Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments

Li, Zhijun; Li, Jianxun; Kang, Yu

doi:10.1016/j.automatica.2010.08.012

Cited by 150 publications

(62 citation statements)

References 12 publications

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“…For the model-based control, the control parameters are chosen as K 1 = diag [10,10], K 2 = diag [8,8]. For the approximation-based control, a number of 256 nodes are used for each S i (Z) with centers chosen in the…”

Section: Remark 7 the Tracking Error Has Been Shown To Converge And Rmentioning

confidence: 99%

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Neural Network Control of a Rehabilitation Robot by State and Output Feedback

et al. 2014

J Intell Robot Syst

228

100

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-In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov's stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.Index Terms -Adaptive neural network control, Full state feedback control, Lyapunov's direct method, Output feedback control, Rehabilitation robot.

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Section: Remark 7 the Tracking Error Has Been Shown To Converge And Rmentioning

confidence: 99%

“…In recent years, the control problem of the constrained robot has gained much attention [6,7,8,9]. The control of an uncertain robot in interactions with the patient is difficult to handle in the control design and stability analysis due to the unknown system dynamics and unknown external force.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Control of a Rehabilitation Robot by State and Output Feedback

et al. 2014

J Intell Robot Syst

228

100

View full text Add to dashboard Cite

show abstract

“…Currently, the adaptive control has already been researched in robot area [10][11][12][13][14][15][16]. Adaptive control based on the reference model of underwater snake-like robot has not been investigated in the literature yet.…”

Section: Introductionmentioning

confidence: 99%

Adaptive controller design for underwater snake robot with unmatched uncertainties

AnFan

et al. 2016

Sci. China Inf. Sci.

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Because of hydrodynamic model error of the present dynamic model, there is a challenge in controller design for the underwater snake-like robot. To tackle this challenge, this paper proposes an adaptive control schemes based on dynamic model for a planar, underwater snake-like robot with model error and time-varying noise. The adaptive control schemes aim to achieve the adaptive control of joint angles tracking and the direction of locomotion control. First, through approximation and reducibility using Taylor expansion method, a simplified dynamics model of a planar amphibious snake-like robot is derived. Then, the L1 adaptive controller based on piecewise constant adaptive law is applied on the simplified planar, underwater snake-like robot, which can deal with both matched and unmatched nonlinear uncertainties. Finally, to control the direction of locomotion, an auxiliary bias signal is used as the control input to regulate the locomotion direction. Simulation results show that this L1 adaptive controller is valid to deal with different uncertainties and achieve the joint angles tracking and fast adaptive at the same time. The modified L1 adaptive controller, in which the auxiliary bias item is added, has the ability to change the direction of locomotion, that is, the orientation angle is periodic with arbitrarily given constant on average.

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“…Along with the diversity of robot working environment and the complexity of the robot's structure, the force/position tracking control has been studied in different kinds of uncertainties. Nowadays, ignoring uncertainties to simplify control design may cause the large steady-state errors or/and poor transient response [5]. For the case of parametric uncertainties, adaptive control method has been presented in [6,7] to make robots adapt the changing control environment.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Wang

Zhang

2017

Complexity

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A dynamic learning method is developed for an uncertain -link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steadystate control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

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Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments

Cited by 150 publications

References 12 publications

Neural Network Control of a Rehabilitation Robot by State and Output Feedback

Neural Network Control of a Rehabilitation Robot by State and Output Feedback

Adaptive controller design for underwater snake robot with unmatched uncertainties

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

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