Feed Forward Mechanism in Human Body Examples

Feedforward Control

Physics of Physiological Measurements

J. Werner , in Comprehensive Biomedical Physics, 2014

5.02.2 Control by the Autonomous Nervous System

Both feedforward and feedback control of many body functions are performed without any conscious effort. This is achieved by a part of the nervous system called the autonomous nervous system. The control centers are situated mainly in the brainstem and in a part of the diencephalon, called the hypothalamus. The latter is the upper integrative center for all unconsciously performed body processes and, simultaneously, it is the neuro-humoral interface for all control processes based on excretion of hormones and their transportation via the blood vessels to the effector organs. In the periphery, the autonomous nervous system can be separated into two antagonistic parts, the sympathetic and the parasympathetic systems. Most effector organs are controlled in a synergistic manner by both systems. From both the central sympathetic and parasympathetic system, two autonomous peripheral neuron types, arranged in series, connect to the effector organs. They also control the special excitatory and conductive system and the working muscle of the heart, modifying cardiac processes via trains of action potentials, which control the processes at the synaptic endings of the sympathetic and the parasympathetic system by excretion of transmitter substances (sympathetic system: noradrenaline; parasympathetic system: acetylcholine). The sinus node in the right atrium, the AV-node, and the atrial myocardium have synaptic contacts to both the sympathetic and the parasympathetic system, whereas the ventricular myocardium is essentially controlled by the sympathetic system ( Figure 2 ). Sympathetic activation causes an increase in heart rate (positive chronotropic effect) at the sinus node, a shortening of the conductive AV-delay experienced by action potentials (positive dromotropic effect) at the AV-node, and an increase in contractility both in the atrial and ventricular myocardium (positive inotropic effect). Parasympathetic activation triggers a reduction in heart rate (negative chronotropic effect), an increase in the AV-conduction time (negative dromotropic effect), and a weakening of atrial contraction (small negative inotropic effect). These local processes may be supported by an adrenaline/noradrenaline mixture excreted via sympathetic activation into the adrenal medulla, which reaches the heart via the blood vessels.

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ACTIVE ISOLATION

S. Griffin , D. Sciulli , in Encyclopedia of Vibration, 2001

Equivalence of Feedback and Feedforward Control

Since the form of the feedforward control filter is dependent on both the path between the disturbance and the system to be controlled and the nature of the disturbance, it is often necessary to make the feedforward control filter-adaptive. Adaptation of the feedforward controller is accomplished by feeding back the error sensor signal to an adaptive feedforward filter, as shown in Figure 3, applied to the same system considered in Figure 1.

Figure 3. Feedforward control with adaptive path.

Since the adaptation of the feedforward controller is dependent on the error signal, a feedback path is introduced into the system. For the case of sinusoidal disturbance, there is an equivalent feedback controller which exhibits exactly the same performance characteristics as the feedforward controller.

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Structure for Next Generation Discharge Control Systems

G. Raupp , ... ASDEX Upgrade Team, in Fusion Technology 1996, 1997

2 HARDWARE-ORIENTED CONTROL

Discharge control requires to execute the sequence of actions defined by the experimentalist, and to feedback control those quantities not accessible to feedforward control with the required accuracy.

In early Tokamak research sequencing of the discharge followed a fixed scheme. A clock was used to generate predefined triggers at predefined times initiating desired actions. Embedded in this scheme was feedback control, limited to few quantities and diagnostic inputs. Analogue control systems were implemented with central trigger to sequence independent PID controllers, for plasma current, density and centre position, each with fixed signal inputs and outputs, and a hardware-based algorithm.

When digital controllers became available, this hardware-oriented set-up was kept, now built around the central processor, with fixed private I/O. Increased computing power allowed software-based control tows and elaborate multi-variable algorithms. Logical structures in software enabled to switch between algorithms to react to plasma events. Operation functions were added, e.g. to download the discharge program into memory, to protocol process data, and to access protection systems. However, as controller behaviour is determined by the fixed input-calculation-output cycle, dynamic collaboration of controllers is difficult, resulting in weakly coupled or stand-alone configurations.

Future plasma control systems will have to process more signals, feedback control more and more complex quantities, perform profile control, and increase accuracy. The question is whether the progress of digital technique with steadily increasing I/O capacity and processing power will be sufficient to comply with future requirements or whether there are reasons to search for a different structure for control systems.

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FEEDFORWARD CONTROL OF VIBRATION

C.R. Fuller , in Encyclopedia of Vibration, 2001

Control of Flexural Waves in Beams

In many applications it is desired to control vibrational waves traveling down long slender structures. The struts that support a helicopter gear box are a good example. By application of feedforward control to the struts, it is possible to isolate vibrations from the gearbox traveling down the struts to the helicopter fuselage. Figure 4 shows a schematic arrangement of a feedforward control system designed to suppress flexural waves in thin beams. In this case, the reference signal is generated by a single accelerometer located on the beam. A single control input is applied to the beam downstream of the reference sensor by an electromagnetic actuator. An error sensor is located further downstream of the beam and used to adapt the digital controller.

Figure 4. An adaptive system for the active control of broadband flexural disturbances on a beam.

The beam was anechoically terminated downstream of the error sensor ensuring only positive traveling waves (to the right) occur in the system. A disturbance which generated broadband random flexural waves was applied to the left end of the beam. This example illustrates the aspects which influence the ease and difficulty of applying feedforward control. Since only flexural waves (due to the source) travel in the beam, then total vibrational reduction is theoretically possible with a SISO controller. Thus the application is relatively simple in a spatial sense and as a result has a low requirement in terms of channels of control. In this case global reductions (throughout an extended area or volume) are readily achievable with a SISO system. However, the application is complicated in the temporal sense in that the excitation is random, so causality in the control path and lengths of the FIR filters are important issues. For the controller of this example, the delay through the control path was measured to be approximately 2.4   ms. The beam used in the test was manufactured from steel with a thickness of 6   mm. Flexural waves in beams are dispersive, that is, their phase and group velocity vary with frequency (see BEAMS). At low frequencies, the waves travel relatively slowly. As the frequency is increased, the waves travel with increasing velocity. For the beam considered here, the delay through the beam from the reference sensor to the control actuator is less than the control path at 800   Hz. Furthermore, the reference sensor will not only pick up the disturbance field but also the control response traveling in the negative direction. For good performance, the resultant feedback from the control actuator must be compensated for, as discussed previously.

Figure 5 shows the attenuation achieved in experimental testing. In this case, the control paradigm used was the feedforward filtered-x with feedback removal. Note that there are large attenuations of the output of the error sensor between 200 and 800   Hz. The fall off in performance above 800   Hz is due to the control system becoming acausal as discussed previously. A measurement of the coherence between the reference and error sensor revealed that the coherence is low, below 200   Hz, due to external sources of vibration transmitted through the beam suspension system at various points. As outlined above, a low value of coherence between the reference and error signal will result in poor attenuation.

Figure 5. The power-spectral density at the response error sensor on the beam illustrated in Figure 4 before control (––) and after the adaptive controller has converged (---).

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Sensors for Control

Clarence W. de Silva , in Encyclopedia of Physical Science and Technology (Third Edition), 2003

I.C Instrumentation and Design

In a control system, one is interested in

•

The plant, or the dynamic system to be controlled

•

Signal measurement for system evaluation (monitoring) and for feedback and feedforward control

•

The drive system that actuates the plant

•

Signal conditioning by filtering and amplification and signal modification by modulation, demodulation, ADC, DAC, and so forth, into an appropriate form

•

The controller that generates appropriate drive signals for the plant

Each function or operation within a control system can be associated with one or more physical devices, components, or pieces of equipment, and one hardware unit may accomplish several of the control system functions. By instrumentation, in the present context, we mean the identification of these various instruments or hardware components with respect to their functions, operation, and interaction with each other and the proper selection and interfacing of these components for a given application—in short, "instrumenting" a control system.

By design, we mean the process of selecting suitable equipment to accomplish various functions in the control system; developing the system architecture; matching and interfacing these devices; selecting the parameter values, depending on the system characteristics; and carrying out necessary modifications or integration of new components, in order to achieve the desired objectives of the overall control system (i.e., to meet design specifications), preferably in an optimal manner and according to some performance criterion. In the present context, design is included as an instrumentation objective. Note that, there can be many designs that meet a given set of performance requirements. The main steps in an instrumentation task are as follows:

1.

Determine the goals of the control system. Prescribe performance specifications for the system in order to achieve these goals.

2.

Identify the components of the overall system.

3.

Perform modeling and analysis of the system, as necessary.

4.

Select parameters of the system according to the performance specifications.

5.

Integrate the overall system. This step will involve component interfacing and matching, and may need additional hardware and software as well.

6.

Carry out system testing, tuning, and further modification if necessary.

Note that several iterations may be necessary in Steps 2 through 6. Identification of key design parameters, modeling of various components, and analysis are often useful in the design process.

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Automation of Crystal Growth from Melt

Jan Winkler , Michael Neubert , in Handbook of Crystal Growth: Bulk Crystal Growth (Second Edition), 2015

28.2.4 Feed-Forward Controller

The feed-forward controller calculates the manipulated values (like heater powers or crystal/crucible/feed rod translation rates) from the reference values of the controlled variables (e.g., crystal diameter) generated by the reference trajectory generator. This can be done empirically or by means of a mathematical model describing the system behavior. In crystal growth, a common method in practice is to determine the feed-forward control by careful analysis of repeated growth runs, resulting in a trajectory for the manipulated variables that can then be used as part of the process recipe in the following runs.

While this method is widely accepted, it suffers from the fact that it is extremely time consuming and, thus, expensive. It functions only if the same conditions are repeatedly met and there are no significant variations from run to run. However, any change in plant setup or change in reference values for the controlled variables means repeating this procedure. Finally, not all details of the system dynamics can be determined by this empirical approach.

If a sufficiently exact model of the process is available, this model can be used as a basis for determining proper feed-forward control trajectories that then can be fine-tuned in an empirical manner afterward. A short example is given as follows illustrating this strategy [19]. One may have a system with the manipulated variable $u\in \mathbb{R}$ (for example, a heater power), the output variable $y\in \mathbb{R}$ (which has to be controlled, e.g., a bright meniscus ring diameter in Cz growth), and two internal states $x_1,x_2\in \mathbb{R}$ (e.g., the crystal diameter and a value describing the cone angle). Its dynamics may be captured by the following very simple, fictive lumped parameter model of order 2:

(28.1a) ${\dot{x}}_1={\mathit{x}}_2$

(28.1b) ${\dot{x}}_2=x_1^2+\left(x_2+2\right)u$

(28.1c) $y=x_1\text{.}$

From this model, a model-based feed-forward control can be easily calculated. Solving Eqn (28.1b) for the heater power u one obtains:

(28.2) $u=\frac{{\dot{x}}_2-x_1^2}{x_2+2}\text{.}$

Now, one would like to steer the system, namely the diameter y, along a reference trajectory $t\mapsto y_{\text{ref}}\left(t\right)$ . This reference trajectory is assumed to be two times continuously differentiable, i.e., ${\dot{y}}_{\text{ref}}\left(t\right),{\ddot{y}}_{\text{ref}}\left(t\right)$ exist. Since according to system (28.1) one has $x_1=y,x_2={\dot{x}}_1=\dot{y},\text{and}{\dot{x}}_2=\ddot{y}$ the resulting reference trajectory for the manipulated variable u reads (cf. Eqn (28.2)):

(28.3) $u_{\text{ref}}=\frac{{\ddot{y}}_{\text{ref}}-y_{\text{ref}}^2}{{\dot{y}}_{\text{ref}}+2}\text{.}$

Using the calculated feed-forward control, one might be able to steer the system along its reference trajectory if the system is stable, only small perturbations are acting on the system, and the model is accurate enough.

The example given above demonstrates the general design procedure of a lumped parameter model-based feed-forward control. However, the dynamics of crystal growth process will certainly never be captured sufficiently by such simple models. Hence, this example also reveals one of the main problems in control system design for crystal growth—finding the appropriate balance between simple models suitable for control system design and accuracy of the models describing the system dynamics. The sections dealing with the control of the particular growth processes will focus especially on this topic.

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Cybernetics and Second-Order Cybernetics

Francis Heylighen , Cliff Joslyn , in Encyclopedia of Physical Science and Technology (Third Edition), 2003

IV.B Mechanisms of Control

While the perturbations resisted in a control relation can originate either inside (e.g., functioning errors or quantum fluctuations) or outside of the system (e.g., attack by a predator or changes in the weather), functionally we can treat them as if they all come from the same, external source. To achieve its goal in spite of such perturbations, the system must have a way to block their effect on its essential variables. There are three fundamental methods to achieve such regulation: buffering, feedback and feedforward (see Fig. 1).

FIGURE 1. Basic mechanisms of regulation, from left to right: buffering, feedforward and feedback. In each case, the effect of disturbances D on the essential variables E is reduced, either by a passive buffer B, or by an active regulator R.

Buffering is the passive absorption or damping of perturbations. For example, the wall of the thermostatically controlled room is a buffer: the thicker or the better insulated it is, the less effect fluctuations in outside temperature will have on the inside temperature. Other examples are the shock absorbers in a car, and a reservoir, which provides a regular water supply in spite of variations in rain fall. The mechanism of buffering is similar to that of a stable equilibrium: dissipating perturbations without active intervention. The disadvantage is that it can only dampen the effects of uncoordinated fluctuations; it cannot systematically drive the system to a non-equilibrium state, or even keep it there. For example, however well-insulated, a wall alone cannot maintain the room at a temperature higher than the average outside temperature.

Feedback and feedforward both require action on the part of the system to suppress or compensate the effect of the fluctuation. For example, the thermostat will counteract a drop in temperature by switching on the heating. Feedforward control will suppress the disturbance before it has had the chance to affect the system's essential variables. This requires the capacity to anticipate the effect of perturbations on the system's goal. Otherwise the system would not know which external fluctuations to consider as perturbations, or how to effectively compensate their influence before it affects the system. This requires that the control system be able to gather early information about these fluctuations.

For example, feedforward control might be applied to the thermostatically controlled room by installing a temperature sensor outside of the room, which would warn the thermostat about a drop in the outside temperature, so that it could start heating before this would affect the inside temperature. In many cases, such advance warning is difficult to implement, or simply unreliable. For example, the thermostat might start heating the room, anticipating the effect of outside cooling, without being aware that at the same time someone in the room switched on the oven, producing more than enough heat to offset the drop in outside temperature. No sensor or anticipation can ever provide complete information about the future effects of an infinite variety of possible perturbations, and therefore feedforward control is bound to make mistakes. With a good control system, the resulting errors may be few, but the problem is that they will accumulate in the long run, eventually destroying the system.

The only way to avoid this accumulation is to use feedback, that is, compensate an error or deviation from the goal after it has happened. Thus feedback control is also called error-controlled regulation, since the error is used to determine the control action, as with the thermostat which samples the temperature inside the room, switching on the heating whenever that temperature reading drops lower than a certain reference point from the goal temperature. The disadvantage of feedback control is that it first must allow a deviation or error to appear before it can take action, since otherwise it would not know which action to take. Therefore, feedback control is by definition imperfect, whereas feedforward could in principle, but not in practice, be made error-free.

The reason feedback control can still be very effective is continuity: deviations from the goal usually do not appear at once, they tend to increase slowly, giving the controller the chance to intervene at an early stage when the deviation is still small. For example, a sensitive thermostat may start heating as soon as the temperature has dropped one tenth of a degree below the goal temperature. As soon as the temperature has again reached the goal, the thermostat switches off the heating, thus keeping the temperature within a very limited range. This very precise adaptation explains why thermostats in general do not need outside sensors, and can work purely in feedback mode. Feedforward is still necessary in those cases where perturbations are either discontinuous, or develop so quickly that any feedback reaction would come too late. For example, if you see someone pointing a gun in your direction, you would better move out of the line of fire immediately, instead of waiting until you feel the bullet making contact with your skin.

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Spectroscopy for Process Analytical Technology (PAT)

Erik Skibsted , Søren Balling Engelsen , in Encyclopedia of Spectroscopy and Spectrometry (Second Edition), 2010

Process Monitoring and Control

Manufacturing methods can largely be divided into continuous or batch-operated manufacturing. In continuous manufacturing, the objective of a spectroscopic analyzer would typically be to monitor the level of certain analytes, provide information to a feed-backward control, and keep the level of the analytes within specified ranges. Accurate, precise, and speedy analysis by spectroscopy combined with a feed-backward control provides a well-controlled process that is easily adjusted to different levels and with low variability. In the following example (Figure 6), ammonium was determined by on-line near-infrared spectroscopy and used for automatically controlled addition of ammonium to maintain certain desired levels that gave a very precise and swift control with much narrower specification of the ammonium concentration and thus the product.

Figure 6. Automated control by on-line near-infrared feedback. The arrow indicates the point at which a spectroscopic analyzer was introduced for control of the process. The step change at day 12 indicates a change in recipe.

In batch-operated manufacturing, the manufacturing process recipe is driven with different serially connected unit operations. As an example, Figure 7 depicts a batch-operated manufacturing process where the raw materials enter process A, followed by process B, before the final product is available.

Figure 7. A batch-operated manufacturing is typically made of several unit operations providing the final product. Spectroscopic analyzers can be placed throughout the chain of unit operations either by at-line-based methods, for example, analyzing complex raw materials, or by on-line or in-line based methods, monitoring the progression of individual unit operations.

There are several opportunities for process monitoring and control of batch processes. Spectroscopic analyzers can be used to monitor the progression of each step and provide feed-backward control to achieve optimal product quality under that particular unit operation and determine the finalization of the step before proceeding to the next unit operation. In industries that use complex and variable raw materials, spectroscopic analyzers can be used to analyze the raw materials entering the manufacturing facility and decide how to process them optimally. By fingerprinting the complex raw materials and using a feed-forward control loop, the manufacturing process is adjusted to accommodate raw material variations. The same control principle can be applied by spectroscopic analysis of the intermediary product leaving process A and then used to feed-forward control process B for optimal recipe settings.

A typical example of raw material analysis is near-infrared analysis of yeast extract, a typical amino acid source used in fermentation. Several batches of yeast extract were analyzed with near-infrared spectra. The different yeast extract batches were then used in fermentations, and the foaming tendency in the batches was recorded. Foaming is a nondesirable situation in the fermentation process. In Figure 8(a), near-infrared spectra of yeast extract samples from different batches are depicted. The spectra were analyzed with a PCA model (Figure 8(b)), and in the score plot, the scores are colored according to foaming tendency of the different yeast extracts. The clustering in the scores was well correlated with the foaming tendency, which shows how the PCA model of the near-infrared spectra could be used to predict the foaming tendency, and with this control, manufacturing problems could be avoided.

Figure 8. Scatter-corrected near-infrared spectra of yeast extract raw material (a) and PCA score plot (b). The spectra and scores are colored according to foaming problems in fermentation (black circles=no foaming, red triangles=little foaming, green stars=heavy foaming).

With rapid in-line spectroscopic analyzers, it is possible to reveal process variations and control these. In-line near-infrared spectroscopy was used to quantify the water concentration in a pharmaceutical powder mixture during a granulation and drying process in a fluid bed reactor (Figure 9). The first part of the process was a two-step spray phase where first an aqueous polymeric solution and later pure water were sprayed onto the powder that was fluidized by heated air blown from the bottom up through the fluid bed reactor. A short spraying pause when shifting to pure water caused a minor drying of the powder (horizontal arrow in Figure 9), which was clearly detected by the near-infrared reflectance probe. After the spray phase started the drying phase, which was automatically terminated when the powder reached a set-point temperature. Three lots were produced with identical process recipes, all dried to the same final product temperature. The lots showed large variations in the water content during the spray and drying phases as well as final water concentration. A large difference in drying time was observed (from 38   min of Lot C to 58   min of Lot B). Prolonged drying time can cause final quality defects such as increased mechanical shear on powder granules, resulting in too many fine particles. The differences in water concentration and drying kinetics are caused by typical noncontrollable process variations such as airflow variations and humidity differences in the inlet air to the fluid bed reactor. By using near-infrared reflectance, water concentration can be monitored and controlled. For example, the inlet air temperature can be adjusted continuously in a feedback control loop and the drying terminated by endpoint control. This will decrease product variability and reduce likelihood of quality defects.

Figure 9. Loss on drying (w/w%) is analyzed directly in a fluidized bed using a near-infrared reflectance probe. Three lots were produced with identical process settings, but the near-infrared analyzer revealed large differences due to variable input air conditions. Shift from polymeric solution to pure water is identified with a horizontal arrow.

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ACTIVE CONTROL OF CIVIL STRUCTURES

T.T. Soong , B.F. SpencerJr., in Encyclopedia of Vibration, 2001

Active, Hybrid, and Semiactive Control Systems

An active structural control system has the basic configuration shown schematically in Figure 1A. It consists of: (1) sensors located about the structure to measure either external excitations, or structural response variables, or both; (2) devices to process the measured information and to compute necessary control force needed based on a given control algorithm; and (3) actuators, usually powered by external sources, to produce the required forces.

Figure 1. Structure with various schemes. (A) Structure with active control; (B) structure with hybrid control; (C) structure with semiactive control. PED, passive energy dissipation.

When only the structural response variables are measured, the control configuration is referred to as feedback control since the structural response is continually monitored and this information is used to make continual corrections to the applied control forces. A feedforward control results when the control forces are regulated only by the measured excitation, which can be achieved, for earthquake inputs, by measuring accelerations at the structural base. In the case where the information on both the response quantities and excitation is utilized for control design, the term feedback–feedforward control is used.

To see the effect of applying such control forces to a linear structure under ideal conditions, consider a building structure modeled by an n-degree-of-freedom lumped mass-spring-dashpot system. The matrix equation of motion of the structural system can be written as:

[1] $M\ddot{x}(t)+C\dot{x}(t)+Kx(t)=Du(t)+Ef(t)$

where M, C, and K are the n×n mass, damping and stiffness matrices, respectively, x(t) is the n-dimensional displacement vector, the m-vector f(t) represents the applied load or external excitation, and r-vector u(t) is the applied control force vector. The n×r matrix D and the n×m matrix E define the locations of the action of the control force vector and the excitation, respectively, on the structure.

Suppose that the feedback–feedforward configuration is used in which the control force u(t) is designed to be a linear function of measured displacement vector x(t) velocity vector x˙(t) and excitation f(t). The control force vector takes the form:

[2] $u(t)=G_{x}x(t)+G_{\dot{x}}\dot{x}(t)+G_{f}f(t)$

in which G _x , $G_{\dot{x}}$ , and G _f are respective control gains which can be time-dependent.

The substitution of eqn (2) into eqn (1) yields:

[3] $M\ddot{x}(t)+(C-D{G}_{\dot{x}})\dot{x}(t)+(K-D{G}_x)x(t)=(E+D{G}_f)f(t)$

Comparing eqn (3) with eqn (1) in the absence of control, it is seen that the effect of feedback control is to modify the structural parameters (stiffness and damping) so that it can respond more favorably to the external excitation. The effect of the feedforward component is a modification of the excitation. The choice of the control gain matrices G _x , $G_{\dot{x}}$ , and G _f depends on the control algorithm selected.

In comparison with passive control systems, a number of advantages associated with active control systems can be cited; among them are: (1) enhanced effectiveness in response control; the degree of effectiveness is, by and large, only limited by the capacity of the control systems; (2) relative insensitivity to site conditions and ground motion; (3) applicability to multihazard mitigation situations; an active system can be used, for example, for motion control against both strong wind and earthquakes; and (4) selectivity of control objectives; one may emphasize, for example, human comfort over other aspects of structural motion during noncritical times, whereas increased structural safety may be the objective during severe dynamic loading.

While this description is conceptually in the domain of familiar optimal control theory used in electrical engineering, mechanical engineering, and aerospace engineering, structural control for civil engineering applications has a number of distinctive features, largely due to implementation issues, that set it apart from the general field of feedback control. In particular, when addressing civil engineering structures, there is considerable uncertainty, including nonlinearity, associated with both physical properties and disturbances such as earthquakes and wind, the scale of the forces involved can be quite large, there are only a limited number of sensors and actuators, the dynamics of the actuators can be quite complex, the actuators are typically very large, and the systems must be failsafe.

It is useful to distinguish between several types of active control systems currently being used in practice. The term hybrid control generally refers to a combined passive and active control system, as depicted in Figure 1B. Since a portion of the control objective is accomplished by the passive system, less active control effort, implying less power resource, is required.

Similar control resource savings can be achieved using the semiactive control scheme sketched in Figure 1C, where the control actuators do not add mechanical energy directly to the structure, hence bounded-input bounded-output stability is guaranteed. Semiactive control devices are often viewed as controllable passive devices.

A side benefit of hybrid and semiactive control systems is that, in the case of a power failure, the passive components of the control still offer some degree of protection, unlike a fully active control system.

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HYBRID CONTROL

J. Tang , K.W. Wang , in Encyclopedia of Vibration, 2001

Introduction

Traditionally, structural vibration control techniques have been categorized into two categories, namely, passive and active. Classical passive methods include material damping enhancement, viscoelastic dampers, frictional dampers and joints, and various vibration absorbers and isolation schemes. The advantages of the passive approach are that the devices are relatively simple and cheap, and the system will always be stable since the control is realized by energy dissipation and/or energy redistribution with no external power being added to the system. However, since it produces fixed designs, such schemes might not be optimal or even effective when the system or the operating condition changes. In addition, these schemes usually work well at the high-frequency region or within a narrow frequency range, but often have poor low-frequency performance. Due to the rapid progress in modern electronics and digital signal-processing technique, active systems with feedback/feedforward control schemes have become a viable means for vibration suppression. A typical active control system consists of the plant, actuator(s), sensor(s), and the control electronics. The vibration control is achieved by applying a secondary input to the structure, thereby modifying the system dynamic response to a desirable pattern. While active systems are generally more effective than passive methods, they have the disadvantages such as being complicated and expensive, having the potential to destabilize the system, and being sensitive to system modeling error and uncertainties.

It is clear that the passive and active vibration suppression approaches both have respective strength and weaknesses. This gives rise to the idea of active–passive hybrid vibration controls. In a hybrid system, the active and passive components are synthesized in an integrated manner such that their combined effect would be superior to that of the individual active or passive actions. If properly designed, hybrid controls could outperform the purely passive and active approaches while requiring much less control power input than active systems. Also, since energy is almost always being dissipated/redistributed by the passive components, they are much more robust and stable than the active approach. In other words, they could have the advantages of both the passive (stability, failsafe, lower power consumption, good high-frequency performance) and active (high performance, especially in the low-frequency range, feedback/feedforward actions) schemes.

In general, the design of a hybrid vibration control system involves the selection/determination of the active and passive components. It is a natural thought first to identify the relations and performance tradeoffs between the active and passive control mechanisms, and then to have the two complement each other such that a system with the best control performance and least control effort can be achieved. There are two schools of thoughts in designing a hybrid system. One is to integrate individual active and passive devices on to the host structure to synthesize a global hybrid control. The other is to design hybrid devices at a local level, such that the actuators will have self-contained active–passive hybrid actions. For both approaches, design strategies need to be developed such that the active and passive actions can be integrated in an optimal manner. A discussion of the design methodologies is presented in the following section. These strategies are generic and are not tied to specific passive control mechanisms and active control actuator/sensor selections. Lastly, some representative self-contained hybrid actuator configuration will be presented.

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