Feedforward-feedback

As shown in the above works, an optimal feedback/feedforward controller can be derived as an analytical function of the numerator and denominator polynomials of Gp(B) and Gn(B). No iteration or integration is required to generate the feedback law, as a consequence of the one step ahead criterion. Shinnar and Palmor (52) have also clearly demonstrated how dead time compensation (discrete time Smith predictor) arises naturally out of the minimum variance controller. These minimum variance techniques can also be extended to multi-variable systems, as shown by MacGregor (51). 

Although the feedback MFC control approach presents favourable performance, the combined feedback-feedforward MFC control approach succeeds to accomplish superior control performance, shown by its short setting time and reduced overshoot and small offset. Further development of the feedforward-feedback MFC approach for controlling a larger number of the WWTF variables is straightforward. As MFC control may successfully work in the presence of constraints, for both manipulated and controlled variables, the proposed control design outperforms the traditional control approach and reveals incentives for its practical implementation. 

Using the transfer function concept, Koppel (1967) derived the optimal control policy for a heat exchanger system described by hyperbolic partial differential equations using the lumped system approach. Koppel and Shih (1968) also presented a feedback interior control for a class of hyperbolic differential equations with distributed control. In an earlier paper Koppel e/ al. (1968) discussed the necessary conditions for the system with linear hyperbolic partial differential equations having a control which is independent of spatial coordinates. The optimal feedback-feedforward control law for linear hyperbolic systems, whose dynamical response to input variations is characterized by an initial pure time delay, was derived by Denn... 

Like any closed-loop system, the behavior of the respiratory control system is defined by the continual interaction of the controller and the peripheral processes being controlled. The latter include the respiratory mechanical system and the pulmonary gas exchange process. These peripheral processes have been extensively studied, and their quantitative relationships have been described in detail in previous reviews. Less well understood is the behavior of the respiratory controller and the way in which it processes afferent inputs. A confounding factor is that the controller may manifest itself in many different ways, depending on the modeling and experimental approaches being taken. Traditionally, the respiratory control system has been modeled as a closed-loop feedback/feedforward regulator whereby homeostasis of arterial blood gas and pH is maintained. Alternatively, the respiratory controller may be viewed as a... 

D.M. Bruce and N.J.B. McFarlane, Control of mixed-flow grain dryers An improved feedback-feedforward algorithm, J. Agric. Eng. Res., 56 225, 1993. 

ANN has also been applied to flow control in microfluidic networks. Assadsangabi et al. [13] presented a combined feedback/feedforward strategy to control the output flow rate in the T-juncti(Mi of microchannels. A finite element model (FEM) was used to generate the training data, and a combined ANN and fuzzy logic (FL) system was utilized to build an inverse model of the flow in the T-junction, which serves as a controller to adjust the output flow rate. 

Assadsangabi B, Movahed S, Eghtesad M, Bzargan-Lari Y (2011) Combined feedback/feedforward velocity control of electrokinetically driven flow in a network of planar microchannels. The 18th IF AC world congress, Milano, Italy... 

E. Rush, T. O. Drews, D. L. Ma, R. C. Alkire, and R. D. Braatz, J. Process Contr., 16, 409 (2006). Robust Nonlinear Feedback-Feedforward Control of a Coupled Kinetic Monte Carlo-Finite Difference Simulation. 

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