Lyapunov-based output feedback learning control of robot manipulators

Dogan, K. Merve; Tatlıcıoğlu, Enver; Zergeroğlu, Erkan; Cetin, Kamil

doi:10.1109/acc.2015.7172173

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…An adaptive learning proportional derivative type controller that learns the input reference signal by identifying its Fourier coefficients was presented in [7]. Recently, in [9], Dogan et al designed an output feedback learning controller for joint tracking.…”

Section: Introductionmentioning

confidence: 99%

Operational/task space learning control of robot manipulators with dynamical uncertainties

Dogan

Tatlıcıoğlu

Zergeroğlu

2015

2015 IEEE Conference on Control Applications (CCA)

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In this work, we consider the problem of operational/task space tracking control of a robot manipulator where a periodic desired end-effector pose is to be tracked. Specifically, we designed a repetitive learning controller that guarantees asymptotic end-effector tracking of periodic trajectories (with known period) while "learning" the overall uncertainties in the system dynamics. The proposed controller does not make use of the inverse kinematic formulation on the position level and the stability of the closed-loop system is guaranteed via Lyapunov based arguments. Numerical studies are conducted on a two link planar robot are presented to illustrate the performance and viability of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Operational/task space learning control of robot manipulators with dynamical uncertainties

Dogan

Tatlıcıoğlu

Zergeroğlu

2015

2015 IEEE Conference on Control Applications (CCA)

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“…Recently a model independent observer based controller was presented in [4]. Repetitive learning type controller approaches, where the desired trajectory needs to be periodic, were also extended to output feedback tracking control of robotic devices [5]. Filtered based approaches, where a velocity surrogate signal generated by a filter formulation is used instead of the actual velocity signal were also applied to the output feedback control of robotic manipulators [6].…”

Section: Introductionmentioning

confidence: 99%

Position Constrained, Adaptive Control of Robotic Manipulators without Velocity Measurements

Gul,

Zergeroglu,

Tatlicioglu

2021

Preprint

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This work presents the design and the corresponding stability analysis of a model based, joint position tracking error constrained, adaptive output feedback 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 to zero, despite the lack of joint velocity measurements and uncertainties associated with the system dynamics. The need for the joint velocity measurements are 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 based argument. A simulation performed on a two-link robotic manipulator is provided in order to illustrate the feasibility and effectiveness of the proposed method.

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“…Consider the general form of infinite-horizon nonlinear regulator problem that minimizes the performance measure [11]: (12) with respect to the system state x and control input u subject to the nonlinear differential constraint:…”

Section: Sdre Regulator Problemmentioning

confidence: 99%

“…There are several control schemes applied in the past for trajectory tracking and vibration suppression of FLM based on nonlinear models. These control schemes include feedback linearization based techniques [7], Lyapunov function based control [12], recursive back stepping [9] and nonlinear H ∞ control [4]. SDRE based techniques provide a systematic method to design nonlinear controllers.…”

Section: Introductionmentioning

confidence: 99%

Vibration Control of Flexible Link Manipulator Using SDRE Controller and Kalman Filtering

Reddy¹,

Jacob²

2017

SIC

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Abstract:The problem of estimating the flexural states while applying State-Dependent Riccati Equation (SDRE) technique to flexible link manipulator (FLM) is the focus of this paper. The proposed method investigates the effect of employing Kalman Filter as state estimator in the case of the deterministically modelled FLM. The dynamic model of the FLM is derived through combined Euler Lagrangian-Assumed modes approach based on two significant modes, resulting in a nonlinear model with six states. The information of states being crucial to the imple mentation of SDRE controller, the state estimator based on Kalman filter designed in this paper, minimizes the effect of noises that may corrupt the state measurements. Simulation results reveal the effectiveness of Kalman filter based SDRE controller for accurate positioning and vibration suppression of the FLM.

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Lyapunov-based output feedback learning control of robot manipulators

Cited by 6 publications

References 21 publications

Operational/task space learning control of robot manipulators with dynamical uncertainties

Operational/task space learning control of robot manipulators with dynamical uncertainties

Position Constrained, Adaptive Control of Robotic Manipulators without Velocity Measurements

Vibration Control of Flexible Link Manipulator Using SDRE Controller and Kalman Filtering

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