Novel Hybrid Iterative Learning–Fractional Predicative PI Controller for Time-Delay Systems

Devan, P. Arun Mozhi; Hussin, Fawnizu Azmadi; Ibrahim, Rosdiazli; Nagarajapandian, M.; Assaad, Maher

doi:10.1007/978-981-19-3501-5_7

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…It is worth mentioning that the ILC approach controls the control signal’s input levels, while alternative learning with adaptation controls systems change entirely the connected processors. ILC is a helpful instrument for industrial time-delayed one and repeated action applications because of the performance increase acquired via repeated learning practices [ 39 , 41 , 42 ]. In uncertain conditions, including classical controllers the feed-forward route is a popular method in the design of closed-loop management framework with single-input, single-output designs.…”

Section: Iterative Learning Controller Based Pid Controllermentioning

confidence: 99%

“…Iterative Learning Control (ILC) is an effective method for increasing system outcomes through ongoing instruction obtained from reiterated system interactions, much like how people learn new skills through doing tasks repeatedly, such as driving a motorcycle, automobile, which comes from ongoing instruction and learning experiences [36][37][38]. The ILC even with unpredictable models, the controller lowers transient tracking error and improves system efficiency by assimilating error information for each cycle [39,40].…”

Section: Iterative Learning Controller Based Pid Controllermentioning

confidence: 99%

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Machine learning algorithm for ventilator mode selection, pressure and volume control

T.,

G.,

et al. 2024

PLoS ONE

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Mechanical ventilation techniques are vital for preserving individuals with a serious condition lives in the prolonged hospitalization unit. Nevertheless, an imbalance amid the hospitalized people demands and the respiratory structure could cause to inconsistencies in the patient’s inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural approach for generating optimal inhalation strategies. Moreover, machine learning-based classifiers are used to evaluate the precision and performance of the ILC-PID controller. These classifiers able to forecast and choose the perfect type for various inhalation modes, eliminating the likelihood that patients will require mechanical ventilation. In pressure control, the suggested accurate neural categorization exhibited an average accuracy rate of 88.2% in continuous positive airway pressure (CPAP) mode and 91.7% in proportional assist ventilation (PAV) mode while comparing with the other classifiers like ensemble classifier has reduced accuracy rate of 69.5% in CPAP mode and also 71.7% in PAV mode. An average accuracy of 78.9% rate in other classifiers compared to neutral network in CPAP. The neural model had an typical range of 81.6% in CPAP mode and 84.59% in PAV mode for 20 cm H2O of volume created by the neural network classifier in the volume investigation. Compared to the other classifiers, an average of 72.17% was in CPAP mode, and 77.83% was in PAV mode in volume control. Different approaches, such as decision trees, optimizable Bayes trees, naive Bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and F1 score.

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Section: Iterative Learning Controller Based Pid Controllermentioning

confidence: 99%

Section: Iterative Learning Controller Based Pid Controllermentioning

confidence: 99%

Machine learning algorithm for ventilator mode selection, pressure and volume control

T.,

G.,

et al. 2024

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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Mechanical Ventilator Pressure and Volume Control Using Classifier Machine Learning Algorithm for Medical Care

Anitha,

Gopu,

Arun Mozhi Devan

2023

J. Electr. Eng. Technol.

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A novel fractional-order dead-time compensating controller for the wireless networks

Devan,

Ibrahim,

Omar

et al. 2023

Sci Rep

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Wireless technology is becoming increasingly critical in industrial environments in recent years, and the popular wireless standards are WirelessHART, ZigBee, WLAN and ISA100.11a, commonly used in closed-loop systems. However, wireless networks in closed-loop control experience packet loss or drops, system delay and data threats, leading to process instability and catastrophic system failure. To prevent such issues, it is necessary to implement dead-time compensation control. Traditional techniques like model predictive and predictive PI controllers are frequently employed. However, these methods’ performance is sluggish in wireless networks, with processes having long dead times and set-point variations, potentially affecting network and process performance. Therefore, this paper proposes a fractional calculus-based predictive PI compensator for wired and wireless networks in the process control industries. The proposed technique has been simulated and evaluated on industrial process models, including pressure, flow, and temperature, where measurement and control are carried out wirelessly. The wireless network’s performance has been evaluated based on packet loss, reduced throughput, and increased system latency. The proposed compensator outperformed traditional methods, demonstrating superior set-point tracking, disturbance rejection, and delay compensation characteristics in the performance evaluations of the first, second, and third-order systems. Overall, the findings indicate that the proposed compensator enhances wireless networks’ performance in the process control industry and improves system stability and reliability by reducing almost half of the overshoot and settling an average of 8.3927% faster than the conventional techniques in most of the systems.

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Novel Hybrid Iterative Learning–Fractional Predicative PI Controller for Time-Delay Systems

Cited by 5 publications

References 38 publications

Machine learning algorithm for ventilator mode selection, pressure and volume control

Machine learning algorithm for ventilator mode selection, pressure and volume control

Mechanical Ventilator Pressure and Volume Control Using Classifier Machine Learning Algorithm for Medical Care

A novel fractional-order dead-time compensating controller for the wireless networks

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