“…8 b. It is observed that the response of the ventilator in terms of rise time and settling time with the HCO-PID controller is better than the performance of existing results 27 , 76 .…”
Section: Hco For Engineering Problemsmentioning
confidence: 83%
“…For the system in (17), a PID feedback controller is optimized with the HCO algorithm. The convergence performance of the HCO algorithm to design an optimal PID controller compared with some existing algorithms 27,76 is presented in Fig. 8a.…”
Section: Case Study 2: Hco For Pid Controller Design For Human Respir...mentioning
Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.
“…8 b. It is observed that the response of the ventilator in terms of rise time and settling time with the HCO-PID controller is better than the performance of existing results 27 , 76 .…”
Section: Hco For Engineering Problemsmentioning
confidence: 83%
“…For the system in (17), a PID feedback controller is optimized with the HCO algorithm. The convergence performance of the HCO algorithm to design an optimal PID controller compared with some existing algorithms 27,76 is presented in Fig. 8a.…”
Section: Case Study 2: Hco For Pid Controller Design For Human Respir...mentioning
Computational techniques are widely used to solve complex optimization problems in different fields such as engineering, finance, biology, and so on. In this paper, the Human Conception Optimizer (HCO) is proposed as a novel metaheuristic algorithm to solve any optimization problems. The idea of this algorithm is based on some biological principles of the human conception process, such as the selective nature of cervical gel in the female reproductive system to allow only healthy sperm cells into the cervix, the guidance nature of mucus gel to help sperm track a genital tracking path towards the egg in the Fallopian tube, the asymmetric nature of flagellar movement which allows sperm cells to move in the reproductive system, the sperm hyperactivation process to make them able to fertilize an egg. Thus, the strategies pursued by the sperm in searching for the egg in the Fallopian tube are modeled mathematically. The best sperm which will meet the position of the egg will be the solution of the algorithm. The performance of the proposed HCO algorithm is examined with a set of basic benchmark test functions called IEEE CEC-2005 and IEEE CEC-2020. A comparative study is also performed between the HCO algorithm and other available algorithms. The significance of the results is verified with statistical test methods. To validate the proposed HCO algorithm, two real-world engineering optimization problems are examined. For this purpose, a complex 14 over-current relay based IEEE 8 bus distribution system is considered. With the proposed algorithm, an improvement of 50% to 60% in total relay operating times is observed comparing with some existing results for the same system. Another engineering problem of designing an optimal proportional integral derivative (PID) controller for a blower driven patient hose mechanical ventilator (MV) is examined. A significant improvement in terms of response time, settling time is observed in the MV system by comparing with existing results.
“…Control signal generated by the proposed controller is also improved in magnitude by 80% to 90% compared to the existing method. Therefore, a modest motor is sufficient to get the ventilator to work as required with the proposed controller 33 . Figure 8 Controller performance.…”
Section: Simulation and Results Analysismentioning
In order to improve the pressure tracking response of an artificial ventilator system, a novel proportional integral derivative (PID) controller is designed in the present work by utilizing an optimal rule-based fuzzy inference system (FIS) with a reshaped class-topper optimization algorithm (RCTO), which is named as (Fuzzy-PID). Firstly, a patient-hose blower-driven artificial ventilator model is considered, and the transfer function model is established. The ventilator is assumed to operate in pressure control mode. Then, a fuzzy-PID control structure is formulated such that the error and change in error between the desired airway pressure and actual airway pressure of the ventilator are set as inputs to the FIS. The gains of the PID controller (proportional gain, derivative gain, and integral gain) are set as outputs of the FIS. A reshaped class topper optimization algorithm (RCTO) is developed to optimize rules of the FIS to establish optimal coordination among the input and output variables of the FIS. Finally, the optimized Fuzzy-PID controller is examined for the ventilator under different scenarios such as parametric uncertainties, external disturbances, sensor noise, and a time-varying breathing pattern. In addition, the stability analysis of the system is carried out using the Nyquist stability method, and the sensitivity of the optimal Fuzzy-PID is examined for different blower parameters. The simulation results showed satisfactory results in terms of peak time, overshoot, and settling time for all cases, which were also compared with existing results. It is observed in the simulation results that the overshoot in the pressure profile is improved by 16% with the proposed optimal rule based fuzzy-PID as compared with randomly selected rules for the system. Settling time and peak time are also improved 60–80% compared to the existing method. The control signal generated by the proposed controller is also improved in magnitude by 80–90% compared to the existing method. With a lower magnitude, the control signal can also avoid actuator saturation problems.
“…The results showed its superiority. In [22], authors have compared hybrid genetic algorithm type fractional order PID controller, Fmincon based pattern search based PID controller and hybrid MPC controller. Hybrid genetic algorithm based FOPID gives better response in terms of performance indices.…”
This paper proposes a Particle Swarm Optimization (PSO) tuned novel Proportional Integral Derivative (PID) like neural network (PID-NN). The structure of proposed PID-NN is very simple having only 3 neurons in the hidden layer and a single output neuron. The proportional, integral, and derivative gains of the PID controller are represented by the three weights in the neural network's output layer, respectively. The suggested approach uses the PSO method to optimize the output layer weights, which correspond to PID gains. The non-linear Continuous Stirred Tank Reactor (CSTR) plant, one of the most popular chemical industry processes, is utilized to test the suggested approach. A jacketed CSTR's temperature is controlled via a Particle Swarm Optimization tuned PID like neural network (PSO-NN-PID) controller. In terms of time domain specifications, the performance of the PSO-based NN-PID controller, the back propagation-based NN-PID controller (BP-NN-PID), and the conventionally tuned PID controller are compared. Mean square error is the objective function used in PSO-NN-PID and BP-NN-PID to optimize PID settings. The results show that the overshoot decreases from 44.13% in case of Zeigler-Nichols tuned PID controller to 26.33% in case of BPNN-PID controller, and further reduces to 23.13% in case of proposed PSO-based NN-PID controller. The decrease in rise time is observed from 0.2727 seconds in case of BPNN tuned PID to 0.1283 seconds in case of proposed PSO-NN-PID controller.
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