Integrated Task Planning and Control for Mobile Manipulators

Tan, Jindong; Xi, Ning; Wang, Yuechao

doi:10.1177/0278364903022005004

Cited by 100 publications

(43 citation statements)

References 48 publications

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“…Remark 1: For the proposed control law (15), once the control parameters such as , Λ P, K d are determined, the input torque τ is bounded for all t. According to aforementioned proof, we can ensure the asymptotical stability of duct cleaning robot.…”

Section: Control Law Designmentioning

confidence: 94%

“…Theorem 1: Applying the control law (15) to duct cleaning robot system with the robustifying term (16). Let the weight tuning law of RBF network be…”

Section: Control Law Designmentioning

confidence: 99%

“…In the last decades, with the assumption of known dynamics, many researches have been carried out [11][12][13][14][15]. For example, in [12], nonlinear feedback control for the mobile manipulator was developed to compensate for the dynamic interaction between the mobile platform and the arm to achieve tracking performance; in [14], coordinate and control of mobile manipulators were presented by authors with two basic task-oriented controls: end-effectors task control and platform selfposture control.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive robust control based on RBF neural networks for duct cleaning robot

Sun

Huang

et al. 2015

Int. J. Control Autom. Syst.

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In this paper, a control strategy for duct cleaning robot in the presence of uncertainties and various disturbances is proposed which combines the advantages of neural network technique and advanced adaptive robust theory. First of all, the configuration of the duct cleaning robot is introduced and the dynamic model is obtained based on the practical duct cleaning robot. Second, the RBF neural network is used to identify the unstructured and dynamic uncertainties due to its strong ability to approximate any nonlinear function to arbitrary accuracy. Using the learning ability of neural network, the designed controller can coordinately control the mobile plant and cleaning arm of duct cleaning robot with different dynamics efficiently. The neural network weights are only tuned on-line without tedious and lengthy off-line learning. Then, an adaptive robust control scheme based on RBF neural network is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. Finally, based on the Lyapunov stability theory, the stability of the whole closed-loop control system, and the uniformly ultimately boundedness of the tracking errors are all strictly guaranteed. Moreover, simulation and experiment results are given to demonstrate that the proposed control approach can guarantee the whole system converges to desired manifold with well performance.

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Section: Control Law Designmentioning

confidence: 94%

“…Theorem 1: Applying the control law (15) to duct cleaning robot system with the robustifying term (16). Let the weight tuning law of RBF network be…”

Section: Control Law Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive robust control based on RBF neural networks for duct cleaning robot

Sun

Huang

et al. 2015

Int. J. Control Autom. Syst.

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“…qÞ is defined by the Christoffel symbols [10]. Property 3: The disturbances are bounded so that ||U xd || £ U d with U d >0 being known constants.…”

Section: Geometrically Constrained Modelingmentioning

confidence: 99%

“…A stable motion control strategy for mobile manipulators applied by an external force was designed [9]. Kinematic redundancy of mobile manipulators was utilized for integrated task planning and decoupled force/position control [10]. As for NN control of robots, a NN based methodology was developed for the motion control of mobile manipulators [11].…”

Section: Introductionmentioning

confidence: 99%

Intelligent compliant force/motion control of nonholonomic mobile manipulator working on the nonrigid surface

Ming

et al. 2005

Neural Comput & Applic

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The task under consideration is to control a mobile manipulator for the class of nonrigid constrained motion. The working surface is deformable. The geometric and physical model of the surface is unknown and all contact force is nonlinear and difficult to model. To accomplish a task of this kind, we propose a force/motion fuzzy controller based on the philosophy of the parallel approach in two decoupled subspaces. In one subspace, we control the constant contact force normal to the surface and estimate the end-effector tool's deformable depth of the surface; in the other, we keep the end-effector's constant velocity parallel to the tangential plane of the surface and suppress the tangential force of the surface deformation. The nonholonomic mobile base is utilized to avoid the singularity. Stability is established and conditions for the control parameters are derived. Performance of the proposed controller is verified through computer simulations compared with the model-based control.

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Adaptive Motion/Force Tracking Control for a Class of Mobile Manipulators

Sun¹,

Wu²

2015

Asian Journal of Control

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In this paper, modeling and adaptive motion/force tracking control is considered for a class of mobile manipulators under the holonomic and affine constraints with the presence of uncertainties and disturbances. Based on a suitable reduced dynamic model, adaptive controllers are proposed to ensure that the states of a closed‐loop system asymptotically track desired trajectories while the constraint force remains bounded by tuning design parameters. Detailed simulation results confirm the effectiveness of the control strategy.

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Integrated Task Planning and Control for Mobile Manipulators

Abstract: In this paper we present an approach to decoupled force/position control of the end-effector along the same direction for redundant

Cited by 100 publications

References 48 publications

Adaptive robust control based on RBF neural networks for duct cleaning robot

Adaptive robust control based on RBF neural networks for duct cleaning robot

Intelligent compliant force/motion control of nonholonomic mobile manipulator working on the nonrigid surface

Adaptive Motion/Force Tracking Control for a Class of Mobile Manipulators

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