scholarly journals Design of Flow Control System with A Kickback Flow as A Manipulated Variable

Eksergi ◽  
2017 ◽  
Vol 14 (2) ◽  
pp. 23
Author(s):  
Yulius Deddy Hermawan
Keyword(s):  
Steady State ◽  
Flow Control ◽  
Liquid Flow ◽  
Stability Criterion ◽  
Closed Loop ◽  
Open Loop ◽  
Loop Model ◽  
Point Change ◽  
Controller Gain ◽  
Control Configuration

The open loop experiment of water flow dynamic in pipe has been done in laboratory. Pump was used to flow water in pipe. Part of liquid from discard of pump was recycled back to the suction of pump (kickback) and adjusted to control the liquid flow to the next process. The open loop laboratory experiment produced the steady state parameters; they were discard flowrate =16.6 [L/min], kickback flowrate =5.8 [L/min], and liquid flowrate to the next process =10.8 [L/min]. These steady state parameters were then used as the initial value for closed loop simulation with computer programming. This study has proposed the liquid flow control configuration by manipulating the kickback flow. Proportional Integral (PI) was proposed to control the flow and Routh-Hurwitz (RH) stability criterion was chosen to predict the range of the controller gain (Kc) that gives stable response. The closed loop model was solved analytically with Laplace method for both servo and regulatory problems. The set point change of flow and disturbance were made based on step function. The scilab software was used to do closed loop simulation. Based on RH stability criterion, the controller gain should be negative in order to give stable response. The closed loop simulation showed that by using controller gain Kc=–0.5 and integral time constant tI=0.3 [min], stable and fast response with Integral Absolute Error (IAE) near to zero (0,0022) could be achieved.

scholarly journals PI- Controlled PV Fed Fly Back Converter with Enhanced Dynamic Response

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4031-4034
Keyword(s):  
Steady State ◽  
Dynamic Response ◽  
Rise Time ◽  
Closed Loop ◽  
Input Voltage ◽  
Pi Controller ◽  
Open Loop ◽  
Load Variations ◽  
Simulink Model ◽  
Step Down

Fly back converter is the most popular converter because of its simplicity, low part counts and isolation. It occupies less volume and it saves cost. Fly back converter steps up and step down the voltage with the same polarity. Open loop operation remains insensitive to the input voltage and load variations. Matlab Simulink model for Fly back converter is established using PI controller. Open loop Fly back converter system and closed loop fly back converter systems are simulated and their outcomes are compared. Comparison is done in terms of Rise time ,Settling time and steady state error

Model Predictive Control and Controller Parameter Optimisation of Combustion Instabilities

2019 ◽  
Vol 36 (2) ◽  
pp. 185-194 ◽  
Author(s):  
I. Yazar ◽  
F. Caliskan ◽  
R. Vepa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Combustion Process ◽  
Open Loop ◽  
Loop Model ◽  
Parameter Optimisation ◽  
Direct Adaptive Control ◽  
The Cost ◽  
Adaptive Control Algorithm

Abstract In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.

Location model for a remanufacturing reverse logistics network based on adaptive genetic algorithm

SIMULATION ◽  
2019 ◽  
Vol 95 (11) ◽  
pp. 1069-1084 ◽  
Author(s):  
Rui Yan ◽  
Bo Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Model ◽  
Reverse Logistics ◽  
Closed Loop ◽  
Value Added ◽  
Open Loop ◽  
Demand Elasticity ◽  
Mixed Integer ◽  
Adaptive Genetic Algorithm ◽  
Loop Model

Energy saving and environmental protection are important issues of today. Concerning the environmental and social need to increase the utilization of used products, this paper introduces two remanufacturing reverse logistics (RL) network models, namely, the open-loop model and the closed-loop model. In an open-loop RL system, used products are recovered by outside firms, while in a closed-loop RL system, they are returned to their original producers. The open-loop model features a location selection with two layers. For this model, a mixed-integer linear program (MILP) is built to minimize the total costs of the open-loop RL system, including the fixed cost, the freight between nodes, the operation cost of storage and remanufacturing centers, the penalty cost of unmet or remaining demand quantity, and the government-provided subsidy given to the enterprises that protect the environment. This MILP is solved using an adaptive genetic algorithm with MATLAB simulation. For a closed-loop RL network model, a special demand function considering the relationship between new and remanufactured products is developed. Remanufacturing rate, environmental awareness, service demand elasticity, value-added services, and their impacts on total profit of the closed-loop supply chain are analyzed. The closed-loop RL network model is proved effective through the analysis of a numerical example.

Pseudo-random binary sequence closed-loop system identification error with integration control

2009 ◽  
Vol 223 (6) ◽  
pp. 877-884 ◽  
Author(s):  
Z Ren ◽  
G G Zhu
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Binary Sequence ◽  
Open Loop ◽  
Loop System ◽  
Loop Model ◽  
Closed Loop System ◽  
Identification Error ◽  
Closed Loop Identification ◽  
Integral Controller

This paper studies the closed-loop system identification (ID) error when a dynamic integral controller is used. Pseudo-random binary sequence (PRBS) q-Markov covariance equivalent realization (Cover) is used to identify the closed-loop model, and the open-loop model is obtained based upon the identified closed-loop model. Accurate open-loop models were obtained using PRBS q-Markov Cover system ID directly. For closed-loop system ID, accurate open-loop identified models were obtained with a proportional controller, but when a dynamic controller was used, low-frequency system ID error was found. This study suggests that extra caution is required when a dynamic integral controller is used for closed-loop system identification. The closed-loop identification framework also has significant effects on closed-loop identification error. Both first- and second-order examples are provided in this paper.

When Causality Does Not Imply Correlation: More Spadework at the Foundations of Scientific Psychology

2011 ◽  
Vol 108 (3) ◽  
pp. 943-954 ◽  
Author(s):  
Richard S. Marken ◽  
Brittany Horth
Keyword(s):  
Sensory Input ◽  
Closed Loop ◽  
Causal Model ◽  
Open Loop ◽  
Tracking Performance ◽  
Loop Model ◽  
Surprising Result ◽  
Scientific Psychology ◽  
Mouse Movements

Experimental research in psychology is based on an open-loop causal model which assumes that sensory input causes behavioral output. This model was tested in a tracking experiment where participants were asked to control a cursor, keeping it aligned with a target by moving a mouse to compensate for disturbances of differing difficulty. Since cursor movements (inputs) are the only observable cause of mouse movements (outputs), the open-loop model predicts that there will be a correlation between input and output that increases as tracking performance improves. In fact, the correlation between sensory input and motor output is very low regardless of the quality of tracking performance; causality, in terms of the effect of input on output, does not seem to imply correlation in this situation. This surprising result can be explained by a closed-loop model which assumes that input is causing output while output is causing input.

scholarly journals Open-loop organization of thalamic reticular nucleus and dorsal thalamus: a computational model

2015 ◽  
Vol 114 (4) ◽  
pp. 2353-2367 ◽  
Author(s):  
Adam M. Willis ◽  
Bernard J. Slater ◽  
Ekaterina D. Gribkova ◽  
Daniel A. Llano
Keyword(s):  
Closed Loop ◽  
Open Loop ◽  
Tunable Filter ◽  
Cortical Activation ◽  
Thalamic Reticular Nucleus ◽  
Reticular Nucleus ◽  
Loop Model ◽  
Dependent Manner ◽  
Dorsal Thalamus ◽  
Low Threshold

The thalamic reticular nucleus (TRN) is a shell of GABAergic neurons that surrounds the dorsal thalamus. Previous work has shown that TRN neurons send GABAergic projections to thalamocortical (TC) cells to form reciprocal, closed-loop circuits. This has led to the hypothesis that the TRN is responsible for oscillatory phenomena, such as sleep spindles and absence seizures. However, there is emerging evidence that open-loop circuits are also found between TRN and TC cells. The implications of open-loop configurations are not yet known, particularly when they include time-dependent nonlinearities in TC cells such as low-threshold bursting. We hypothesized that low-threshold bursting in an open-loop circuit could be a mechanism by which the TRN could paradoxically enhance TC activation, and that enhancement would depend on the relative timing of TRN vs. TC cell stimulation. To test this, we modeled small circuits containing TC neurons, TRN neurons, and layer 4 thalamorecipient cells in both open- and closed-loop configurations. We found that open-loop TRN stimulation, rather than universally depressing TC activation, increased cortical output across a broad parameter space, modified the filter properties of TC neurons, and altered the mutual information between input and output in a frequency-dependent and T-type calcium channel-dependent manner. Therefore, an open-loop model of TRN-TC interactions, rather than suppressing transmission through the thalamus, creates a tunable filter whose properties may be modified by outside influences onto the TRN. These simulations make experimentally testable predictions about the potential role for the TRN for flexible enhancement of cortical activation.

Discrete Time Open-Loop and Closed-Loop Flow Control Based on Van der Pol Modeling

2016 ◽  
Author(s):  
Valentina Motta ◽  
Philippe Mouyon ◽  
Carsten Döll
Keyword(s):  
Flow Control ◽  
Discrete Time ◽  
Closed Loop ◽  
Open Loop ◽  
Van Der Pol

Frequency Domain Stability Criteria for Distributed Parameter Systems

1970 ◽  
Vol 92 (2) ◽  
pp. 377-384 ◽  
Author(s):  
H. C. Khatri
Keyword(s):  
Frequency Domain ◽  
Stability Criterion ◽  
Closed Loop ◽  
Linear Time ◽  
Distributed Parameter Systems ◽  
Open Loop ◽  
Distributed Parameter ◽  
Stability Criteria ◽  
Domain Stability ◽  
Loop Stability

For distributed parameter systems, open-loop stability in the sense of bounded outputs for bounded inputs, and closed-loop asymptotic stability are considered. Frequency domain stability criteria for open and closed-loop distributed parameter systems are given. The closed-loop stability criterion is similar to V. M. Popov’s stability criterion for lumped systems. The criteria are limited to those linear, time-invariant systems whose dynamics can be described by a transfer function which is the ratio of the multiple transform of the output to the multiple transform of the input. The input may or may not be distributed. An example is given to illustrate the applications of the stability criteria.

scholarly journals Open-loop and closed-loop flow control based on Van der Pol modeling

2017 ◽  
Vol 229 (1) ◽  
pp. 389-401
Author(s):  
Valentina Motta ◽  
Leonie Malzacher
Keyword(s):  
Flow Control ◽  
Closed Loop ◽  
Open Loop ◽  
Van Der Pol
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