Computer Architecture and Digital Systems
Jibril Bala; Olayemi Olaniyi; Taliha Folorunso; Tayo Arulogun
Volume 6, Issue 4 , November 2020, , Pages 213-226
Abstract
Proportional-Integral-Derivative (PID) controllers and Internal Model Controllers (IMC) are effective tools in control analysis and design. However, parameter tuning, and inaccurate model representation often lead to unsatisfactory closed loop performance. In this study, we analyse the effect of PID ...
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Proportional-Integral-Derivative (PID) controllers and Internal Model Controllers (IMC) are effective tools in control analysis and design. However, parameter tuning, and inaccurate model representation often lead to unsatisfactory closed loop performance. In this study, we analyse the effect of PID controllers and IMCs tuned with Genetic Algorithm (GA) and Fuzzy Logic (FL), on a poultry feeding system. The use of GA and FL for tuning of the PID and IMC parameters was done to enhance the adaptability and optimality of the controller. A comparative analysis was made to analyse closed loop performance and ascertain the most effective controller. The results showed that the GA-PID and FL-PID gave a better performance in the aspect of rise time, settling time and Integrated Absolute Error (IAE). On the other hand, the GA-IMC and FL-IMC gave better performances in the aspect of the performance overshoot. Therefore, for processes in which a faster response and lower IAE are desired, the GA-PID and FL-PID are more effective while for processes in which the major objective is to minimise the overshoot, the GA-IMC and FL-IMC are more suitable.
Software Engineering and Information Systems
Saeid Khajehvand; Seyed Mahdi Abtahi
Volume 5, Issue 2 , May 2019, , Pages 81-92
Abstract
In this paper, chaotic dynamic and nonlinear control in a glucose-insulin system in types I diabetic patients and a healthy person have been investigated. Chaotic analysis methods of the blood glucose system include Lyapunov exponent and power spectral density based on the time series derived from the ...
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In this paper, chaotic dynamic and nonlinear control in a glucose-insulin system in types I diabetic patients and a healthy person have been investigated. Chaotic analysis methods of the blood glucose system include Lyapunov exponent and power spectral density based on the time series derived from the clinical data. Wolf's algorithm is used to calculate the Lyapunov exponent, which positive values of the Lyapunov exponent mean the dynamical system is chaotic. Also, a wide range in frequency spectrum based on the power spectral density is also used to confirm the chaotic behavior. In order to control the chaotic system and reach the desired level of a healthy person's glucose, a novel fuzzy high-order sliding mode control method has been proposed. Thus, in the control algorithm of the high-order sliding mode controller, all of the control gains computed by the fuzzy inference system accurately. Then the novel control algorithm is applied to the Bergman's mathematical model that is verified using the clinical data set. In this system, the control input is the amount of insulin injected into the body and the control output is the amount of blood glucose level at any moment. The simulation results of the closed-loop system in various conditions, along with the performance of the control system in disturbance presence, indicate the proper functioning of this controller at the settling time, overshoot and the control inputs.
Pattern Analysis and Intelligent Systems
OLATUNJI HEZEKIAH ADIGUN; OLUSOLA JOEL OYEDELE
Volume 5, Issue 1 , February 2019, , Pages 11-18
Abstract
This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, ...
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This paper employs Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict water level that leads to flood in coastal areas. ANFIS combines the verbal power of fuzzy logic and numerical power of neural network for its action. Meteorological and astronomical data of Santa Monica, a coastal area in California, U. S. A., were obtained. A portion of the data was used to train the ANFIS network, while other portions were used to check and test the generalization ability of the ANFIS model. Water level predictions were made for 24 hours, 48 hours and 72 hours, in which training, checking and testing of the model were performed for each of the prediction periods. The model results from the training, checking and testing data groups show that 48 hours prediction has the least Root Mean Square Error (RMSE) of 0.05426, 0.06298 and 0.05355 for training, checking and testing data groups respectively, showing that the prediction is most accurate for 48 hours.
Pattern Analysis and Intelligent Systems
Olatunji Hezekiah Adigun
Volume 4, Issue 4 , November 2018, , Pages 247-254
Abstract
Multivariable liquid level control is essential in process industries to ensure quality of the product and safety of the equipment. However, the significant problems of the control system include excessive time consumption and percentage overshoot, which result from ineffective performance of the tuning ...
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Multivariable liquid level control is essential in process industries to ensure quality of the product and safety of the equipment. However, the significant problems of the control system include excessive time consumption and percentage overshoot, which result from ineffective performance of the tuning methods of the PID controllers used for the system. In this paper, fuzzy logic was used to tune the PID parameters to control a four-coupled-tank system in which liquid level in tanks 1 and 2 were controlled. Mass Balance equation was employed to generate the transfer function matrix for the system, while a Fuzzy Inference System (FIS) file is created and embedded in fuzzy logic controller blocks, making tuning rules for the PID. Matlab R2009b simulation of the system model shows that the rise time (RT), settling time (ST), peak value (PV) and percentage overshoot (PO) for the developed DF-PID controller were 1.48 s, 4.75 s, 15 cm and 0% respectively for tank-1; and 0.86 s, 2.62 s, 10 cm and 0% respectively for tank-2, which are the smallest and best values when compared with other PID tuning methods namely: Ziegler-Nichols, Cohen-Coon and Chien-Hrones-Reswick PID tuning methods.
Zahra Barati; Mahdi Jafari Shahbazzadeh; Vahid Khatibi Bardsiri
Volume 2, Issue 4 , November 2016, , Pages 9-16
Abstract
predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product ...
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predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product development. The absence of these activities before starting the project will lead to various problems. Researchers focus their attention on determining techniques with the highest effort prediction accuracy or on suggesting new combinatory techniques for providing better estimates. Despite providing various methods for the estimation of effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. In this article, a new method has been presented in order to increase the accuracy of effort estimation. This model is based on the type-2 fuzzy logic in which the gradient descend algorithm and the neuro-fuzzy-genetic hybrid approach have been used in order to teach the type-2 fuzzy system. In order to evaluate the proposed algorithm, three databases have been used. The results of the proposed model have been compared with neuro-fuzzy and type-1 fuzzy system. This comparison reveals that the results of the proposed model have been more favorable than those of the other two models.
Computer Networks and Distributed Systems
Samaneh Nazari Dastjerdi; Hamid Haj Seyyad Javadi
Volume 1, Issue 2 , May 2015, , Pages 39-44
Abstract
Network sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power supply). Therefore, ...
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Network sensors consist of sensor nodes in which every node covers a limited area. The most common use ofthese networks is in unreachable fields.Sink is a node that collects data from other nodes.One of the main challenges in these networks is the limitation of nodes battery (power supply). Therefore, the use oftopology control is required to decrease power consumption and increase network accessibility.In this paper, a network is modeled by a graph. Each vertex in the graphindicatesa sensor node and the edges display the communication links between them.Changes in the graph show changes in network topology and a different path to the sink.In this research, “fuzzy logic” is used for topology control. As the fuzzy logic utilizes optimized sensing radius comparing with minimum-maximum sensing radius, we expect less dead nodes, more mean residual energy and relatively more load balance in the network. At first, 2-input fuzzy algorithm was chosen. However 3-input fuzzy algorithm was also observed due to reasons explained in the main text. In both algorithms, we haveload balance in network and prolong network lifetime. Unreachable paths are less encountered with higher rates of packet delivery. The final standard deviation (STD) reaches to its minimum level, while the residual energy in sensors remains close to each other.