Airborne Computer System Path-Tracking Based Multi-PID-PSO Controller Design

Abdullah, Mohammed Najm; Dagher, Khulood E.

doi:10.22266/ijies2021.0630.33

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…The RBF neural network feedforward algorithm is calculated according to Eq. ( 15) [14], which describes the discrete PID controller as follows:…”

Section: Control Strategy Designmentioning

confidence: 99%

“…As every particle in the group symbolizes a point that moves across a (Dim)-dimensional search space, the positions of the particles indicate multiple potential sets of unknown parameters that need to be idealized. The rate at which the particle's location changes is determined by its velocity, while its fitness or quality measure is determined by its position inside the search space [14,20].…”

Section: Control Parameters Tuning Algorithmsmentioning

confidence: 99%

“…Each iteration modifies each particle's location and velocity in accordance with Eqs. ( 23) and (24) [14], and the parameters' definitions are shown in Table 2. According to the description in [21], the basic steps of the CPSO algorithm involve finding and finetuning the parameters of the nonlinear RBF-NN-PID controller and incorporating the chaos method into International Journal of Intelligent Engineering and Systems, Vol.17, No.…”

Section: Control Parameters Tuning Algorithmsmentioning

confidence: 99%

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Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

2024

IJIES

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This paper presents an adaptive neural network acting like a proportional-integral-derivative (PID) controller that uses an intelligent meta-heuristic technique to improve the drug infusion rate (propofol) as a manipulated variable in closed-loop control of anesthesia systems using the Bispectral Index (BIS) as the primary controlled variable. The effect of propofol on the human body is modelled using the pharmacokinetic (PK) and pharmacodynamics (PD) models. A physiological dataset of patients, including gender, weight, height, age, and the like, determines the parameters of the PK/PD mathematical model. The proposed controller seeks to provide the optimal propofol control action, which is in charge of swiftly, precisely, and accurately maintaining a triad of hypnosis, analgesia, and neuromuscular blockade by infusing several drugs that are specific to each state. To train this neural network like a PID controller with the radial basis function (RBF) in a neuron, the meta-heuristic method is employed. The first technique is particle swarm optimization (PSO), which has been widely used in both data estimation and training because of its quick computing speed, while the second technique is the chaotic PSO algorithm, and the third technique is the modified CPSO algorithm (MCPSO). The fundamental proposed procedures of the MCPSO algorithm use the chaos method, including the coefficients of acceleration, and remove the two random parameters from the velocity update equation to generate more randomness in the search space to quickly solve the local minima problem. The PSO, CPSO, and MCPSO meta-heuristic algorithms use the mean square error (MSE) performance index to find and optimize the optimal or the nearly ideal gain parameters of the nonlinear neural network to function like a PID controller. The simulation results show that the proposed controller for different physiological dataset patients is characterized by its efficacy and resilience in terms of controlling the depth of the hypnosis state and the infusion rate of the anesthetic drug during surgery in order to avoid under-or over-dosing of the drug for the patient through the desired value of BIS (50) with minimizing the steady-state error, which is equal to zero without any oscillation. Moreover, the comparison results showed that the proposed RBF-NN-PID controller enhanced the time in one minute to reach the depth of anesthesia at the moderate hypnotic state when compared to the fractional-order adaptive highgain controller, in which the time to reach the depth of anesthesia is two minutes. In contrast, the adaptive neuro-fuzzy controller reached the depth of anesthesia in three minutes. Therefore, the time was improved by 50% and 67%, respectively. In particular, the surgery BIS index was kept at the BIS desired 50 at the moderate hypnotic state without any error and with no oscillation at steady-state.

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“…The RBF neural network feedforward algorithm is calculated according to Eq. ( 15) [14], which describes the discrete PID controller as follows:…”

Section: Control Strategy Designmentioning

confidence: 99%

Section: Control Parameters Tuning Algorithmsmentioning

confidence: 99%

Section: Control Parameters Tuning Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

2024

IJIES

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“…The speed and position update methods for the i th particle at the (𝑘 + 1 ) iteration are represented in Eqs. ( 4) and ( 5), respectively [26][27][28][29][30].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

2022

IJIES

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During the previous years, the number of researchers who discussed the field of path planning algorithms has increased. In this paper, a proposed hybrid technique for path planning based on the benefits of sampling-based algorithms and heuristic path planning algorithms was enhanced. Rapidly-exploring Random Tree star (RRT*) was used to generate the candidate nodes that represent a collision-free path. These candidate nodes were applied to the Particle Swarm Optimization (PSO) algorithm to generate the shortest and the smoothest path planning (the optimal path planning) for the mobile robot. The RRT*PSO algorithm was applied in two scenarios (static and dynamic environments) with a workspace of [500×500] cm using MATLAB 2021a. In the static environment, a workspace full of obstacles was chosen to find the shortest collision-free path. In addition, a reference path equation was found to calculate the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels. In this work, the suggested hybrid RRT*PSO algorithm improves path length 34.27% compared to the A* algorithm and the fuzzy analytic hierarchy process (A*-FAHP hybrid) algorithm and 0.35% compared to the Self-adaptive evolutionary game-based particle swarm optimization (SAEGBPSO) algorithm. In the dynamic environment, an algorithm named (contour path down and contour path up) was suggested to avoid collisions with dynamic obstacles. By using this suggested algorithm, the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels were calculated to obtain a smooth path followed and free-navigation with dynamic obstacles in an environment.

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“…Cooperation between security researchers, software providers, and the broader security community is essential for responsibly disclosing zero-day vulnerabilities. We discuss the difficulties and ethical issues that arise throughout this procedure, and we stress the importance of applying fixes quickly to protect against zero-day exploits [11].…”

Section: Introductionmentioning

confidence: 99%

Zero-Day Vulnerabilities: Unveiling the Threat Landscape in Network Security

zaib

2022

MJCS

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Today's digital ecosystem is particularly vulnerable to attacks due to zero-day vulnerabilities. Attackers take advantage of these flaws, which software developers and security experts are unaware of, leading to catastrophic outcomes. Through an analysis of their lifetime, discovery approaches, exploitation methods, disclosure protocols, and patching strategies, this study tries to reveal the danger landscape of zero-day vulnerabilities. We examine the effects of zero-day vulnerabilities on network security and the difficulties that businesses confront by reviewing the relevant literature and discussing actual cases. We also investigate preventative measures and methods of defense, such as intrusion and anomaly detection systems and cutting-edge AI. We stress the need for responsible disclosure, prompt patching, and continued research to counter these difficult to detect dangers. In order to better understand zero-day vulnerabilities, implement countermeasures, and respond to them, this article is an invaluable resource for researchers, security experts, and organizations.

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Airborne Computer System Path-Tracking Based Multi-PID-PSO Controller Design

Cited by 10 publications

References 15 publications

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

Zero-Day Vulnerabilities: Unveiling the Threat Landscape in Network Security

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