Adaptive control of nonlinear system with time delay based on the feedback error learning method

Ushida, Shun; Kimura, Hidenori

doi:10.1109/icit.2002.1189251

Cited by 8 publications

(9 citation statements)

References 8 publications

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“…Thus, care must be taken in the choice of feedback gains for FEL, especially in the adaptive feedforward formulation. In Ushida and Kimura (2002), similar theoretical studies of FEL including time delay are presented for a specific class of plant dynamics such as a robot arm. One of the differences between our work and Ushida and Kimura's (2002) is that we explicitly address the problem of how to select feedback gains to ensure stability of FEL.…”

Section: Discussionmentioning

confidence: 99%

“…One of the differences between our work and Ushida and Kimura's (2002) is that we explicitly address the problem of how to select feedback gains to ensure stability of FEL. In contrast, the study by Ushida and Kimura (2002) only mentions that feedback gains need to be selected such that the tracking error dynamics with respect to the non-adaptive control system using all the correct parameters in the plant dynamics are stable. In practice, analysis of the tracking error dynamics by Ushida and Kimura (2002) is not likely to be straightforward since the tracking error dynamics are highly nonlinear and rather complicated even without time delay.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, the study by Ushida and Kimura (2002) only mentions that feedback gains need to be selected such that the tracking error dynamics with respect to the non-adaptive control system using all the correct parameters in the plant dynamics are stable. In practice, analysis of the tracking error dynamics by Ushida and Kimura (2002) is not likely to be straightforward since the tracking error dynamics are highly nonlinear and rather complicated even without time delay. In contrast, in this paper, the tracking error dynamics of the non-adaptive system are linear, and we provide more explicit condition for the choice of feedback gains to guarantee stability.…”

Section: Discussionmentioning

confidence: 99%

“…Stability analyses of FEL for a class of linear systems and a two-link planar robot arm in a horizontal plane are presented by Miyamura and Kimura (2002) and Ushida and Kimura (2002), respectively. However, the plant dynamics considered in Miyamura and Kimura (2002) are confined to a restricted class of linear systems (stable and stably invertible), 2 and these studies do not address practical issues, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Feedback error learning and nonlinear adaptive control

2004

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In this paper, we present our theoretical investigations of the technique of feedback error learning (FEL) from the viewpoint of adaptive control. We first discuss the relationship between FEL and nonlinear adaptive control with adaptive feedback linearization, and show that FEL can be interpreted as a form of nonlinear adaptive control. Second, we present a Lyapunov analysis suggesting that the condition of strictly positive realness (SPR) associated with the tracking error dynamics is a sufficient condition for asymptotic stability of the closed-loop dynamics. Specifically, for a class of second order SISO systems, we show that this condition reduces to K 2 D . K P ; where K P and K D are positive position and velocity feedback gains, respectively. Moreover, we provide a 'passivity'-based stability analysis which suggests that SPR of the tracking error dynamics is a necessary and sufficient condition for asymptotic hyperstability. Thus, the condition K 2 D . K P mentioned above is not only a sufficient but also necessary condition to guarantee asymptotic hyperstability of FEL, i.e. the tracking error is bounded and asymptotically converges to zero. As a further point, we explore the adaptive control and FEL framework for feedforward control formulations, and derive an additional sufficient condition for asymptotic stability in the sense of Lyapunov. Finally, we present numerical simulations to illustrate the stability properties of FEL obtained from our mathematical analysis. q

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feedback error learning and nonlinear adaptive control

2004

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“…In [5], stability analysis is considered for the general n-link rigid serial manipulator. In [6], they consider the case with time delay. We note, however, that the specific bounds on the feedback gain matrices are not discussed.…”

Section: Introductionmentioning

confidence: 99%

Feedback Error Learning with basis function networks

Ishihara

Doornik

Sanger

2006

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006

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Predictive display and interaction of telerobots based on augmented reality

Xiong

Xie

2006

Robotica

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A predictive display method and man-virtual robot interaction based on augmented reality are applied to control a telerobot. We first discuss the process of the augmented reality environment development. Then, we present the advantages of predictive display. Simulation of virtual robot's tasks in the augmented environment improves the safety of the telerobot when it executes the planned tasks. In addition, the immediate feedback from the virtual robot avoids the exacerbation of maneuverability caused by time-delay. For a more natural operation process, we apply multi man-virtual robot interactive methods. Lastly, the experiment of pick & place is conducted to validate the system.

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Adaptive control of nonlinear system with time delay based on the feedback error learning method

Cited by 8 publications

References 8 publications

Feedback error learning and nonlinear adaptive control

Feedback error learning and nonlinear adaptive control

Feedback Error Learning with basis function networks

Predictive display and interaction of telerobots based on augmented reality

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