Neural network based hybrid force/position control for robot manipulators

Kumar, Naveen; Panwar, Vikas; Sukavanam, N.; Sharma, S. P.; Borm, Jin-Hwan

doi:10.1007/s12541-011-0054-3

Cited by 74 publications

(33 citation statements)

References 15 publications

(8 reference statements)

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“…In order to perform the stability analysis of the dynamic system given in (15) for this controller, the following Lyapunov function candidate is used,…”

Section: Pd-type Controller Structurementioning

confidence: 99%

“…Direct methods involve an explicit form representation of force-feedback, while in indirect methods force-feedback is not explicit but rather is regulated using the position of the end-effector. Among the most important direct interaction control approaches, we might consider explicit force control [5][6][7], hybrid force/ position control [8][9][10][11][12][13][14][15] and parallel force/motion control [16,17]. Meanwhile, among the most relevant indirect interaction control approaches, the stiffness control [18][19][20][21][22] and impedance control approaches [2, 4, 23 -29] might be considered.…”

Section: Introductionmentioning

confidence: 99%

“…, and considering (17) we obtain x = 0 ; in this case, the origin is an equilibrium point of the system (15). If the interaction force exists, it is given by…”

mentioning

confidence: 99%

See 2 more Smart Citations

On Stiffness Regulators with Dissipative Injection for Robot Manipulators

Chávez-Olivares

Reyes-Cortés

González-Galván

2015

International Journal of Advanced Robotic Systems

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The stiffness controller proposed by Salisbury is an interaction control strategy designed to achieve a desired form of static behavior as regards the interaction of a robot manipulator with the environment. The main idea behind this approach is the simulation of a multidimensional linear spring -or linear elastic material -using the difference between the actual position of the end-effector and a constant position (relaxed point), multiplied by a constant stiffness matrix. In this paper, this idea is generalized with the objective of proposing a controller structure that includes a family of stiffness models based on the idea of linear elastic materials. The new controller structure also includes a damping term in order to have control over energy dissipation, as well as a term added for the purpose of compensating the gravity forces of the links. The stability analysis of the proposed controller was performed in the Lyapunov sense. The new stiffness controller is presented as a case study and compared to other cases, such as the Salisbury controller (Cartesian PD) and the tanh-tanh controller. Experimental results using a three degrees-offreedom direct-drive robot for the evaluation of controllers in a constrained motion task are presented.

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“…In order to perform the stability analysis of the dynamic system given in (15) for this controller, the following Lyapunov function candidate is used,…”

Section: Pd-type Controller Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Stiffness Regulators with Dissipative Injection for Robot Manipulators

Chávez-Olivares

Reyes-Cortés

González-Galván

2015

International Journal of Advanced Robotic Systems

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“…The quadratic optimization and sliding-mode based hybrid position and force control approach for a robot manipulator was presented in [3] where the optimal feedback control law was derived to decide matrix differential Riccati equation and a feed forward neural network was applied to tackle the dynamic model uncertainties. A neural adaptive control scheme for hybrid force/position control of rigid robot manipulators was presented in [4]. Based on decomposed robot dynamics into force, position and redundant joint subspaces, a neural controller was proposed to tackle the parametric uncertainties, present in the dynamical model of the robot manipulator.…”

Section: Introductionmentioning

confidence: 99%

ANFIS PD+I Based Hybrid Force/ Position Control of an Industrial Robot Manipulator

Chaudhary¹,

Panwar²,

Sukavanam³

et al. 2014

IJMMM

Self Cite

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Abstract-A hybrid force plus position controller based on adaptive neuro fuzzy inference system proportional derivative + integral (ANFIS-PD+I), with unspecified robot dynamics has been proposed for a robot manipulator under constrained environment. The proposed controller has been employed as a principal controller to tune up orthodox PID gains throughout the complete trajectory tracking process. The validity of the proposed controller has been studied using a 6-Degree of Freedom (DOF) PUMA robot manipulator. Simulation outcomes illustrates that the projected force / position controller adheres to the desired path closer and smoother.Index Terms-Adaptive neuro fuzzy control, degrees of freedom, force control, position control, robot manipulator.

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“…Therefore the hybrid position-force control should be applied. The problem of the manipulator hybrid position-force control [1,2,3,4,5,6] is complex, because the manipulator is a nonlinear object, whose parameters may be unknown, variable and the working conditions are changeable. The hybrid control consists of a position control, which realises movement in the so-called contact surface, and a force control, which realises an interaction force normal to the surface.…”

Section: Introductionmentioning

confidence: 99%

Conventional and Fuzzy Force Control in Robotised Machining

Hendzel

Burghardt

Gierlak

et al. 2013

SSP

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Abstract. This article presents an application of the hybrid position-force control of the robotic manipulator with use of artificial neural networks and fuzzy logic systems in complex control system. The mathematical description of the manipulator and a closed-loop system are presented. In the position control were used the PD controller and artificial neural networks, which compensate nonlinearities of the manipulator. The paper presents mainly the application of various strategies of the force control. The force control strategies using conventional controllers P, PI, PD, PID and fuzzy controllers are presented and discussed. All of the control methods were verified on the real object in order to make a comparison of a control quality.

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Neural network based hybrid force/position control for robot manipulators

Cited by 74 publications

References 15 publications

On Stiffness Regulators with Dissipative Injection for Robot Manipulators

On Stiffness Regulators with Dissipative Injection for Robot Manipulators

ANFIS PD+I Based Hybrid Force/ Position Control of an Industrial Robot Manipulator

Conventional and Fuzzy Force Control in Robotised Machining

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