Robust RBF neural network–based backstepping controller for implantable cardiac pacemakers

Karar, Mohamed Esmail

doi:10.1002/acs.2884

Cited by 28 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the other methods of the PID tuned by BBO, the PID and the immune controllers provide a plausible response. Table 4 addresses a direct comparison between the three controllers, and Robust RBF-neural-network based backstepping Figure. 9 The YNI model response with HR=70 bpm regulated by immune-PID controller in FPGA control in [7] with respect to the maximum overshoot (%), rise time, settling time and ess respectively. Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…the paper uses Zeigler-Nichols tuning to determine the PID parameters, where it represents the output layer weights in the neural network. M. E. Karar suggests a new back-stepping adaptive controller for heart rate regulation, in dual sensor pacemaker based on radial basis function [7]. The developed controller is tested using pre-determined data of four patients heart rate during different activities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and FPGA Implementation of Immune-PID Controller Based on BBO Algorithm for Heart Rate Regulation

Mohsin¹,

Tashan²,

Karam³

2021

IJIES

View full text Add to dashboard Cite

In this paper an optimal Immune-Proportional-Integral-Derivative (Immune-PID) scheme is suggested to control to the heart rate of humans and regulate it, based on Yanagihara, Noma, and Irisawa (YNI) as mathematical model of the heart rate. The parameters of the suggested immune PID controller are optimized by using Biogeography-Based-Optimization (BBO) algorithm. In addition, the proposed controller scheme is implemented using Field Programmable Gate Array (FPGA) due to its great data storage capacity, low consumption of energy, and high speed of operation. The Xilinx system generator blocks and Spartan-6 (XC6SLX45T-3FGG484) board are used due to availability with 1% utilization of hardware platform slices. The performance of the proposed controller is compared to traditional PID and immune controllers. That comparative analysis results show best improvement when using the proposed controller with 0% maximum overshoot, a reduction of steady state error and rising time reaches to 0.1 and 0.0023 second respectively and faster response reaches to 0.01 second.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and FPGA Implementation of Immune-PID Controller Based on BBO Algorithm for Heart Rate Regulation

Mohsin¹,

Tashan²,

Karam³

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…Artificial neural networks such as multilayer perceptron (MLP) and radial basis function (RBF) neural networks, and deep neural networks have been widely used in many applications [14,15,16]. These neural networks showed powerful performance to deal with real problems in an efficient way by initial training of labeled data, and then autonomously operated [17,18].…”

Section: Introductionmentioning

confidence: 99%

IoT and Neural Network-Based Water Pumping Control System For Smart Irrigation

2020

Inf. Sci. Lett.

View full text Add to dashboard Cite

This article aims at saving the wasted water in the process of irrigation using the Internet of Things (IoT) based on a set of sensors and Multi-Layer Perceptron (MLP) neural network. The developed system handles the sensor data using the Arduino board to control the water pump automatically. The sensors measure the environmental factors; namely temperature, humidity, and soil moisture to estimate the required time for the operation of water irrigation. The water pump control system consists of software and hardware tools such as Arduino Remote XY interface and electronic sensors in the framework of IoT technology. The machine learning algorithm such as the MLP neural network plays an important role to support the decision of automatic control of IoT-based irrigation system, managing the water consumption effectively.

show abstract

“…The mathematical model of neurons in neural networks was first proposed by Mcculloch and Pitts in 1943. It has been widely used in image processing [7], load forecasting [8], medical device [9] and pattern recognition [10]. The greatest advantage of artificial neural networks is that the observed data is continuously learned as a mechanism for approaching arbitrary function.…”

Section: Introductionmentioning

confidence: 99%

“…9) where e is a tracking error, r e q q = − , c is a positive constant, r q is a reference trajectory. The derivative of Eq.…”

mentioning

confidence: 99%

Dynamic Fractional Order Sliding Mode Control Method of Micro Gyroscope Using Double Feedback Fuzzy Neural Network

Fei

Chen

2020

IEEE Access

View full text Add to dashboard Cite

In this paper, a dynamic fractional order sliding mode control method based on a double feedback fuzzy neural network controller is proposed to deal with the unknown parameters and upper bound of uncertainty. Firstly, the switching function of the dynamic fractional order sliding mode control is designed, which not only fixes switching function of the ordinary sliding mode control, but also increases the fractional order, so that the switching function has a higher degree of freedom. In addition, the expert experience of fuzzy logic and the self-learning ability of neural network are used to improve the control accuracy and estimate the upper bound of uncertainty. Meanwhile, by using Lyapunov stability theory, the adaptive laws of unknown parameters in the system are derived to realize online adjustment, which increases the robustness of the system. Finally, the simulation results show that the proposed control method is more effective than the ordinary adaptive sliding mode control method in terms of convergence speed, parameter fitting effect, output signal tracking speed and tracking error. INDEX TERMS Fractional order; Dynamical sliding mode control; Fuzzy system; Neural network; Micro gyroscope.

show abstract

Robust RBF neural network–based backstepping controller for implantable cardiac pacemakers

Cited by 28 publications

References 26 publications

Design and FPGA Implementation of Immune-PID Controller Based on BBO Algorithm for Heart Rate Regulation

Design and FPGA Implementation of Immune-PID Controller Based on BBO Algorithm for Heart Rate Regulation

IoT and Neural Network-Based Water Pumping Control System For Smart Irrigation

Dynamic Fractional Order Sliding Mode Control Method of Micro Gyroscope Using Double Feedback Fuzzy Neural Network

Contact Info

Product

Resources

About