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System Architecture
Published in Dobrivoje Popovic, Vijay P. Bhatkar, Distributed Computer Control for Industrial Automation, 2017
Dobrivoje Popovic, Vijay P. Bhatkar
As already pointed out in 2.1, the substantial characteristic of decentralized, hierarchically organized computer-systems is their decomposability into a series of individual functional levels. The possible levels are, as shown in Fig. 2.1-11, control, supervisory, production scheduling, and management level. At each level, some automation functions are implemented to operate on the next “lower” level. The execution of functions is, however, initiated and controlled by the next “higher” level. For instance, the “lowest” automation level, i.e., the direct control process level, interacts - via the corresponding process interface - with the plant instrumentation (e.g. with the installed sensors and actuators) which, for some control algorithms implemented as specific automation functions of the level, are applied correspondingly. The optimal set-point values for the algorithms will be supplied by the next “higher” level, i.e., by the supervisory control level, in which the optimization strategy algorithms are implemented.
Further Extensions of Flexibility Analyses
Published in Chuei-Tin Chang, Vincentius Surya Kurnia Adi, Deterministic Flexibility Analysis, 2017
Chuei-Tin Chang, Vincentius Surya Kurnia Adi
In typical control applications, it is not possible to achieve all goals simultaneously because they involve inherent conflicts and tradeoffs. First of all, the selected PID controller must balance two important objectives: performance and robustness. A feedback control system exhibits a high degree of performance if it provides rapid and smooth responses to disturbances and set-point changes with little, if any, oscillation. On the other hand, a control system should also be robust, that is, the controller provides satisfactory performance for a wide range of process conditions and for a reasonable degree of model inaccuracy. Robustness can usually be achieved by choosing conservative controller settings, but this choice tends to result in poor performance.
Characterization and Measurement of Microcomponents with the Atomic Force Microscope (AFM)
Published in Wolfgang Osten, Optical Inspection of Microsystems, 2019
F. Michael Serry, Joanna Schmit
During raster scanning, as the tip encounters changes in the sample topography, the cantilever’s position and movement in the Z direction is altered, triggering a corresponding variation in the input signal. In order for the input signal to be useful for topography mapping and measurement, the AFM operator, using the AFM software, establishes a reference tip–sample force value via a software parameter commonly known as the setpoint. The input signal is monitored and compared to the setpoint value. The difference between these two values is referred to as the error signal, which is used in the feedback loop explained next.
Energy performance of air-conditioned buildings based on short-term weather forecast
Published in Science and Technology for the Built Environment, 2022
Marko G. Ignjatović, Bratislav D. Blagojević, Mirko M. Stojiljković, Aleksandar S. Anđelković, Milena B. Blagojević, Dejan M. Mitrović
All physical parameters a user or operator can interfere with represent independent variables for optimization. These parameters can be either local (thermostatic valve position, indoor temperature thermostat) or central (supply water temperature setpoints – heating curve, systems’/components’ availability within a certain timespan, energy carriers flow by positioning control valves or changing speed for pumps and fans, etc.). The planning horizon, over which the objective function is calculated, is of arbitrary duration, spanning from several hours to three days (the range of official short-term weather forecast in Serbia is 72 hours, updated twice a day). The execution (control) horizon, over which the optimal results are implemented in the real building/model, is shorter and has the duration of typically up to 24 hours. Each day is split into several parts (Figure 4) with all variables remaining constant (so-called timestep), with at least one part of the day representing occupied hours (during which thermal comfort should be within the predefined limits) and the second part representing unoccupied hours (during which thermal comfort is free floating). This environment allows defining timesteps of arbitrary length with the minimum timestep of one hour, although EnergyPlus allows sub-hourly calculations of up to 1 minute in resolution. After retrieving short-term weather forecast or measured data on the building site, a proper format weather file is created, necessary for simulations to run. A building energy model described in an EnergyPlus related textual file (*. IDF) with a constant number of lines is also needed.
A new optimisation method of PIDC controller under constraints on robustness and sensitivity to measurement noise using amplitude optimum principle
Published in International Journal of Control, 2021
Petar D. Mandić, Marko Č. Bošković, Tomislav B. Šekara, Mihailo P. Lazarević
Additional performance indices computed to evaluate the quality of control system are set-point response characteristics: overshoot, settling time and rise time. Percentage overshoot is defined as where is the maximum (peak) value, while is the steady state value of step response. The settling time is the time for which the step response reaches and stays within of . is used in this paper. Rise time denoted with is defined as the time required for the response to rise from 10% to 90% of .
A numerical and experimental study of a simple model-based predictive control strategy in a perimeter zone with phase change material
Published in Science and Technology for the Built Environment, 2018
Anastasios C. Papachristou, Charalampos A. Vallianos, Vasken Dermardiros, Andreas K. Athienitis, JosÉ A. Candanedo
During the heating season in Canada, it is a common practice to lower the temperature set-point at night to save energy (Manning et al. 2007; Moon and Han 2011). This set-point change can either be performed manually or by using a programmable thermostat. Although programmable thermostats are intended to save energy, their inefficient use when manually operated can result in increased energy consumption, higher electricity peak demand, and other issues. Moreover, occupants often complain about the complexity of the modern thermostats and the discomfort due to their inappropriate use (Peffer et al. 2011).