Optimum PID controller for airplane wing tires based on gravitational search algorithm

Abstract

In this paper, the gravitational search algorithm (GSA) is proposed as a method for controlling the opening and closing of airplane wing tires. The GSA is used to find the optimum proportional-integral-derivative (PID) controller, which controls the wing tires during take-off and landing. In addition, the GSA is suggested as an approach for overcoming the absence of the transfer function, which is usually required to design the optimum PID. The use of the GSA is expected to improve the system. Two of the most popular optimisation algorithms-the harmony search algorithm (HSA) and the particle swarm optimisation (PSO)-were used for the sake of comparison. Moreover, the GSA-, HSA- and PSO-based optimum PID controllers were compared with one of the most important PID tuning methods, the Ziegler-Nichols (ZN) method. In this study, the integral time absolute error (ITAE) was used as a fitness function. First, four transfer functions for different applications were used to compare the performance of the GSA-based PID (PID-GSA), HSA-based PID (PID-HSA), PSO-based PID (PID-PSO) and Ziegler-Nichols-based PID (PID-ZN). Next, the GSA was used to design the optimum PID controller for the opening and closing systems of the airplane wing tires. The results reveal that the GSA provides better outcomes in terms of ITAE when compared with the other adopted algorithms. Furthermore, the GSA demonstrates a fast and robust response to reference variation.

Design and Experimental Study of an Efficient Controlled Cooling System for Optical Communication Laser Diodes

“…The PID control process is based on a feedback circle to control process variables (PVs), where each PV is a system quantity that is measured by a sensor and controlled to match a desired set output point (SP), as shown in Figure 2 [22]. The error in each feedback circle is generated due to the difference between the PV value and SP value [23]. This error will be used as a reference by the PID controller to generate a control signal [24,25].…”

Artificial Neural Network-Based Transmission Power Control for Underwater Wireless Optical Communication System

“…The effectiveness of an ANN model is largely dependent on its architecture, where the algorithm that is utilized for training, and the selection of the s tructure utilized during the process of training are very important factors in ANN performance [27,28]. The ANN model could also be combined with many other optimization algorithms, such as an adaptive neural-fuzzy inference system [29].…”

Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks

“…The MG could manage, aggregate, and deploy DGs, for the most part when a grid is disconnected. Alternative aggregator choice dependent on smart grid upgrades is the concept of a virtual power plant (VPP) [9][10][11].…”

“…In this regard, scheduling problems need a smart controller for the VPP system using optimization algorithms. Thus, several optimization algorithms have been established by researchers recently, such as the genetic algorithm [7], gravitational search algorithm [9][10][11], butterfly algorithm [12], herd-related optimization approaches [13], whale optimization algorithm [14], cat swarm optimization [15], practical swarm optimization (PSO) [16], etc. The energy management duties are to ensure security; use a mixture of energy, generation, transmission, and distribution resources; and minimize losses and increase profit [17][18][19][20][21].…”