Trial-and-error tuning

The systematic tuning approaches of [3, 4] are implemented not only for EKF, but also for CEKF formulation and compared with the traditional trial-and-error tuning. 

The advantage of feedback control is that corrective action is taken regardless of the source of the disturbance. Its chief drawback is that no corrective action is taken until after the controlled variable deviates from the set point. Feedback control may also result in undesirable oscillations in the controlled variable if the controller is not tuned properly that is, if the adjustable controller parameters are not set at appropriate values. Although trial-and-error tuning can achieve satisfactory performance in some cases, the tuning of the controller can be aided by using a mathematical model of the dynamic process. 

This trial and error tuning procedure has a number of disadvantages. It is obvious that it may be quite burdensome if a large number of trial settings are required and the process dynamics are quite slow. Furthermore, continuous cycling of a process may be objectionable since the closed-loop system is at a stability limit. Thus external disturbances or a change in the process could result in unstable operation. 

All controller gains include measurement/transmitter and valve gains. The values were obtained by sequential trial-and-error tuning to obtain minimum-overshoot responses for step changes in set point. 

The starting point for trial-and-error tuning is always with the controller gain, integral action, and derivative action all at a minimum. 

Trial and error method controller tuning 101 Tube 623... 

One additional important reason why nonbonded parameters from quantum chemistry cannot be used directly, even if they could be calculated accurately, is that they have to implicitly account for everything that has been neglected three-body terms, polarization, etc. (One should add that this applies to experimental parameters as well A set of parameters describing a water dimer in vacuum will, in general, not give the correct properties of bulk liquid water.) Hence, in practice, it is much more useful to tune these parameters to reproduce thermodynamic or dynamical properties of bulk systems (fluids, polymers, etc.) [51-53], Recently, it has been shown, how the cumbersome trial-and-error procedure can be automated [54-56A],... 

Learn how to design and produce new substances, materials, and molecular devices with properties that can be predicted, tailored, and tuned before production. This ability would greatly streamline the search for new useful substances, avoiding considerable trial and error. Recent and projected advances in chemical theory and computation should make this possible. 

Once multivalent carbohydrate-protein interactions are firmly established with the assistance of neoglycoconjugates such as those described above, further focus toward fine-tuned geometry and valency requirements becomes necessary for a thorough understanding of the binding interactions involved. Until now, these investigations have been more or less dependent on trial and error which... 

The model-based observer requires tuning of 6 parameters, i.e., the nonzero values in matrix L and y . As for the model-free observer defined by (5.29), (5.30), and (5.34), the dynamics of the reaction is not required, and only two gains (the main diagonal of matrix Le) and two update gains (y0 and yq) are needed. Finally, the observer (5.36) requires tuning of the two gains k and lq. All the above gains have been tuned via a trial-and-error procedure and are summarized in Table 5.2. 

The following values have been adopted for the PID gains gp = 10, g = 5 10-1, and go = 10-3. In order to perform a fair comparison between the PID approach and the two-loop controller-observer strategies, the PID gains have been tuned via a trial-and-error procedure so as to achieve the same control effort of the other considered control strategies (i.e., so as to obtain the same time histories of u as far as possible). 

Initially use proportional-only controllers in all loops except flow7 controllers, where the normal tight tuning can be used K = 0.5 and T = 0.3 minutes). Set the gains in all level controllers (except reactors) equal to 2. Adjust the temperature, pressure, and composition controller gains by trial and error to see if you can line out the system with the proposed control structure. If P-only control cannot be made to work, PI will not w7ork either. When stable operation is achieved, add a little reset action to each PI controller (one at a time) to pull the process into the setpoint values. 

In the second case the setpoints for the average concentrations of A at extract and that of B at raffinate are changed simultaneously after 20 switching times as shown in Figure 3. For the control purpose, the prediction and control horizons are set equal to S and 2 switching periods, respectively. The weighting matrices are tuned by the trial and error method. Here it is noticed that the control inputs act predictively to bring the control output to their new respective setpoints. It is clearly seen that the control performance is quite satisfactorily. 

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