Implementation of Self Tune Single Neuron PID Controller for Depth of Anesthesia by FPGA

Hattim, Layla; Karam, Ekhlas H.; Issa, Abbas H.

doi:10.1007/978-3-030-01653-1_10

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…PID controllers are classified as an easy and traditional controllers adopted for improving the performance of the system. Nowadays, many studies suggested different modifications and structures for more enhancement in system response either by combining PID controller with some intelligent methods such as neural network [17] or using fuzzy logic with it [18] or modify its structures based on system behavior as in [19], [20] or adopt the fractional analysis either the integral or the differential parts of PID controller or together by fraction number for improving system response, this form of enchantment is a special case controller and become an improved structure of conventional PID controller [21]. In this paper, a novel cascade structure named proportional-integral-one plus integral-derivative PI-(1+ID) [22] is suggested to control temperature based o GTO algorithm and its block diagram is shown in Figure 3.…”

Section: The Proposed Controllermentioning

confidence: 99%

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Dual stage cascade controller for temperature control in greenhouse

Abood

Kadhim²,

Mohammed³

2023

Bulletin EEI

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In this paper, a dual stage cascade controller PI-(1+PD) is adopted to maintain and control temperature in greenhouse environment based on a smart and intelligent gorilla troops optimization (GTO) method for evaluating the controller gains to enhance the system response by reducing the error value and minimize the integral time absolute error (ITAE) fitness functions during simulation. The simulation results are obtained by using MATLAB 2019, then compared with two conventional controllers proportional integral derivative (PI and PID) based on evaluation parameters for all controllers in term of peak time, rise time, settling time and overshoot to show its efficient response if compared with other controllers used.

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Section: The Proposed Controllermentioning

confidence: 99%

“…This choice is selected when the young gorilla males reach the age puberty, competition are violent because they fighting with other males for create their groups by choosing adult females. This choice is selected if 𝑎 < 𝑤 and it can be translated as shown in ( 14)- (17):…”

Section: Competition For Adult Femalesmentioning

confidence: 99%

Dual stage cascade controller for temperature control in greenhouse

Abood

Kadhim²,

Mohammed³

2023

Bulletin EEI

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“…PID controllers is regarded as a simple and classical controllers that adopted for improving system behavior. Nowadays, studies are made with a different changes for PID controller like combining it with a neural network [19,20] or changing its structure to reach to a best stability and robustness as in [21,22] or changing it by add a fractional variables (integral & derivative) to the classical PID to improve system output [23,24], this type of change is an augmented type PID controller. These parameters (μ for derivative variable and λ for the integral variable) make the gains of controller be five variables.…”

Section: Fopid Controllermentioning

confidence: 99%

Design an Optimal Fractional Order PID Controller for Speed Control of Electric Vehicle

Kadhim,

Abood,

Mohammed

2023

JESA

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In recent years, electric vehicles have garnered significant attention due to their environmental and economic advantages compared to conventional vehicles, including reduced emissions and lower fuel costs. This study proposes an optimal fractional-order PID (FOPID) controller to regulate electric vehicle (EV) speed. The FOPID controller is advantageous due to its ability for stabilizing the system, managing parameter variations, and mitigating potential disturbances. The tuning of this controller's gains is achieved through an intelligent Ant Colony Optimization (ACO) algorithm. The selection of the gain values is strategically based on minimizing error, thereby ensuring a robust system response without overshoot or undershoots. The performance of the proposed controller is analyzed and compared to a classical PID controller for demonstrating its superior performance. Simulation results illustrate the efficiency of the proposed controller, which exhibits no fluctuation or oscillation in its response (zero overshoot) and fast settling and rise times of 0.0476 and 0.0297, respectively. By using the optimal gains determined by the smart ACO, the proposed controller achieves a satisfactory and robust system response in controlling EV speed.

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“…A conventional proportional-integral-derivative (PID) controllers are commonly adopted in different engineering applications due to its simplicity and can give efficient results by reducing the system error and obtain stable response values [12,13], the first term named proportional will calculate the controller output depending on the proportional error value, with a high coefficient value, in this case the PID controller will behave more fastly and give a faster target value. The integral error will depend on the integral of the error value then calculated for the error found and for its duration value, in this case the integral appraises time, it can find the current error and the past error values in which the proportional part alone cannot find it.…”

Section: Conventional Pid Controller With Filtermentioning

confidence: 99%

Performance Evolution of Different Optimal Controllers for Controlling AVR System

Mohamed,

Abood

2023

JESA

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The main issue in electrical system is providing a stable voltage values in order to obtain best devices performance for this reason controlling the automatic voltage regulation (AVR) system becoma more helpful to achieve this requirments, in this paper three controllers are suggested for maintaining the terminal voltage level value of the generator part in AVR system that supplied to custumers, these controllers are (Conventional PID, ArcTan PID and Nonlinear PID), all gains of these controllers is tuned by using an intelligent sun flower optimization (SFO) algorithm. The objective of the design is finding suitable values of these gains that give a stable response based on minimizing error value and testing it using the Integral Time Absolute Error (ITAE) fitness function, the numerical results exhibited that the ArcTan PID controller give the best results values with a lower settling time (0.698) and its faster than conventional PID 5.034 % and faster than nonlinear PID by 5.163% for 5 second simulation time also its reach its peak value in 0.513 sec. and an expectable overshoot value equal to 0.513 with a small error value (0.000645) at its steady state case and then small error value when working at normal state without any disturbance or any uncertainty cases applied but when a disturbances signal is applied with a value equal to ± 0.3 to the system, the NLPID and the conventional PID presents a best response by returning the system to its desired value in just 2 second in the two cases applied and when change the gain values of two parts of AVR system( amplifier & sensor) the NLPID behaves as a the more suitable one and give a superior robust performance in facing this unwanted signals and then a desired response is achieved after small period time with satisfied and acceptable values for woring in this enviroment.

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Implementation of Self Tune Single Neuron PID Controller for Depth of Anesthesia by FPGA

Cited by 7 publications

References 13 publications

Dual stage cascade controller for temperature control in greenhouse

Dual stage cascade controller for temperature control in greenhouse

Design an Optimal Fractional Order PID Controller for Speed Control of Electric Vehicle

Performance Evolution of Different Optimal Controllers for Controlling AVR System

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