An adaptive switching learning control method for trajectory tracking of robot manipulators

Ouyang, P. R.; Zhang, W.J.; Gupta, Madan M.

doi:10.1016/j.mechatronics.2005.08.002

Cited by 174 publications

(80 citation statements)

References 22 publications

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“…The forward dynamics were obtained using the transfer function of the system. Their approach is promising because the iterative learning controller is simple and yet provides greater enhancement of control accuracy Ouyang et al, 2006). However, the PIFA does not seem to be repetitive on its own, as each step is slightly different, and the motion at the current step has some dependence on the motion at the previous steps.…”

Section: Feedback Controlmentioning

confidence: 99%

Modeling and control of piezoelectric inertia–friction actuators: review and future research directions

Liu

et al. 2015

Mech. Sci.

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Abstract. This paper provides a comprehensive review of the literature regarding the modeling and control of piezoelectric inertia-friction actuators (PIFAs). Examples of PIFAs are impact drive mechanisms (IDMs) and friction-driving actuators (FDAs). In this paper, the critical challenges are first identified in modeling and control of PIFAs. Second, a general architecture of PIFAs is proposed to facilitate the analysis and classification of the literature regarding modeling and control of PIFAs. This general architecture covers all types of PIFAs (e.g., FDAs, IDMs) and thus serves as a general conceptual model of PIFAs. There is an additional benefit with this general architecture of PIFAs, namely that it is conducive to innovation in PIFAs, as new specific PIFAs may be designed in order to tailor to a specific application (for example, both FDAs and IDMs are viewed as specific PIFAs). Finally, the paper presents future directions in modeling and control for further improvement of the performance of PIFAs.

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Section: Feedback Controlmentioning

confidence: 99%

Modeling and control of piezoelectric inertia–friction actuators: review and future research directions

Liu

et al. 2015

Mech. Sci.

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“…( ) is the gravity term and is the control moment. In this paper, the robot manipulators should have the following common robot properties [16].…”

Section: Problem Formulationmentioning

confidence: 99%

“…The variable structure control (VSC) with sliding mode has been used to solve the load disturbance and other uncertainties [14]. Iterative learning controller of manipulator can help to finish repetitive tasks [15,16]. Intelligent learning control method is becoming an effective strategy for manipulator with complex task and environment.…”

Section: Introductionmentioning

confidence: 99%

Inner-Learning Mechanism Based Control Scheme for Manipulator with Multitasking and Changing Load

Xue

Xiang

2014

Advances in Mechanical Engineering

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With the rapid development of robot technology and its application, manipulators may face complex tasks and dynamic environments in the coming future, which leads to two challenges of control: multitasking and changing load. In this paper, a novel multicontroller strategy is presented to meet such challenges. The presented controller is composed of three parts: subcontrollers, inner-learning mechanism, and switching rules. Each subcontroller is designed with self-learning skills to fit the changing load under a special task. When a new task comes, switching rule reselects the most suitable subcontroller as the working controller to handle current task instead of the older one. Inner-learning mechanism makes the subcontrollers learn from the working controller when load changes so that the switching action causes smaller tracking error than the traditional switch controller. The results of the simulation experiments on two-degree manipulator show the proposed method effect.

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“…With the industrial development, there are more and more control objectives about the system tracking problem (Ouyang et al, 2006;Mauder, 2008;Smolders et al, 2008), which is very important in control theory synthesis. Taking the robot as an example, it is often required to follow some special trajectories quickly as well as provide robustness to system uncertainties, including unmodeled dynamics, internal parameter variations and external disturbances.…”

Section: Optimal Sliding Mode Tracking Control For Uncertain Nonlineamentioning

confidence: 99%

Optimal Sliding Mode Control for a Class of Uncertain Nonlinear Systems Based on Feedback Linearization

Pang¹,

Yang²

2011

Robust Control, Theory and Applications

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An adaptive switching learning control method for trajectory tracking of robot manipulators

Cited by 174 publications

References 22 publications

Modeling and control of piezoelectric inertia–friction actuators: review and future research directions

Modeling and control of piezoelectric inertia–friction actuators: review and future research directions

Inner-Learning Mechanism Based Control Scheme for Manipulator with Multitasking and Changing Load

Optimal Sliding Mode Control for a Class of Uncertain Nonlinear Systems Based on Feedback Linearization

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