Dynamics and Control of Mechanical Systems in Offshore Engineering

He, Wei; Ge, Shuzhi Sam; How, Bernard Voon Ee; Choo, Yoo Sang

doi:10.1007/978-1-4471-5337-5

Cited by 65 publications

(37 citation statements)

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“…From (19), we can see that the control input in the (i + 1)th iteration consists of two parts: the first part is the control input profile in the previous iteration and the second part is relevant to the tracking error profile in the previous iteration. Since both U i (s) and E i (s) are available before implementing control in the (i + 1)th iteration, the ILC law (19) is obviously a feedforward control scheme.…”

Section: Lidpss Without Iteration-dependent External Disturbancementioning

confidence: 99%

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Iterative learning control of inhomogeneous distributed parameter systems—frequency domain design and analysis

Huang

et al. 2014

Systems & Control Letters

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Section: Lidpss Without Iteration-dependent External Disturbancementioning

confidence: 99%

“…Such scenario is more practical and implementable in certain applications [18][19][20]. (iv) Instead of considering the stability or set-point problem as in [7,[11][12][13], we consider more general output tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control of inhomogeneous distributed parameter systems—frequency domain design and analysis

Huang

et al. 2014

Systems & Control Letters

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“…However, when the input saturations are taken into account, handling simultaneously both uncertain parameters and input constraints is problematic. The existing approaches [19][20][21][22] may not be directly applied due to constraints. Furthermore, when the input scalings are unknown and disturbances effect the system, the control problem becomes much more challenging.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control for an uncertain robotic manipulator with input saturations

Tran

2016

Control Theory Technol.

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In this paper, we address the control problem of an uncertain robotic manipulator with input saturations, unknown input scalings and disturbances. For this purpose, a model reference adaptive control like (MRAC-like) is used to handle the input saturations. The model reference is input to state stable (ISS) and driven by the errors between the required control signals and input saturations. The uncertain parameters are dealt with by using linear-in-the-parameters property of robotic dynamics, while unknown input scalings and disturbances are handled by non-regressor based approach. Our design ensures that all the signals in the closed-loop system are bounded, and the tracking error converges to the compact set which depends on the predetermined bounds of the control inputs. Simulation on a planar elbow manipulator with two joints is provided to illustrate the effectiveness of the proposed controller.

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“…Artificial neural networks have been widely used for the control design of uncertain nonlinear systems [15,16,17,18,19,20,21,22,23]. The relevant applications for this approach based on the Lyapunov's stability theory include [24,25,26,27,28,29,30,31]. However, most of above papers considers the constrained force as a part of the system uncertainties, and neural networks are used to approximate those uncertainties for achieving the control objective.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

J Intell Robot Syst

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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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Dynamics and Control of Mechanical Systems in Offshore Engineering

Cited by 65 publications

References 0 publications

Iterative learning control of inhomogeneous distributed parameter systems—frequency domain design and analysis

Iterative learning control of inhomogeneous distributed parameter systems—frequency domain design and analysis

Adaptive control for an uncertain robotic manipulator with input saturations

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

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