Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

Zhang, Caihong; Sun, Tairen

doi:10.1155/2014/267307

Cited by 15 publications

(10 citation statements)

References 31 publications

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“…In this regard, another contribution from Zhang et al, in ref. [7], has given the idea of formation control of NMR. First, the formation trajectory converted into a special tracking problem, and then, a neural network-based observer is applied to estimate the states of the leader vehicle.…”

Section: Introductionmentioning

confidence: 99%

Robust Output-Feedback Formation Control Design for Nonholonomic Mobile Robot (NMRs)

Yousuf

2019

Robotica

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SummaryThis paper addresses the systematic approach to design formation control for kinematic model of unicycle-type nonholonomic mobile robots. These robots are difficult to stabilize and control due to their nonintegrable constraints. The difficulty of control increases when there is a requirement to control a cluster of nonholonomic mobile robots in specific formation. In this paper, the design of the control scheme is presented in a three-step process. First, a robust state-feedback point-to-point stabilization control is designed using sliding mode control. In the second step, the controller is modified so as to address the tracking problem for time-varying reference trajectories. The proposed control scheme is shown to provide the desired robustness properties in the presence of the parameter variation, in the region of interest. Finally, in third step, tracking problem of a single nonholonomic mobile robot extends to formation control for a group of mobile robots in the leader–follower scenario using integral terminal- based sliding mode control augmented with stabilizing control. Starting with the transformation of the mathematical model of robots, the proposed controller ensures that the robots maintain a constant distance between each other to avoid collision. The main problem with the proposed controller is that it requires all states specially velocities. Therefore, the state-feedback control scheme is then extended to output feedback by incorporating a highgain observer. With the help of Lyapunov analysis and appropriate simulations, it is shown that the proposed output-feedback control scheme achieves the required control objectives. Furthermore, the closed loop system trajectories reach to desired equilibrium point in finite time while maintaining the special pattern.

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Section: Introductionmentioning

confidence: 99%

Robust Output-Feedback Formation Control Design for Nonholonomic Mobile Robot (NMRs)

Yousuf

2019

Robotica

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“…En García et al (2017), Liu et al (2017), Pan et al (2016), Sa et al (2017), y Sun et al (2017) se diseñan controles aplicados robots manipuladores. En Hernández et al (2017), Marín et al (2014), Marín et al (2013), Ortigoza et al (2016), Serrano et al (2018), y Zhang et al (2014) se estudia el seguimiento de la trayectoria en los robots móviles. En Hernández et al (2016), Olivares et al (2014), Páramo et al (2017), Peng et al (2017), y Rubio (2018) se enfocan en los controles aplicados a péndulos.…”

Section: Introductionunclassified

Mínimos Cuadrados Recursivos para un Manipulador que Aprende por Demostración

Rubio

García

Aquino

et al. 2019

Rev. iberoam. autom. inform. ind.

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En este trabajo, se desarrolla un sistema de control automatizado para permitir que un manipulador aprenda y planifique las trayectorias a partir de las demostraciones dadas por la mano de un usuario. La entrada de datos es adquirida por un sensor, y se aprende su comportamiento a través de un algoritmo de aprendizaje automático basado en los mínimos cuadrados recursivos. Se utiliza un perfil de trayectoria de interpoladores a tramos para evitar el movimiento impulsivo del manipulador. Se realiza el análisis de las cinemáticas directa e inversa para obtener los valores de las variables articulares para el manipulador. Se crea un modelo dinámico usando la formulación de Newton-Euler. Se aplica un control proporcional derivativo al sistema. Los sistemas de monitoreo y control se implementan en una plataforma embebida para fines de prueba.

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“…Other control approaches such as adaptive control based on neural network for WMRs are proposed in recent years. 3,22,23 However, energy saving is not considered in those control strategies.…”

Section: Introductionmentioning

confidence: 99%

Robust control of a redundant wheeled drive system for energy saving and fail safe motion

Mohamed

Uchiyama

2017

Advances in Mechanical Engineering

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This article presents sliding mode-based robust tracking control for a redundant wheeled drive system, which is designed for energy saving and fail safe motion. Wheeled mobile robots are widely used in different applications such as a wheelchair and an automated guided vehicle because of their loading capability, low mechanical complexity, and simple engineering design. In addition, using wheeled mobile robots is an effective way to enhance the quality of life for elderly and disabled people to promote their independence and to extend their activities. Wheeled mobile robots are generally operated using embedded batteries, which determine the operating time. Therefore, saving energy motion is a significant requirement to extend the operation time. The dynamics of a redundant wheeled drive system is described. Distribution control and sliding mode control for wheeled mobile robots are proposed, and its stability is guaranteed based on the Lyapunov stability theory. Finally, the robustness and effectiveness of the proposed method are demonstrated experimentally, providing superior results to conventional state feedback control and robust tracking in the real environment with less energy.

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Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

Cited by 15 publications

References 31 publications

Robust Output-Feedback Formation Control Design for Nonholonomic Mobile Robot (NMRs)

Robust Output-Feedback Formation Control Design for Nonholonomic Mobile Robot (NMRs)

Mínimos Cuadrados Recursivos para un Manipulador que Aprende por Demostración

Robust control of a redundant wheeled drive system for energy saving and fail safe motion

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