Forward Feed

Feedforward Control

Feedforward control is an effective way to rapidly eliminate the effect of the disturbance on the output.

From: Electric Motor Control , 2017

Process Control Fundamentals

Saeid Mokhatab , William A. Poe , in Handbook of Natural Gas Transmission and Processing, 2012

14.4.9.1 Feedforward Control

Feedforward control differs from feedback control in that the load or primary disturbance is measured and the manipulated variable is adjusted so that deviations in the controlled variable from the setpoint are minimized. The controller can then reject disturbances before they affect the controlled variable. For accurate feedforward control, steady-state or dynamic analysis should be the basis for models that relate the effect of the manipulated and disturbance variable on the controlled variable. Since the model is an approximation and not all disturbances are measured, feedforward control should always be used in conjunction with feedback control. This combination will allow compensation for measured and unmeasured disturbances as well as model mismatch.

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Process Control

William A. Poe , Saeid Mokhatab , in Modeling, Control, and Optimization of Natural Gas Processing Plants, 2017

3.3.6.1 Feedforward Control

Feedforward control differs from feedback control in that the load or primary disturbance is measured and the manipulated variable is adjusted so that deviations in the controlled variable from the set point are minimized. The controller can then reject disturbances before they affect the controlled variable. For accurate feedforward control, steady-state or dynamic analysis should be the basis for models that relate the effect of the manipulated and disturbance variable on the controlled variable. Since the model is an approximation and not all disturbances are measured, feedforward control should always be used in conjunction with feedback control. This combination will allow compensation for measured and unmeasured disturbances as well as model mismatch (Seborg, 2011).

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Industrial control system simulation routines

Peng Zhang , in Advanced Industrial Control Technology, 2010

(1) Feed-forward control

Feed-forward control can be based on process models. A feed-forward controller has been combined with different feedback controllers; even the ubiquitous three-term proportional-integral-derivative (PID) controllers can be used for this purpose. A proportional-integral controller is optimal for a first-order linear process (expressed with a first-order linear differential equation) without time delays. Similarly, a PID controller is optimal for a second-order linear process without time delays. The modern approach is to determine the settings of the PID controller based on a model of the process, with the settings chosen so that the controlled responses adhere to user specifications. A typical criterion is that the controlled response should have a quarter decay ratio, or it should follow a defined trajectory, or that the closed loop has certain stability properties.

A more elegant technique is to implement the controller within an adaptive framework. Here the parameters of a linear model are updated regularly to reflect current process characteristics. These parameters are in turn used to calculate the settings of the controller, as shown schematically in Figure 19.7. Theoretically, all model-based controllers can be operated in an adaptive mode, but there are instances when the adaptive mechanism may not be fast enough to capture changes in process characteristics due to system nonlinearities. Under such circumstances, the use of a nonlinear model may be more appropriate. Nonlinear time-series, and neural networks, have been used in this context. A nonlinear PID controller may also be automatically tuned, using an appropriate strategy, by posing the problem as an optimization problem. This may be necessary when the nonlinear dynamics of the plant are time-varying. Again, the strategy is to make use of controller settings most appropriate to the current characteristics of the controlled process.

Figure 19.7. Schematic of adaptive controllers.

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Optimal control process of heat exchanger networks

Wilfried Roetzel , ... Dezhen Chen , in Design and Operation of Heat Exchangers and their Networks, 2020

9.4.1 Basic concepts of the model predictive control

Feedback and feedforward are two types of control schemes for systems that react automatically to the dynamical disturbance inputs. The control of any dynamic system, including heat exchangers and HENs, should be executed on the basis of automatic control principle. Traditional automatic control is based on PID feedback method, by which the feedback is the sum of proportional plus integral plus derivative. As is shown in Fig. 9.5 as an example, for controlling the outlet temperature of the hot stream at the set value, t″_h,set, the deviation Δz  = z  − z _ref between the measured outlet temperature t″_h and its target temperature t″_h,set are sent to the feedback controller. According to Δz and its variation history, the controller sends a manipulating signal y to adjust the bypass fraction so that the deviation Δz will approach zero again. Like all other PID feedback control systems, during the control process, the manipulation of input parameters is carried out only after the deviation of the controlled output variables has been detected; therefore lag error and sometime undesired fluctuations and/or instability cannot be avoided.

Fig. 9.5. Feedback control system.

A feedforward control method was developed to overcome these shortcomings. In a feedforward control system, the controller senses the deviation of inlet parameters and give the corresponding adjustments based on the predication of dynamic response of the system, as is shown in Fig. 9.6. For the traditional feedforward control, dynamic behavior of the system is determined by analytical methods or experimental methods, which may be expressed in differential equation form or transfer function form, and they can be converted each other by mathematical methods. In order to improve the control quality of feedforward control in its application in a HEN, to predict the transient responses of the HEN more accurately becomes necessary for the proper manipulation. Guan et al. (2004) used a mathematical model based on a distributed parameter approach to predict the dynamic behaviors of a plate-fin HE and the change of output parameters caused by certain disturbances can be calculated. The dynamic analysis model is embedded in the controller. When a disturbance of input parameter occurs, or the system should be switched from one state to a new operation state, the predictive model in the controller will predict the response of output parameters based on the sense of input parameter change, and then the controller instructs the manipulation of bypass valves; thus, the change of output parameter is compensated or the target change is carried out. The control process is essentially a compensation process.

Fig. 9.6. Model predictive feedforward control system.

Model predictive feedforward control is an open-loop control, but different from the common open-loop control, where the adjustments of input parameters are determined according to the relationships of the transfer functions (matrix) by applying the inverse Laplace transform to it, here, the model predictive controller manipulates according to the solution of transient responses of inlet disturbances; thus, better qualities (absolute errors and speed limits) are ensured. Especially when a distributed parameter approach is adopted to set up the model, the transient responses of the whole system upon disturbances can be predicted, including the inner parameters like temperature distribution inside the heat exchanger or HEN. This is a great help to improve the control quality, for example, when a bypass valve manipulates upon a certain disturbance to maintain the target outputs or to switch to a new operation state, because of the inertia of the system, quick manipulation is expected to eliminate the delay of the outputs, which could result in overadjustment. Small overmanipulation of outputs can usually be tolerated, but when the overmanipulation of outputs appears, unacceptable overshot of inner parameters such as unexpected high temperature could happen. By distributed parameter approach, inner parameters can be calculated at the same time so that these can be chosen as special constraints. The other features of distributed parameter model predictive control are the following:

(1)

It can be designed for multivariable processes; based on the solution of dynamics of multistream heat exchangers and their networks, a set of responses corresponding to different input disturbances can be given at the same time, while the traditional open-loop control can only compute the corrective action to one input variable. Therefore, this accurate model predictive feedforward control system can be used for multistream heat exchanger control, where the traditional open-loop control is difficult to be implemented.

(2)

As previously mentioned, the accurate model predictive control can choose the optimal control scheme from many choices under different constraints, which is impossible by the traditional open-loop control.

The implementation of distributed parameter model predictive feedforward control is a reverse question of dynamic solution obtained. The controller should give the corrective instruction based on the response solution upon the changes of objective input parameters to adjust the inputs of assistant fluid, namely, the changes of inputs should be given based on the response solution, but the solution obtaining calculation is to find the output response based on the information of input changes; the former is much difficult to be carried out. Thus, two steps are needed in the control execution: firstly, the solution obtaining calculation is implemented according to the change of objective input parameter, and then, the adjustment of auxiliary input is obtained by iterative calculations.

The model predictive feedforward control is a big improvement to traditional feedback control, but it still needs improvement. Because of the accuracy of the model adopted and the inertia of the system, it may not give the complete compensation when disturbances happened, and for no signal of output is feedback to the controller, the possible error cannot be adjusted. If the output error is feedback and the controller will act based on the integrated response, the system is a "true" model predictive control according to the control theory. The model predictive control system can be schematically shown in Fig. 9.7, where the feedback is included, but it is different from traditional feedback and feedforward system in that it is not the output, but the error of output compensation is feedback. Since Richalet et al. (1978), the predictive control method was developed by many researches (Clarke et al., 1987a,b; Soeterboek, 1990; Richalet, 1993). Some applications of model predictive control had been given for heat exchanger control (Hecker et al., 1997). Several books and thousands of papers about the model predictive control have been published. Intensive reviews of the developments in model predictive control and their applications were given by Garriga and Soroush (2010), Tran et al. (2014), Vazquez et al. (2014), Lio et al. (2014), Ellis et al. (2014), Karamanakos et al. (2014), Lee (2011), and Mayne (2014).

Fig. 9.7. Model predictive control system with feedback compensation.

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Control Approaches for Systems with Hysteresis

Lei Liu , Yi Yang , in Modeling and Precision Control of Systems with Hysteresis, 2016

5.3.2 Composite Hysteresis-Based Feedforward Control

Composite hysteresis-based feedforward control uses mechanical vibration, electrical dynamics, the static hysteresis effect, and the creep effect. The dynamics and effects will be compensated at broadband frequencies. Figure 5.12 shows inversion-based feedforward control using mechanical vibration, electrical dynamics, static hysteresis, and the creep effect. ${Ĝ}_{\text{c}}$ denotes the estimation of the linear creep effect G _c, ${Ĝ}_{\text{ev}}$ denotes the estimation of the mechanical vibration and electrical dynamics G _ev, Γ denotes the static hysteresis, x _r represents the reference or desired displacement, and y represents the output displacement.

Figure 5.12. Inversion-based feedforward control using mechanical vibration, electrical dynamics, static hysteresis, and the creep effect.

Figure 5.13 shows the tracking performance of the static hysteresis and creep based feedforward control. The peak-peak amplitude of the tracking error is less than 3%.

Figure 5.13. Inversion-based feedforward control of a sinusoidal signal at 10   Hz.

Figure 5.14 illustrates the tracking performance of a piezoelectric smart system obtained with use of the inversion of the mechanical vibration, electrical dynamics, static Preisach hysteresis, and the creep effect. The inversion-based feedforward control error is smaller than the open-loop error without compensation, but there is still an offset value. The composite hysteresis-based inversion-based feedforward control is effective at high frequencies.

Figure 5.14. Inversion-based feedforward control of a sinusoidal signal at 200   Hz (the dashed line represents the open-loop error without compensation and the solid line represents the inversion-based feedforward control error).

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MPC Design for Air-Handling Control of a Diesel Engine

Nassim Khaled , Bibin Pattel , in Practical Design and Application of Model Predictive Control, 2018

10.7 MPC Controller Structure

The multivariable controller implemented in this work consists of two main parts—feedforward and feedback (Fig. 10.8).

Figure 10.8. Controller structure.

Reprinted with permission SAE Copyright © 2014 SAE International. Further distribution of this material is not permitted without prior permission from SAE.

The role of feedforward control is to provide a fast responding, approximate actuator position to the controller during heavy transients. The feedforward part of the control is realized by a set of static lookup tables which are computed through an optimization-based routine performed on the control oriented model of the engine, and the desired setpoints for the control. The output of the lookup tables is filtered by first order filters. The time constants of the filters can be used as additional tuning parameters. The algorithm to compute the feedforward lookup tables is based on a numerical inversion of the control-oriented model in a specified grid of operating points. The tables are parameterized by a selected set of important external variables—in this case, engine speed and fuel injection quantity. The optimization in one point of the grid can be written as:

$\begin{array}{ll}{u}_{FF}^{*}\hfill & =\text{arg}\underset{u,\epsilon }{\min }\left\{\begin{array}{c}{\displaystyle \sum _{k\in \mathrm{tracked}}}{q}_k{\left(y_k-y_k^{sp}\right)}^2+{\displaystyle \sum _{k\in \mathrm{manipulated}}}{r}_k{\left(u_k-u_k^{sp}\right)}^2+\\ {\displaystyle \sum _{k\in \mathrm{limited}}}{w}_k{\left(y_k-{\overline{y}}_k-{\epsilon }_k\right)}^2+{\displaystyle \sum _{k\in \mathrm{limited}}}{w}_k{\left({\underset{\_}{y}}_k-{\epsilon }_k-y_k\right)}^2\end{array}\right\}\hfill \\ \mathrm{S}.\mathrm{t}.\hfill & {\underset{\_}{u}}_k\le u_k\le {\overline{u}}_k,\forall k\in \mathrm{manipulated}\hfill \\ \hfill & {\underset{\_}{y}}_k-{\epsilon }_k\le y_k\le {\overline{y}}_k+{\epsilon }_k,\forall k\in \mathrm{limited}\hfill \\ \hfill & {\epsilon }_k\ge 0,\forall k\in \mathrm{limited}\hfill \end{array}$

where q _k , w _k , r _k are weighting (tuning) parameters. For a given point of grid, y _k is k-th output of the model (it is a function of inputs u _k ) and $y_k^{sp}$ is the setpoint value. ${\overline{y}}_k$ and ${\underset{\_}{y}}_k$ are the upper and lower limits of the k-th output. $u_k^{sp}$ is the preferred position of k-th actuator, ${\overline{u}}_k^{sp}$ and ${\underset{\_}{u}}_k^{sp}$ are the upper and lower limits of the k-th manipulated variable. ${\epsilon }_k$ is the auxiliary variable that enables softening the constraints in case of conflicting or unfeasible limits.

The feedback portion takes care of disturbance rejection, model-engine mismatch (whether from production dispersion or aging), offset-free steady state tracking, and constraints handling. The feedback consists of a MPC controller in which only time-varying limits on actuators were employed, i.e.:

$\underset{\_}{u}(\mathrm{t}+\mathrm{k})\le u(\mathrm{t}+\mathrm{k}|\mathrm{k})\le \overline{u}(\mathrm{t}+\mathrm{k})$

The MPC cost function used in this work can be written as:

$J(u,x(\mathrm{t}))={\sum _{k=0}^N\Vert y(\mathrm{t}+\mathrm{k}|\mathrm{t})-y_{sp}(\mathrm{t})\Vert }_Q^2+{\sum _{k=1}^{N_c}\Vert \mathrm{\Delta }u(\mathrm{t}+\mathrm{k}|\mathrm{k})\Vert }_R^2$

where the first term in the cost function is penalization of tracking error over the prediction horizon N, and the second term is penalization of actuator movements over the control horizon Nc, i.e.:

$\mathrm{\Delta }u(\mathrm{t}+\mathrm{k}|\mathrm{k})=u(\mathrm{t}+\mathrm{k}|\mathrm{k})-u(\mathrm{t}+\mathrm{k}-1)$

For engine applications where all the computations must be done on an ECM, a fast and reliable QP solver is the key for successful implementation of a MPC in engine control applications. Currently, there are various solvers that can meet these strict requirements (see, e.g., [16]). The QP solver employed in this work is an explicit solver from the Honeywell OnRAMP software suite that has been designed with the goal of meeting the computational speed and ECM footprint requirements.

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Raw Gas Transmission

Saeid Mokhatab , ... John Y. Mak , in Handbook of Natural Gas Transmission and Processing (Fourth Edition), 2019

3.8.4.3.3.3 Control Methods

Control methods (feed forward control, slug choking, and active feedback control) for slug handling are characterized by the use of process and/or pipeline information to adjust available degrees of freedom (pipeline chokes, pressure, and levels) to reduce or eliminate the effect of slugs in the downstream separation and compression unit. Control based strategies are designed based on simulations using rigorous multiphase simulators, process knowledge, and iterative procedures. To design efficient control systems, it is therefore advantageous to have an accurate model of the process (Bjune et al., 2002).

The feed-forwarded control aims to detect the buildup of slugs and, accordingly, prepares the separators to receive them, e.g., via feed-forwarded control to the separator level and pressure control loops. The aim of slug choking is to avoid overloading the process facilities with liquid or gas. This method makes use of a topside pipeline choke by reducing its opening in the presence of a slug, and thereby protecting the downstream equipment (Courbot, 1996). Like slug choking, active feedback control makes use of a topside choke. However, with dynamic feedback control, the approach is to solve the slug problem by stabilizing the multiphase flow. Using feedback control to prevent severe slugging has been proposed by Hedne and Linga (1990), and by other researchers (Molyneux et al., 2000; Havre and Dalsmo, 2001; Bjune et al., 2002). The use of feedback control to stabilize an unstable operating point has several advantages. Most importantly, one is able to operate with even, nonoscillatory flow at a pressure drop that would otherwise give severe slugging. Fig. 3.31 shows a typical application of an active feedback control approach on a production flow line/pipeline system, and illustrates how the system uses pressure and temperature measurements (PT and TT) at the pipeline inlet and outlet to adjust the choke valve. If the pipeline flow measurements (FT) are also available, these can be used to adjust the nominal operating point and tuning parameters of the controller.

Figure 3.31. Typical configuration of a feedback control technique in flow line/riser systems (Bjune et al., 2002).

Note, the response times of large multiphase chokes are usually too long for such a system to be practical. The slug suppression system (S3) developed by Shell has avoided this problem by separating the fluids into a gas and liquid stream, controlling the liquid level in the separator by throttling the liquid stream and controlling the total volumetric flow rate by throttling the gas stream. Hence, the gas control valve back pressures the separator to suppress surges and as it is a gas choke, it is smaller and therefore more responsive than a multiphase choke.

The S3 is a small separator with dynamically controlled valves at the gas and liquid outlets, positioned between the pipeline outlet and the production separator. The outlet valves are regulated by the control system using signals calculated from locally measured parameters, including pressure and liquid level in the S3 vessel and gas and liquid flow rates. The objective is to maintain constant total volumetric outflow. The system is designed to suppress severe slugging and decelerate transient slugs so that associated fluids can be produced at controlled rates. In fact, implementation of the S3 results in a stabilized gas and liquid production approximating the ideal production system. Installing S3 is a cost effective modification and has lower capital costs than other slug catchers on production platforms. The slug suppression technology also has two advantages over other slug-mitigation solutions, where unlike a topside choke, the S3 does not cause production deferment and controls gas production, and the S3 controller uses locally measured variables as input variables and is independent of downstream facilities (Kovalev et al., 2003).

The design of stable pipeline-riser systems is particularly important in deepwater fields, since the propensity towards severe slugging is likely to be greater and the associated surges more pronounced at greater water depths. Therefore, system design and methodology used to control or eliminate severe slugging phenomenon become very crucial when considering the safety of the operation and the limited available space on the platform. Currently, there are three basic elimination methods that have been already proposed. However, the applicability of current elimination methods to deepwater systems is very much in question. Anticipating this problem, different techniques should be developed to be suitable for different types of problems and production systems (Mokhatab et al., 2007a).

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12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering

Alexander Schaum , Thomas Meurer , in Computer Aided Chemical Engineering, 2015

6 Conclusions

An inversion-based feedforward control for the Droop model for microalgae growth in the chemo-stat is presented. The dilution rate is manipulated in such a way that biomass set point changes are followed in finite time with practical stability with respect to feed perturbations. The asymptotic stability of the internal dynamics due to mass-conservation is exploited to show this property. Numerical results show that the steady-state to steady-state transition time can be significantly reduced by a factor of four using the proposed approach in comparison to a simple step change in the feed flow, and show the robustness with respect to unknown feed perturbations.

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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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Process Systems Engineering for Pharmaceutical Manufacturing

Ravendra Singh , in Computer Aided Chemical Engineering, 2018

5.2 Feedforward (FF) Control Strategy

The basic concept of feedforward control is to measure important disturbance variables and take corrective action before they upset the process (see Fig. 4A). It takes proactive control actions and can provide better control. The main challenge is that the disturbance variables must be measured online/inline and it requires a feedforward controller model. The feedforward controller alone does not take into account the measured signal of the control variable; therefore, achieving the desired set point is not guaranteed (Singh et al., 2015a,b). Practically, the feedforward controller is normally coupled with feedback controller so that the proactive control action can be taken as well as the set point tracking of control variable can be guaranteed.

In case of feedforwardforward-only control scheme, the process input variable or disturbances are measured before they can affect the process (see Fig. 4C). The measured signals are then sent to feedforward controller, which adjusts the manipulated variable. As shown in the figure, the feedforward controller takes action ahead of time before the change in any input variable or disturbances can upset the plant. In this case, since there is no feedback of the control variable signal to mitigate offset, the set-points tracking of the response of the control variables is not ensured.

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