Sliding mode control of robotic arms with deadzone

This paper presented a new data-driven robust control scheme for unknown nonlinear systems in the presence of input saturation and external disturbances. According to the input and output data of the nonlinear system, a recurrent neural network (RNN) data-driven model is established to reconstruct the dynamics of the nonlinear system. An adaptive output-feedback controller is developed to approximate the unknown disturbances and a novel input saturation compensation method is used to attenuate the effect of the input saturation. Under the proposed adaptive control scheme, the uniformly ultimately bounded convergence of all the signals of the closed-loop nonlinear system is guaranteed via Lyapunov analysis. The simulation results are given to show the effectiveness of the proposed data-driven robust controller.

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“…Assumption 3 (see [21][22][23]). For the disturbances in system (1), there exists unknown positive constant such that ‖ ‖ ≤ .…”

Section: Problem Formulation and Neurodynamic Model By Rnnmentioning

confidence: 99%

Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances

Wang

Gong

Liu

2017

Mathematical Problems in Engineering

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“…The model of the robotic manipulators is highly nonlinear, highly complicated, and coupled. [1][2][3][4][5][6][7] In the early research of robots control, the prior knowledge of the system model are needed. A lot of control methods, for example, active control, inverse-based control, backstepping control, have been proposed for robotic manipulators with accurate system model.…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite-time fuzzy command filtered controller design for uncertain robotic manipulators

Yan-ping

2019

International Journal of Advanced Robotic Systems

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In this article, an adaptive fuzzy control command filtered control approach, which is capable of achieving finite-time trajectory tracking control of uncertain robotic manipulators, is proposed by using the backstepping control technique. To obtain a finite-time estimation of a virtual control input and its first-order derivative, a second-order finite-time command filter is designed. Based on the backstepping control technique, an adaptive fuzzy controller that guarantees not only that the tracking errors tend to an arbitrary small region in finite time but also that all signals in the closed-loop system keep bounded is established. Finally, the effectiveness of the proposed method is demonstrated by simulation on a two-link robotic manipulator.

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“…With the increasing scale of data sets and the increasing demand for real-time data processing, and more reliance on large-scale and long-term data storage, [4][5][6] 5G technology can play its powerful role only when it is used in conjunction with the next generation of robots equipped with electric servo motors, sensors and other advanced hardware. As robots become more sophisticated and take over more tedious work in factories, the demand for small, efficient motors will increase.…”

Section: Introductionmentioning

confidence: 99%

Research on cross-layer design and optimization algorithm of network robot 5G multimedia sensor network

Yang

2019

International Journal of Advanced Robotic Systems

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Cross-layer optimization based on maximizing the utility of network robot 5G multimedia sensor network is a systematic method for cross-layer design of wireless networks. It abstracts the functional and performance requirements of the layers in the protocol stack into objective functions and constraints in mathematical optimization problems. In this article, the cross-layer optimization problem of wireless Mesh networks using multi-radio interface multi-channel technology is studied. The optimization problem is modelled based on the network utility maximization method, and the corresponding algorithm is proposed. Based on the random network utility maximization method, the cross-layer optimization model of network robot 5G multimedia sensor network is established. Aiming at the time-varying randomness of random data flow and wireless propagation environment in network robot 5G multimedia sensor network, a model of joint congestion control and power control based on chance constrained programming is proposed, and its genetic algorithm is used to verify it. Reforming research will help speed up the practical pace of the field, with certain theoretical forward-looking and practical value.

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Sliding mode control of robotic arms with deadzone

Cited by 42 publications

References 34 publications

Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances

Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances

Adaptive finite-time fuzzy command filtered controller design for uncertain robotic manipulators

Research on cross-layer design and optimization algorithm of network robot 5G multimedia sensor network

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