			Home About us Contact

Unknown Disturbances (unknown + disturbance)

Distribution by Scientific Domains

Engineering	80%

Selected Abstracts

Precise disturbance modeling for improvement of positioning performance

ELECTRICAL ENGINEERING IN JAPAN, Issue 2 2010
Masafumi Yamamoto
Abstract This paper presents a modeling methodology for unknown disturbances in mechatronics systems, based on disturbance estimation using an iterative learning process. In disturbance modeling, nonlinear frictions are specially handled as disturbances in the mechanisms, which mainly degrade trajectory control performance. Friction can be mathematically modeled by using learned estimation data as a function of the displacement, velocity. acceleration, and jerk of the actuator. This model has the distinctive feature that friction compensation can be achieved with a generalization capability for different conditions. The proposed positioning control approach with disturbance modeling and compensation has been verified by experiments using a table drive system on a machine stand. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(2): 31,39, 2010; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/eej.20928 [source]

Adaptive estimation and rejection of unknown sinusoidal disturbances through measurement feedback for a class of non-minimum phase non-linear MIMO systems

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2006
Weiyao Lan
Abstract This paper develops an adaptive estimation method to estimate unknown disturbances in a class of non-minimum phase non-linear MIMO systems. The unknown disturbances are generated by an unknown linear exosystem. The frequencies, phases and amplitudes of the disturbances are unknown, the only available information of the disturbances is the number of distinctive frequencies. The system considered in this paper is a class of MIMO non-linear systems in the output feedback form which can be non-minimum phase. The proposed estimation algorithm provides exponentially convergent estimates of system states, unknown disturbances in the system and frequencies of the disturbances characterized by the eigenvalues of the exosystem. Moreover, based on the stabilization controller for the disturbance free system, the estimates of the disturbances are used to solve the disturbance rejection problem. The unknown disturbances are compensated completely with the stability of the whole closed-loop system. Copyright © 2006 John Wiley & Sons, Ltd. [source]

A complete parameterization of clf-based input-to-state stabilizing control laws

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 17 2004
J. W. Curtis
Abstract Sontag's formula proves constructively that the existence of a control Lyapunov function implies asymptotic stabilizability. A similar result can be obtained for systems subject to unknown disturbances via input-to-state stabilizing control Lyapunov functions (ISS-clfs) and the input-to-state analogue of Sontag's formula. The present paper provides a generalization of the ISS version of Sontag's formula by completely parameterizing all continuous ISS control laws that can be generated by a known ISS-clf. When a simple inner-product constraint is satisfied, this parameterization also conveniently describes a large family of ISS controls that solve the inverse-optimal gain assignment problem, and it is proved that these controls possess Kalman-type gain margins. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Combining state estimator and disturbance observer in discrete-time sliding mode controller design,

ASIAN JOURNAL OF CONTROL, Issue 5 2008
Jeang-Lin Chang
Abstract In response to a multiple input/multiple output discrete-time linear system with mismatched disturbances, an algorithm capable of performing estimated system states and unknown disturbances is proposed first, and then followed with the design of the controller. Attributed to the fact that both system states and disturbances can be estimated simultaneously with our proposed method, the estimation error is constrained at less than O(T) as the disturbance between the two sampling points is insignificant. In addition, the estimated system states and disturbances are then to be used in the controller when implementing our algorithm in a non-minimum phase system (with respect to the relation between the output and the disturbance). The tracking error is constrained in a small bounded region and the system stability is guaranteed. Finally, a numerical example is presented to demonstrate the applicability of the proposed control scheme. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]

Dynamic On-Line Reoptimization Control of a Batch MMA Polymerization Reactor Using Hybrid Neural Network Models

CHEMICAL ENGINEERING & TECHNOLOGY (CET), Issue 9 2004
Y. Tian
Abstract A hybrid neural network model based on-line reoptimization control strategy is developed for a batch polymerization reactor. To address the difficulties in batch polymerization reactor modeling, the hybrid neural network model contains a simplified mechanistic model covering material balance assuming perfect temperature control, and recurrent neural networks modeling the residuals of the simplified mechanistic model due to imperfect temperature control. This hybrid neural network model is used to calculate the optimal control policy. A difficulty in the optimal control of batch polymerization reactors is that the optimization effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. With the presence of an unknown amount of reactive impurities, the off-line calculated optimal control profile will be no longer optimal. To address this issue, a strategy combining on-line reactive impurity estimation and on-line reoptimization is proposed in this paper. The amount of reactive impurities is estimated on-line during the early stage of a batch by using a neural network based inverse model. Based on the estimated amount of reactive impurities, on-line reoptimization is then applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimization control of a simulated batch methyl methacrylate polymerization process. [source]