Airborne Computer System Based Collision-Free Flight Path Finding Strategy Design for Drone Model

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.

Section: Control Parameters Tuning Algorithmsmentioning

confidence: 99%

“…As a result, the particle updates its equations for position and velocity, which are as follows [21]:…”

Section: Gpid_best Particlementioning

confidence: 99%

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

2024

“…Each iteration modifies each particle's location and velocity in accordance with Eqs. ( 7) and ( 8), and the parameters' definitions are shown in Table 2 [19][20][21][22].…”

Section: The Pso Algorithmmentioning

confidence: 99%

Enhancement of the Blood Glucose Level for Diabetic Patients Based on an Adaptive Auto-Tuned PID Controller via Meta-Heuristic Methods

2024

This work proposes an intelligent method that uses an adaptive auto-tuned proportional-integral-derivative (PID) controller to track and regulate the blood glucose level of diabetic patients. The suggested controller seeks to provide the optimal insulin control action, which is in charge of swiftly, precisely, and accurately managing the blood glucose level. To train this controller, two meta-heuristic techniques are employed. The first technique is the particle swarm optimization (PSO), which has been widely used in both data estimation and training because of its quick computing speed, while the second one is an intrusion detection technique called grey wolf optimization (GWO), which was created to categorize data and effectively find multiple intrusions. The mean square error performance index is used in the two distinct meta-heuristic algorithm types to determine and optimize the optimal or nearly optimal gain parameters of the adaptive PID controller. The results of the MATLAB simulations for three different patients demonstrated the efficacy and resilience of the proposed control algorithm in tracking the dynamic behaviour of the diabetic patients' blood glucose levels by minimizing overshoot in the transient state to zero value, maintaining the steady-state blood glucose level in the normal physiological level of (60-120) mg/dl. These characteristics were particularly evident when we added a meal as a disturbance effect. Moreover, the comparison results showed that the proposed PID-GWO and PID-PSO algorithms enhanced the time (96 and 88) minutes to reach the blood glucose level at a normal physiological level by 4% and 12%, respectively, when compared to the fractional order PID and fuzzy logic control algorithms that the blood glucose level reached at a normal physiological level at 100 minutes and improved the time by 20% and 27%, respectively, when compared to the type-2 fuzzy control algorithm that the blood glucose level reached at a normal level at 120 minutes. In particular, the blood glucose level was kept at the desired normal physiological level without any oscillation.

“…The ideas of personal best and global best are introduced into the FA in the proposed algorithm. All the procedures in the FA remain the same, except for the firefly movement, which has been modified to include the concepts of personal and global best [35]. As a result, the FA algorithm's adjusted position vector can be represented as shown in Eqs.…”

Section: The Proposed Hybrid Optimization Algorithmmentioning

confidence: 99%

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

2022

The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way. Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles. Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks. This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA). On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO). To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms. Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively. Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively. All methods are simulated in an environment with static obstacles using the 2018b MATLAB package.