Robust leaning control

Heinzinger, G.; Fenwick, David R.; Paden, B.; Miyazaki, Fumio

doi:10.1109/cdc.1989.70152

Cited by 60 publications

(68 citation statements)

References 7 publications

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“…where L : ℜ m × [0, T ] → ℜ r×m is a bounded learning operator, y d (t) ≡ y 0 (t) and γ ∈ [0.1) allows the influence of a bias term [16]. Equation (5) describes a standard D-type (Differential-type) NLIC for a class of nonlinear time-varying systems (Eq.…”

Section: A Sufficient Condition For Convergence and Robustness Of Ilcmentioning

confidence: 99%

“…and the initial state error (4)) is presented in [16,17]. Consequently, the learning operator remains to be specified so that the sufficient condition for robustness and convergence, Eq.…”

Section: ) Is the Corresponding Input And X D (T) Is The Correspondinmentioning

confidence: 99%

“…Equation (7) describes a PD-type ILC. It has to be mentioned that for robust convergence the update law (7) and (5) must contain derivatives of the outputẏ d −ẏ l [16].…”

Section: Corollary 1 [16] If the Update Law (5) Is Replaced Withmentioning

confidence: 99%

“…In addition, the following assumptions hold [16,17]: Let us consider the following learning update law…”

Section: A Sufficient Condition For Convergence and Robustness Of Ilcmentioning

confidence: 99%

“…We consider a class of multi-input-multi-output nonlinear time-varying systems described by the following state-space equations [16,17]:…”

Section: A Sufficient Condition For Convergence and Robustness Of Ilcmentioning

confidence: 99%

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Iterative learning control with sampled-data feedback for robot manipulators

Delchev¹,

Boiadjiev²,

Kawasaki³

et al. 2014

Archives of Control Sciences

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This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached.

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Section: A Sufficient Condition For Convergence and Robustness Of Ilcmentioning

confidence: 99%

“…and the initial state error (4)) is presented in [16,17]. Consequently, the learning operator remains to be specified so that the sufficient condition for robustness and convergence, Eq.…”

Section: ) Is the Corresponding Input And X D (T) Is The Correspondinmentioning

confidence: 99%

“…Equation (7) describes a PD-type ILC. It has to be mentioned that for robust convergence the update law (7) and (5) must contain derivatives of the outputẏ d −ẏ l [16].…”

Section: Corollary 1 [16] If the Update Law (5) Is Replaced Withmentioning

confidence: 99%

“…In addition, the following assumptions hold [16,17]: Let us consider the following learning update law…”

Section: A Sufficient Condition For Convergence and Robustness Of Ilcmentioning

confidence: 99%

“…We consider a class of multi-input-multi-output nonlinear time-varying systems described by the following state-space equations [16,17]:…”

Section: A Sufficient Condition For Convergence and Robustness Of Ilcmentioning

confidence: 99%

See 3 more Smart Citations

Iterative learning control with sampled-data feedback for robot manipulators

Delchev¹,

Boiadjiev²,

Kawasaki³

et al. 2014

Archives of Control Sciences

View full text Add to dashboard Cite

show abstract

Iterative learning control for robotic manipulators: A bounded‐error algorithm

Delchev

2013

Adaptive Control & Signal

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This paper presents a model-based nonlinear iterative learning control (NILC) for nonlinear multiple-input and multiple-output mechanical systems of robotic manipulators. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory-tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. Both standard and bounded-error learning control laws with feedback controllers attached are considered. The NILC synthesis is based on a dynamic model of a six degrees of freedom robotic manipulator. The dynamic model includes viscous and Coulomb friction and input generalized torques are bounded. With respect to the bounded-error and standard learning processes applied to a virtual PUMA 560 robot (Unimation, Inc. Danburry, CT, USA), simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control.

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Learning Control of Robot Manipulators in Task Space

Dogan

Tatlıcıoğlu

Zergeroğlu

et al. 2017

Asian Journal of Control

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Two important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end-effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which "learns" the overall uncertainties in the robot manipulator dynamics. The stability of the closed-loop system and asymptotic end-effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in-house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller.

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Robust leaning control

Cited by 60 publications

References 7 publications

Iterative learning control with sampled-data feedback for robot manipulators

Iterative learning control with sampled-data feedback for robot manipulators

Iterative learning control for robotic manipulators: A bounded‐error algorithm

Learning Control of Robot Manipulators in Task Space

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