scholarly journals Control of inventory system with random demand and product damage during delivery using the linear quadratic gaussian method

2021 ◽  
Vol 22 (3) ◽  
pp. 1748
Author(s):  
Sutrisno Sutrisno ◽  
Widowati Widowati ◽  
R. Heru Tjahjana
Keyword(s):  
Inventory System ◽  
Inventory Level ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Optimal Value ◽  
State Space System ◽  
Product Delivery ◽  
Linear State ◽  
The Right ◽  
Product Inventory

This study formulates a dynamical system for the control of a single product inventory system in accordance with the random value of demand and the percentage of damaged product during the delivery process. The formulated model has the form of a linear state-space system comprising of two disturbances, which represents the random value of demand and the percentage of the damaged product during delivery. The optimal value of the product amount ordered to the supplier is properly calculated by using the linear quadratic gaussian (LQG) method. The controller is used by the manager to make inventory level decisions under the uncertainty of demand and damaged items during the product delivery process. The result showed that the optimal product order for each review time was achieved, and the inventory level was used to obtain the right set point properly. Moreover, based on comparison with other research results, the proposed model was well performed.

scholarly journals Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity under Experimental Greenhouse

2016 ◽  
Vol 6 (5) ◽  
pp. 2262
Author(s):  
M. Outanoute ◽  
A. Lachhab ◽  
A. Ed-dahhak ◽  
M. Guerbaoui ◽  
A. Selmani ◽  
...  
Keyword(s):  
Relative Humidity ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Monitoring And Control ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
State Space System ◽  
Optimal Dynamic ◽  
And Control

<p><span lang="EN-US">This paper describes one practical approach that suggests a model based technique to control in real time the relative humidity under greenhouse. The humidity level is one of the most difficult environmental factors to be regulated in greenhouse. Moreover, maintaining and correcting for more or less humidity can be a challenge for even the most sophisticated monitoring and control equipment. For these raisons, a Linear Quadratic Gaussian (LQG) controller for relative humidity regulation under greenhouse turns out to be useful. Indeed a LQG controller is proposed for a relative humidity under a greenhouse control task. So, the state space model, which is best fitting the acquired data, was identified using the Numerical Subspace State Space System IDentification (N4SID) algorithm. The mathematical model that is obtained will be used for evaluating the parameters of LQG strategy. The proposed controller is implemented in two steps, in one hand, Kalman filter (KF) is used to develop an observer that estimates the state of relative humidity under greenhouse. In the other hand, the state feedback controller gain is estimated using a linear quadratic criterion function. The suggested optimal implemented controller using Matlab/Simulink environment is applied to an experimental greenhouse. We found, according to the results, that the controller is able to lead the inside relative humidity to the desired value with high accuracy, regardless of the external disturbances.</span></p>

Synthesis of an Optimal Dynamic Regulator Based on Linear Quadratic Gaussian (LQG) for the Control of the Relative Humidity under Experimental Greenhouse

2016 ◽  
Vol 6 (5) ◽  
pp. 2262 ◽  
Author(s):  
M. Outanoute ◽  
A. Lachhab ◽  
A. Ed-dahhak ◽  
M. Guerbaoui ◽  
A. Selmani ◽  
...  
Keyword(s):  
Relative Humidity ◽  
State Space ◽  
State Space Model ◽  
The State ◽  
Monitoring And Control ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
State Space System ◽  
Optimal Dynamic ◽  
And Control

<p><span lang="EN-US">This paper describes one practical approach that suggests a model based technique to control in real time the relative humidity under greenhouse. The humidity level is one of the most difficult environmental factors to be regulated in greenhouse. Moreover, maintaining and correcting for more or less humidity can be a challenge for even the most sophisticated monitoring and control equipment. For these raisons, a Linear Quadratic Gaussian (LQG) controller for relative humidity regulation under greenhouse turns out to be useful. Indeed a LQG controller is proposed for a relative humidity under a greenhouse control task. So, the state space model, which is best fitting the acquired data, was identified using the Numerical Subspace State Space System IDentification (N4SID) algorithm. The mathematical model that is obtained will be used for evaluating the parameters of LQG strategy. The proposed controller is implemented in two steps, in one hand, Kalman filter (KF) is used to develop an observer that estimates the state of relative humidity under greenhouse. In the other hand, the state feedback controller gain is estimated using a linear quadratic criterion function. The suggested optimal implemented controller using Matlab/Simulink environment is applied to an experimental greenhouse. We found, according to the results, that the controller is able to lead the inside relative humidity to the desired value with high accuracy, regardless of the external disturbances.</span></p>

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