For automobile industry, rubber is widely used for noise isolation and vibration reduction. However, due to its distributed and nonlinear characteristics, it is hard to precisely estimate its characteristics such as t...
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ISBN:
(纸本)9781424400997
For automobile industry, rubber is widely used for noise isolation and vibration reduction. However, due to its distributed and nonlinear characteristics, it is hard to precisely estimate its characteristics such as the loss coefficient which is defined as the tangent of the phase delay between the fundamental components of the strain and the stress under sinusoidal driving. Moreover, even using a truncated finite-dimensional model, with rubber's nonlinearity, resonance of the testing mechanical system, and measurement noise, optimal estimation of the loss coefficient by using Kalman filter is not feasible in the presence of these uncertainties and non-Gaussian disturbances. Therefore, H-infinity filter is applied in this paper to robustly estimate the loss coefficient from the state-space perspective. As a state-space model for representing a sinusoidal signal has eigenvalues on the unit circle, the measured data is first processed by imposing a suitable exponential decay in order to ensure the stability of the H-infinity filter. Moreover, due to finite data length, an iterative H-infinity filter is developed to improve the accuracy of parameter estimates. At each iteration, estimation of disturbances by using the H-infinity filter is first performed by applying the previously estimated components of the desired signal. Then a robust estimation of the desired signal is made with respect to the measured signal which is subtracted by the estimated disturbance. Both simulation study and experimental test are conducted to verify the performance of the proposed iterative H. filter.
A new Fourier series based learning control scheme is presented in this paper. The proposed controller consists of two parts: a time domain feedback controller that is designed to stabilize the system and improve the ...
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A new Fourier series based learning control scheme is presented in this paper. The proposed controller consists of two parts: a time domain feedback controller that is designed to stabilize the system and improve the robustness to random disturbance, and a Fourier series based learning controller that is used to generate the best feedforward in the face of deterministic modeling uncertainties. The learning controller is essentially a feedback controller in frequency domain. A new iterativealgorithm based on the Fourier series approximation generates the optimal feedforward to force the state trajectory converge to a stable sliding surface. Only the historical input and output information of the closed-loop system is used. There is no requirement for knowledge about the system structure and parameters. The stability analysis of the closed-loop system with the learning controller is also provided. The effectiveness of the proposed controller is experimentally verified on a positioning table. (C) 2000 Elsevier Science B.V. All rights reserved.
learning control is a concept for controlling dynamic systems in an iterative manner. It arises from the recognition that robotic manipulators are usually used to perform repetitive tasks. Most researches on the itera...
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learning control is a concept for controlling dynamic systems in an iterative manner. It arises from the recognition that robotic manipulators are usually used to perform repetitive tasks. Most researches on the iterativelearning control of robots have been focused on the problem of free motion control and hybrid position/force control where the learning controllers are designed to track the desired motion and force trajectories. The iterativelearning impedance control of robotic manipulators, however, has been studied recently. In this paper, an iterativelearning impedance control problem for robotic manipulators is formulated and solved. A target impedance is specified and a learning controller is designed such that the system follows the desired response specified by the target model as the actions are repeated, A design method for analyzing the convergence of the learning impedance system is developed. A sufficient condition for guaranteeing the convergence of the system is also derived, The proposed learning impedance control scheme is implemented on an industrial selective compliance assembly robot arm (SCARA) robot, SEIKO TT3000. Experimental results verify the theory and confirm the effectiveness of the learning impedance controller.
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