Self-Sensing Active Magnetic Dampers for Vibration Control

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Angelo Bonfitto ◽  
Xavier De Lépine ◽  
Mario Silvagni ◽  
Andrea Tonoli
Keyword(s):  
Vibration Control ◽  
Closed Loop ◽  
Flexible Structures ◽  
Open Loop ◽  
System Response ◽  
Model Parameters ◽  
Loop Model ◽  
Single Degree Of Freedom ◽  
Luenberger Observer ◽  
Model Response

The aim of this paper is to investigate the potential of a self-sensing strategy in the case of an electromagnetic damper for the vibration control of flexible structures and rotors. The study has been performed in the case of a single degree of freedom mechanical oscillator actuated by a couple of electromagnets. The self-sensing system is based on a Luenberger observer. Two sets of parameters have been used: nominal ones (based on simplifications on the actuator model) and identified ones. In the latter case, the parameters of the electromechanical model used in the observer are identified starting from the open-loop system response. The observed states are used to close a state-feedback loop with the objective of increasing the damping of the system. The results show that the damping performance are good in both cases, although much better in the second one. Furthermore, the good correlation between the closed-loop model response and the experimental results validates the modeling, the identification procedure, the control design, and its implementation. The paper concludes on a sensitivity analysis, in which the influence of the model parameters on the closed-loop response is shown.

Model Predictive Control and Controller Parameter Optimisation of Combustion Instabilities

2019 ◽  
Vol 36 (2) ◽  
pp. 185-194 ◽  
Author(s):  
I. Yazar ◽  
F. Caliskan ◽  
R. Vepa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Combustion Process ◽  
Open Loop ◽  
Loop Model ◽  
Parameter Optimisation ◽  
Direct Adaptive Control ◽  
The Cost ◽  
Adaptive Control Algorithm

Abstract In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.

Location model for a remanufacturing reverse logistics network based on adaptive genetic algorithm

SIMULATION ◽  
2019 ◽  
Vol 95 (11) ◽  
pp. 1069-1084 ◽  
Author(s):  
Rui Yan ◽  
Bo Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Model ◽  
Reverse Logistics ◽  
Closed Loop ◽  
Value Added ◽  
Open Loop ◽  
Demand Elasticity ◽  
Mixed Integer ◽  
Adaptive Genetic Algorithm ◽  
Loop Model

Energy saving and environmental protection are important issues of today. Concerning the environmental and social need to increase the utilization of used products, this paper introduces two remanufacturing reverse logistics (RL) network models, namely, the open-loop model and the closed-loop model. In an open-loop RL system, used products are recovered by outside firms, while in a closed-loop RL system, they are returned to their original producers. The open-loop model features a location selection with two layers. For this model, a mixed-integer linear program (MILP) is built to minimize the total costs of the open-loop RL system, including the fixed cost, the freight between nodes, the operation cost of storage and remanufacturing centers, the penalty cost of unmet or remaining demand quantity, and the government-provided subsidy given to the enterprises that protect the environment. This MILP is solved using an adaptive genetic algorithm with MATLAB simulation. For a closed-loop RL network model, a special demand function considering the relationship between new and remanufactured products is developed. Remanufacturing rate, environmental awareness, service demand elasticity, value-added services, and their impacts on total profit of the closed-loop supply chain are analyzed. The closed-loop RL network model is proved effective through the analysis of a numerical example.

Active vibration control experiments on an AgustaWestland W30 helicopter airframe

2011 ◽  
Vol 226 (6) ◽  
pp. 1504-1516 ◽  
Author(s):  
J E Mottershead ◽  
M Ghandchi Tehrani ◽  
S James ◽  
P Court
Keyword(s):  
Vibration Control ◽  
Frequency Response ◽  
Closed Loop ◽  
Vibration Suppression ◽  
Active Vibration Control ◽  
Input Voltage ◽  
Open Loop ◽  
Control Technique ◽  
Response Functions ◽  
Frequency Response Functions

This article describes the practical application of a vibration control technique, developed by the authors and known as the receptance method, to the AgustaWestland W30 helicopter airframe in the vibration test house at Yeovil. The experimental work was carried out over a total of 5 days in two visits to the Yeovil site during February and March 2011. In the experiments, existing electro-hydraulic actuators were used; they were built into the airframe structure and originally designed for vibration suppression by the methodology known as active control of structural response developed at the AgustaWestland Helicopters site in Yeovil. Accelerometers were placed at a large number of points around the airframe and an initial open-loop modal test was carried out. In a subsequent test, at higher actuator input voltage, considerable non-linearity was discovered, to the extent that the ordering of certain modes had changed. The vibration modes were, in general, heavily damped. Control was implemented using measured frequency response functions obtained at the higher input level. After acquiring the necessary measurements, simulations were carried out and the controller was implemented using MATLAB/Simulink and dSPACE. The closed-loop poles were mostly assigned with small real parts so that the system would be lightly damped and sharp peaks would be clearly apparent in the measured closed-loop frequency response functions. Locations of the open- and closed-loop poles in the complex s-plane were obtained to verify that the required assignment of poles had taken place.

Fault detection and tolerance in synchronous vibration control of rotor—magnetic bearing systems

2001 ◽  
Vol 215 (12) ◽  
pp. 1401-1416 ◽  
Author(s):  
M N Sahinkaya ◽  
M O T Cole ◽  
C R Burrows
Keyword(s):  
Vibration Control ◽  
Magnetic Bearing ◽  
Open Loop ◽  
Magnetic Bearings ◽  
Control Technique ◽  
Effective Control ◽  
System Response ◽  
Rotor Support ◽  
Measured System ◽  
Control Forces

The use of magnetic bearings in rotating machinery provides contact-free rotor support, and allows vibration control using both closed-loop and open-loop strategies. One of the simplest and most effective methods to reduce synchronous lateral vibration when using magnetic bearings is through an open-loop adaptive control technique, in which the amplitude and phase of synchronous magnetic control forces are adjusted automatically to minimize the measured vibrations along the rotor. However, transducer malfunction, or faults in the signal-processing channels, may cause the controller to adapt incorrectly, with unwanted and possibly catastrophic effects. It is shown that an extension to the control strategy, which utilizes the variances of the measured system response and identified parameters, enables the faults to be detected and accounted for so that a modified control action can achieve continued and effective control of the synchronous vibration. The approach is extended further to identify changes in external factors, such as unbalance and rotor dynamics. Various faults and perturbations are examined experimentally, and the ability of the controller to detect and compensate for these changes is demonstrated.

Pseudo-random binary sequence closed-loop system identification error with integration control

2009 ◽  
Vol 223 (6) ◽  
pp. 877-884 ◽  
Author(s):  
Z Ren ◽  
G G Zhu
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Binary Sequence ◽  
Open Loop ◽  
Loop System ◽  
Loop Model ◽  
Closed Loop System ◽  
Identification Error ◽  
Closed Loop Identification ◽  
Integral Controller

This paper studies the closed-loop system identification (ID) error when a dynamic integral controller is used. Pseudo-random binary sequence (PRBS) q-Markov covariance equivalent realization (Cover) is used to identify the closed-loop model, and the open-loop model is obtained based upon the identified closed-loop model. Accurate open-loop models were obtained using PRBS q-Markov Cover system ID directly. For closed-loop system ID, accurate open-loop identified models were obtained with a proportional controller, but when a dynamic controller was used, low-frequency system ID error was found. This study suggests that extra caution is required when a dynamic integral controller is used for closed-loop system identification. The closed-loop identification framework also has significant effects on closed-loop identification error. Both first- and second-order examples are provided in this paper.

When Causality Does Not Imply Correlation: More Spadework at the Foundations of Scientific Psychology

2011 ◽  
Vol 108 (3) ◽  
pp. 943-954 ◽  
Author(s):  
Richard S. Marken ◽  
Brittany Horth
Keyword(s):  
Sensory Input ◽  
Closed Loop ◽  
Causal Model ◽  
Open Loop ◽  
Tracking Performance ◽  
Loop Model ◽  
Surprising Result ◽  
Scientific Psychology ◽  
Mouse Movements

Experimental research in psychology is based on an open-loop causal model which assumes that sensory input causes behavioral output. This model was tested in a tracking experiment where participants were asked to control a cursor, keeping it aligned with a target by moving a mouse to compensate for disturbances of differing difficulty. Since cursor movements (inputs) are the only observable cause of mouse movements (outputs), the open-loop model predicts that there will be a correlation between input and output that increases as tracking performance improves. In fact, the correlation between sensory input and motor output is very low regardless of the quality of tracking performance; causality, in terms of the effect of input on output, does not seem to imply correlation in this situation. This surprising result can be explained by a closed-loop model which assumes that input is causing output while output is causing input.

scholarly journals Closed-loop updating of wind turbine models using blade pitch excitation

2021 ◽  
Author(s):  
Bo Marcel Schwarz-Wolf ◽  
Martin Dalgaard Ulriksen ◽  
Dawid Augustyn ◽  
Martin Skovmand Jensen ◽  
Ronnie Refstrup Pedersen
Keyword(s):  
Wind Turbine ◽  
Output Feedback ◽  
Closed Loop ◽  
Model Updating ◽  
Element Model ◽  
Open Loop ◽  
Model Parameters ◽  
Linear Transformations ◽  
Application Study ◽  
Ill Posed

Structural monitoring of wind turbines often involves the use of a numerical model, which must be regularly updated to yield an adequate representation of the governing dynamics. The model updating can be cast as an inverse problem, in which selected model parameters are estimated by minimizing the discrepancy between experimental target poles and model-predicted ones. An issue that may prevail in this setting is that the problem will be ill-posed, because the number of model parameters to be updated exceeds the number of target poles. The noted issue can be remedied by a virtual implementation of output feedback, which allows for computation of multiple closed-loop (CL) eigenstructures through non-linear transformations of the open-loop transfer matrix. The present paper offers an application study, in which virtual output feedback is used for updating numerical wind turbine models based on CL system poles. In particular, we explore the feasibility of employing the excitation stemming from the blade pitch control system as the input in the open-loop input-output realization required in the virtual output feedback. The methodological concept is outlined, and subsequently the applicability of the procedure is tested numerically in the context of simulations with a finite element model of a parked wind turbine. The preliminary findings suggest that procedures for customizing the pitch excitation to yield sufficient system excitation are required for the CL model updating to be feasible.

scholarly journals Control-Oriented High-Frequency Turbomachinery Modeling: Single-Stage Compression System 1D Model

1993 ◽  
Author(s):  
O. O. Badmus ◽  
S. Chowdhury ◽  
K. M. Eveker ◽  
C. N. Nett
Keyword(s):  
Experimental Data ◽  
High Frequency ◽  
Closed Loop ◽  
Open Loop ◽  
Rotating Stall ◽  
Single Stage ◽  
Model Parameters ◽  
Compression System ◽  
First Order ◽  
Direct Model

In this paper, a 1D unsteady compressible viscous flow model of a generic compression system previously developed by the authors is applied to a multi-stage axial compressor experimental rig configured for single–stage operation. The required model parameters and maps are identified from experimental data. The resulting model is an explicit system of 9 first order ODE’s. The model inputs are compressor speed, nozzle area, compressor discharge bleed area, plenum bleed area, inlet total pressure and entropy, and nozzle and bleed exit static pressures. The model and experimental data are compared with respect to both open–loop uncontrolled and closed–loop controlled behaviors. These comparisons focus on i) forced transients and ii) global nonlinear dynamics and bifurcations. In all cases the comparison between the model and experimental data is excellent. Of particular interest is the ability of the model, which does not include any hysteretic maps, to predict experimentally observed hysteresis with respect to the onset and cessation of surge. This predictive capability of the model manifests itself as the coexistence of a stable equilibrium (rotating stall) and a stable periodic solution (surge) in the model at a single fixed set of system input values. Also of interest is the fact that the controllers used for closed–loop comparisons were designed directly from the model with no a posteriori tuning of controller parameters. Thus, the excellent closed–loop comparisons between the model and experimental data provide strong evidence in support of the validity of the model for use in direct model based controller design. The excellent agreement between the model and experimental data summarized above is attributed in large part to the use of effective lengths within the model, as functions of axial Mach number and nondimensional compressor rotational speed, as prescribed by the modeling technique. The use of these effective lengths proved to be far superior to the use of physical lengths. The use of these effective lengths also provided substantial improvement over the use of physical lengths coupled with fixed first order empirical lags, as proposed by other authors for the modeling of observed compressor dynamic lag. The overall success of this model is believed to represent a positive first step toward a complete experimental validation of the approach to control–oriented high–frequency turbomachinery modeling being developed by the authors.

scholarly journals Open-loop organization of thalamic reticular nucleus and dorsal thalamus: a computational model

2015 ◽  
Vol 114 (4) ◽  
pp. 2353-2367 ◽  
Author(s):  
Adam M. Willis ◽  
Bernard J. Slater ◽  
Ekaterina D. Gribkova ◽  
Daniel A. Llano
Keyword(s):  
Closed Loop ◽  
Open Loop ◽  
Tunable Filter ◽  
Cortical Activation ◽  
Thalamic Reticular Nucleus ◽  
Reticular Nucleus ◽  
Loop Model ◽  
Dependent Manner ◽  
Dorsal Thalamus ◽  
Low Threshold

The thalamic reticular nucleus (TRN) is a shell of GABAergic neurons that surrounds the dorsal thalamus. Previous work has shown that TRN neurons send GABAergic projections to thalamocortical (TC) cells to form reciprocal, closed-loop circuits. This has led to the hypothesis that the TRN is responsible for oscillatory phenomena, such as sleep spindles and absence seizures. However, there is emerging evidence that open-loop circuits are also found between TRN and TC cells. The implications of open-loop configurations are not yet known, particularly when they include time-dependent nonlinearities in TC cells such as low-threshold bursting. We hypothesized that low-threshold bursting in an open-loop circuit could be a mechanism by which the TRN could paradoxically enhance TC activation, and that enhancement would depend on the relative timing of TRN vs. TC cell stimulation. To test this, we modeled small circuits containing TC neurons, TRN neurons, and layer 4 thalamorecipient cells in both open- and closed-loop configurations. We found that open-loop TRN stimulation, rather than universally depressing TC activation, increased cortical output across a broad parameter space, modified the filter properties of TC neurons, and altered the mutual information between input and output in a frequency-dependent and T-type calcium channel-dependent manner. Therefore, an open-loop model of TRN-TC interactions, rather than suppressing transmission through the thalamus, creates a tunable filter whose properties may be modified by outside influences onto the TRN. These simulations make experimentally testable predictions about the potential role for the TRN for flexible enhancement of cortical activation.

scholarly journals Iterative LQG Controller Design Through Closed-Loop Identification

1996 ◽  
Vol 118 (2) ◽  
pp. 366-372 ◽  
Author(s):  
Min-Hung Hsiao ◽  
Jen-Kuang Huang ◽  
David E. Cox
Keyword(s):  
Kalman Filter ◽  
State Feedback ◽  
Closed Loop ◽  
Controller Design ◽  
Open Loop ◽  
The State ◽  
Magnetic Suspension ◽  
Loop Model ◽  
Closed Loop Identification ◽  
Noise Statistics

This paper presents an iterative LQG controller design approach for a linear stochastic system with an uncertain openloop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQG controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

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