FPGA Implementation of Single Neuron PID Controller for Depth of Anesthesia Based on PSO

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

doi:10.1109/scee.2018.8684186

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…To start simulation for controlling temperature in greenhouse we must specify the desired temperature needed, in this study 25 o C is regarded as an optimal value for the greenhouse environment and based on this the controller with the smart GTO optimization method will track the and monitoring the response to reach to the desired value with a stable behavior, all simulation results is obtained using MATLAB 2019 and the parameter of the GTO algorithm is shown in Table 1. The system error between desired and actual response is monitored and minimized using ITAE function [26], [27] as shown in (18) in the smart GTO algorithm iterations, Figure 5 The system response is indicated in Figure 6 and for demonstrate the controller efficiency a comparison with two conventional controller (PI and PID) is done and the response of all controllers is shown in Figure 7, and all controllers gain values is shown in Table 2. The response analysis results for the comparison of all controllers are shown in Table 3.…”

Section: Simulation Resultsmentioning

confidence: 99%

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: Simulation Resultsmentioning

confidence: 99%

Dual stage cascade controller for temperature control in greenhouse

Abood

Kadhim²,

Mohammed³

2023

Bulletin EEI

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“…Then to test the performance of the controller in tracking desired output value, a performance index (fitness function) is used to test the error continuously. In this paper, integral time absolute error (ITAE) was adopted as a fitness function and it is shown in (11) [26], used in WOA to tune the controller gains and achieve stable system response, the fitness function for the system is indicated in Figure 4 and the block diagram of the system shown in Figure 5. To show the system performance based on optimal FOPID controller a comparison with classical PID is done (tuned using WOA also), Figure 6 indicates the system response for the optimal controllers PID and FOPID, and the gains of the optimal controllers are listed in Table 3 The difference between the proposed controller and classical PID controller appears in the step response analysis, this is due to the benefits of fractional mathematic effect on system response as shown it has fast settling time with 38% faster than the classical PID controller and the small overshoot (1.24) which achieve a stable and efficient desired response.…”

Section: Simulation Resultsmentioning

confidence: 99%

Design of fractional order PID controller for AVR system using whale optimization algorithm

Abood

Oleiwi

2021

IJEECS

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In this paper a robust fractional order PID (FOPID) controller is proposed to control the automatic voltage regulator (AVR) system, the tuning of the controller gains are done using whale optimization algorithm (WOA) and integral time absolute error (ITAE) cost function is adopted to achieve an efficient performance. The transient analysis was done and compared with conventional PID in terms of overshoot, settling time, rise time, and peak time to explain the superiority of the proposed controller. Finally, a robustness analysis is done by adding external disturbances to the system and changing the system parameters by ±20% from its original value, the controller overcomes the disturbances signals with less than 0.25 s and faces the changes of the system values and returning the response within (0.7-1) sec and led the system to the desired response efficiently. The numerical simulations showed that the smart WOA offers satisfying results and faster response reflected clearly on the robust and stable performance of the proposed controller in improving the transient analysis of AVR system response.

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“…Finally, for achieve an optimal system performance a good choice for the fitness criteria must be utilized [24], different fitness criteria is found like integral of absolute error (ITAE), integral of time square errors (ITSE), integral of square errors (ISE) and integral absolute errors (IAE). In this paper the ITAE in (42) is used as a fitness function [25] for the SSA algorithm to tune the best values for the backstepping controller gains to obtain optimal system response as shown in Figure 3.…”

Section: Squirrel Search Algorithmmentioning

confidence: 99%

Optimal backstepping controller for controlling DC motor speed

2022

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In this paper, squirrel search algorithm (SSA) is adapted to tune the optimal parameters of a backstepping controller for controlling speed in DC motor; Lyapunov stability theorem is applied to derive the control low to achieve system stability analysis. M-file and Simulink platform is used to simulate the response of the system, a comparison with conventional controllers is utilized, all gains are tuned using SSA method and integral time absolute error (ITAE) fitness function to test the efficient performance of the proposed controller also a comparison with other tuned different controllers is done. Transient response analysis is used to validating the proposed controller performance. The simulation results showed a stable response and efficint performance for the proposed controller when compared with the two conventional controllers used by 41.6% for PID controller and by 32% for PI controller in rise time while in settling time is superior by 36.97% for PID controller and by 41.82% for PI controlle these values led for achieving the desired speed in fast and stable response.

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FPGA Implementation of Single Neuron PID Controller for Depth of Anesthesia Based on PSO

Cited by 13 publications

References 11 publications

Dual stage cascade controller for temperature control in greenhouse

Dual stage cascade controller for temperature control in greenhouse

Design of fractional order PID controller for AVR system using whale optimization algorithm

Optimal backstepping controller for controlling DC motor speed

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