) Saka, Irem; Unver, Sukru; Selim, Erman; Tatlicioglu, Enver; Zergeroglu, Erkan
    This paper introduces a novel integrator backstepping-based sliding mode controller for robot manipulators equipped with brushless DC motors. Our control design explicitly incorporates the intricate dynamics of actuators, enabling robust performance even in the presence of uncertainties in models of both dynamic and electrical subsystems. Rigorous stability analysis using Lyapunov techniques ensures boundedness of all signals under the closed-loop operation and guarantees global asymptotic stability of the joint position tracking error. Furthermore, an experimental study is conducted to demonstrate the effectiveness of the proposed method, utilizing an in-house developed two degree of freedom planar robot manipulator actuated by brushless DC motors.
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    Robust Prescribed Time Control of Euler–Lagrange Systems
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) 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.
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    Robust State/Output-Feedback Control of Robotic Manipulators: An Adaptive Fuzzy-Logic-Based Approach With Self-Organized Membership Functions
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Yilmaz, B. Melih; Tatlicioglu, Enver; Savran, Aydogan; Alci, Musa
    This article aims to design a joint space tracking controller for robotic manipulators having uncertainties in their mathematical representations under the additional constraint that joint velocity sensing not being available. A two-part design is followed where in the first part, the modeling uncertainties are dealt with a self-organized adaptive fuzzy-logic (AFL)-based controller where full-state feedback (FSFB) is assumed. The stability analysis yields semiglobally uniformly ultimately bounded tracking results. In the second part, a high-gain joint velocity observer is designed followed by replacing error vectors in the FSFB controller with their saturated versions obtained from the observer design to arrive at a self-organized AFL-based robust output-feedback controller. The stability analysis is performed via a multiple-step Lyapunov-type method where the semiglobal uniform ultimate boundedness of the tracking error is ensured. Comparative experiment results obtained from a planar robotic manipulator are presented to demonstrate the efficacy of the proposed control methodology.
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    Robust task space position constraint control of kinematically redundant manipulators: A barrier Lyapunov approach
    (Sage Publications Ltd, 2024) Gul, Samet; Zergeroglu, Erkan; Tatlicioglu, Enver
    This study introduces a novel robust control strategy for kinematically redundant robotic manipulators, aimed at maintaining task space position errors within predefined constraints. The proposed design ensures that the tracking errors for both the end-effector's position and sub-tasks are uniformly ultimately bounded, despite dynamic uncertainties. It also guarantees that the position error of the end-effector stays within a predetermined safe region and achieves predictable overshoot and transient performance, provided the initial error is bounded within this safe area. The incorporation of a BLF into our stability analysis is the cornerstone of our methodological advancement, which is crucial for imposing predefined constraints on task space position errors. Simulations and experiments confirm the controller's effectiveness, showing consistent error maintenance, stability of error terms, and boundedness of closed-loop signals. Tighter constraints increased control effort but led to faster convergence and improved tracking performance.
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    Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators in Task Space
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Yilmaz, B. Melih; Tatlicioglu, Enver; Savran, Aydogan; Alci, Musa
    End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this article. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed-loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented.