Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering

Xia, Kerui; Gao, Haibo; Ding, Liang; Liu, Guangjun; Deng, Zongquan; Liu, Zhen; Ma, Changyou

doi:10.1007/s00521-016-2643-7

Cited by 34 publications

(16 citation statements)

References 40 publications

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“…The theory of human brain activity can be formed into a mathematical model with the characteristics of self-learning ability, nonlinear approximation ability, and parallel distribution. At present, the application of artificial neural network in mechanical arm control system is widespread [6][7][8].…”

Section: Methodology 21 the Neural Networkmentioning

confidence: 99%

Analysis of Mechanical Arm Control Methods Under Non-Linear Differential Equation

2020

IJOMAM

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Section: Methodology 21 the Neural Networkmentioning

confidence: 99%

Analysis of Mechanical Arm Control Methods Under Non-Linear Differential Equation

2020

IJOMAM

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“…The parameters are respectively the stiffness coefficient k dc , the initial torque τ s0 , and the damping coefficient b dc of the door closer. The parameter identification methods of the door handle torque model, i.e., (9), and the door torque model, i.e., (18), are investigated as examples.…”

Section: Parameter Identification Methodsmentioning

confidence: 99%

“…6 (b) shows the joint angle of the manipulator during the turning of the door handle. The kinematics of the manipulator were calculated based on [18].…”

Section: A Path Planning Of Door Handle Turningmentioning

confidence: 99%

Dynamic Modeling and Experimental Validation of Door-Opening Process by a Mobile Manipulator

Gao¹,

Ma²,

Ding³

et al. 2019

IEEE Access

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Door-opening is a critical problem for robots used for rescue purposes in nuclear power plants (NPPs). A force and torque (F/T) sensor is not used in the rescue task for door-opening in the NPPs environment. It is, therefore, necessary to study the dynamic model of door-opening. The NPPs door and a door handle dynamic model are significant to the mechanical design, simulation, and dynamic feed-forward control of an NPP robot. To obtain the accurate force data of door-opening, a method based on a gravity compensation algorithm combined with a transformation of the contact force of the end effector is proposed. This paper analyzes the structural features and dynamics model of a fire door and a door handle. Methods for identifying the model parameters are also developed by combining a Nelder-Mead simplex search with the least-squares algorithm. The extensive door-opening experiments were carried out in this paper, and the results validate the fidelity of the derived dynamic models of the door and the door handle. INDEX TERMS Door-opening dynamic model, parameter identification, mobile manipulator.

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“…The and components of innovation vector extracted to track two dimensional targets and It is estimated that the motion of target for each axis is independent. The fuzzy h-infinity innovation vector for xaxis is represented by, (14) The high positive values of and suggest that innovation sequence enhances at a very speedy rate.…”

Section: Fuzzy H-infinity Filter Based Multi-sensory Data Fusion Imentioning

confidence: 99%

“…The validity domains of sensors are defined using fuzzy sets [11] while the KF and Takagi-Sugeno fuzzy modeling technique are combined to extend the classical Kalman linear state estimation to the nonlinear system. These methods have been widely used in applications such as dynamic mobile localization [12], truck backing-up problems [13], and trajectory tracking control [14]. Even though extended methods combined with fuzzy set theory, such as fuzzy KF or fuzzy EKF, are popular when dealing with target tracking problems, they have two main limitations: (1) the estimation of the motion states is important for tracking the maneuvering target during the entire tracking process because it directly impacts the parameters of the observation equation [15].…”

mentioning

confidence: 99%

Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks

Lokesh¹,

Reddy²

2020

IJITEE

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Data collection utilizing wireless sensors networks (WSNs) has been utilized for surveillance, monitoring environment, animal etc. Target tracking of maneuvering objects is an essential need of modern life. Nonetheless, because of diverse nature of sensor and complex environment, sensors measurement errors need to be minimized considering diverse motion states in process of tracking (sensing) operation. Enhancing network lifetime (i.e., reducing energy dissipation of sensor nodes) and improving tracking quality are major concern of target tracking using WSN. Form improving network energy efficiency, multi-sensory target tracking method has been modelled using Kalman Filter (KF) by existing target tracking method. The KF based model are affected due to presence of noise or missing data. For overcoming research issues this paper present an H-infinity filter (HF) to evaluate fusion for maneuvering target tracking in WSN. Further, to minimize the estimation errors and reduces/controlling the effects of outliers fuzzy H-infinity (FHF) filter for target tracking WSN is presented. Experiment outcome shows proposed HF and FHF fusion model attain better performance than existing KF based method for clustered based WSN in terms of positional and velocity root mean square error and energy dissipation.

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Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering

Cited by 34 publications

References 40 publications

Analysis of Mechanical Arm Control Methods Under Non-Linear Differential Equation

Analysis of Mechanical Arm Control Methods Under Non-Linear Differential Equation

Dynamic Modeling and Experimental Validation of Door-Opening Process by a Mobile Manipulator

Energy Efficient Target Tracking Method for Multi-Sensory scheduling in Wireless Sensor Networks

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