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Analysing Tactical Cognitive Systems: Theories, Models and Methods
Published in Peter Berggren, Staffan NäHlinder, Erland Svensson, Assessing Command and Control Effectiveness, 2017
The main advantage of feed-forward control is the possibility of counteracting the effects of disturbances before they are visible as undesired deviations from the reference. Control theory has proven that although feed-forward control can be considered the perfect mode of control, it is often only achievable for a limited time due to model errors caused by, among other things, the time constants of a process. However, if the system output can be used to determine the system state, there is only a limited need for detailed knowledge of system dynamics, and feedback control can be maintained. The necessary adjustments and updates of the controller’s internal system model can be made by constantly measuring the deviation of the system output from the reference value. The joint cognitive system is a learning system, thus unstable without feedback. Feedback is needed to correct deviations and compensate for the incompleteness and inadequacy of the internal system model. Reason (1997) emphasizes the importance of this balance between feedback (reactive or compensatory) control and feed-forward (proactive or anticipatory) control. The concept of the feedback–feed-forward control balance is crucial if the cognitive system is to achieve optimal performance in a tactical mission.
Demand Calibration in Multitask Environments: Interactions of Micro and Macrocognition
Published in Emily S. Patterson, Janet E. Miller, Macrocognition Metrics and Scenarios, 2018
Feedforward control uses the anticipated future state of the system to guide behavior. Feedforward control is critical for safe driving; it enables experienced drivers to detect hazards more reliably than inexperienced drivers (McKenna et al., 2006). Feedforward control can compensate for the limits of feedback control, but it suffers from other problems. Feedforward control requires an accurate internal model to anticipate the future state of the system and is vulnerable to unanticipated disturbances. The uncertainty associated with poor mental models of the roadway and the IVIS coupled with inherent variability limits the effectiveness of feedforward control to modulate attention between the road and the IVIS.
Control for Advanced Semiconductor Device Manufacturing: A Case History
Published in William S. Levine, Control System Applications, 2018
T. Kailath, C. Schaper, Y. Cho, P. Gyugyi, S. Norman, P. Park, S. Boyd, G. Franklin, K. Saraswat, M. Moslehi, C. Davis
Using these models, a variety of control strategies was evaluated. The fundamental strategy was to use feedforward in combination with feedback control. Feedforward control was used to get close to the desired trajectory and feedback control was used to compensate for inevitable tracking errors. A feedback controller based on the Internal Model Control (IMC) design procedures was developed using the low-order physics-based model. An LQG feedback controller was developed using the black-box model. Gain scheduling was used to compensate for the nonlinearities. Optimization procedures were used to design the feedforward controller. Controller design is described in Section 5.5.
Tuning rules for feedforward control from measurable disturbances combined with PID control: a review
Published in International Journal of Control, 2021
Two feedforward structures have been treated, the classical one and the non-interacting one, where the non-interacting structure enables a separation between the feedback and the feedforward actions. Seven tuning rules with different approaches and design goals have been presented and compared. They all have their advantages and disadvantages and are useful for different purposes. It is shown that they can provide a significant improvement of the load disturbance rejection in terms of decreased IAE and ISE values. As for the pure feedback control case, there is a trade-off between performance and control signal effort, and the different tuning rules provide different solutions to this balance. A great advantage with feedforward control is that stability and robustness issues are not influenced by feedforward, and are therefore not part of the trade-off.
Predictive control strategies for optimizing temperature stability in instantaneous hot water systems
Published in Science and Technology for the Built Environment, 2021
Ismael A. S. Ehtiwesh, André F. Quintã, Jorge A. F. Ferreira
Integrated feed-forward with feedback control (FFPID) can significantly improve the system performance over simple feedback control whenever there is a major disturbance that can be assessed before it impacts the process. Feed-forward control can reduce the effect of disturbance measured at the output of the process and is often used in conjunction with feedback control to track changes at the set-point and suppress unmeasured disturbances that occur in the actual process. It was implemented to evaluate and validate the predictive controllers since FFPID techniques are normally used by TGWHs manufacturers. This controller has an improved performance in temperature control without imposing a significant cost of implementation. The FFPID control model was developed to define the thermal power needed to heat the water, as schematically represented in Figure 6. The feedforward component is based on the heat exchanger energy balance equation that calculates the predicted thermal power needed to heat the inlet water Tin, to the required set-point temperature Tset, in steady-state conditions, for the measured water flow rate , where cp,w is the water’ specific heat capacity at constant pressure, that is given as follows:
Compensating Voltage Sag in Distribution Systems Using Single Phase Quasi-Z-Source AC/AC Converter
Published in Electric Power Components and Systems, 2019
Farshad Khosravi, Amin Mirzaei, Mahdi Rezvanyvardom
Feed-forward control method proposed in [33] is utilized to produce the gate signals for the QZs AC–AC converter power devices employed in the proposed voltage sag compensator. The control scheme has been designed to ensure that a regulated output voltage is generated despite the occurrence of voltage sag in the input voltage. The proposed feed-forward controller is based on low pass filtering. To elaborate more about filtering technique, at first, the filter is designed with a property to cutoff those frequencies, which are lower than the first resonance-mode of system, and then band stop filters are designed with center frequencies at resonance-mode of the system. Using low-pass filter, the input-energy at all frequencies higher than cutoff frequency can be decreased. In feed-forward control, there is a coupling between input signal and control variable; in the other words, there is a coupling from the disturbance and/or from the set-point straightly to the control variable. An ideal kind of feed-forward in the set-point for all types of signals, such as ramp and sinusoid, provides zero control error in the disturbance. Feed-forward controller has quick response speed depending on the model of disturbance to an amount extent [32]. Under ideal conditions, feed-forward control can entirely omit the influence of the measured disturbance on the process output. Even in some cases with modeling error, this control can decrease the measured disturbance effect on the output. Feed-forward control has some economic benefits due to lower operation cost or/and raised salability of the product because of its more consistent quality.