Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm

Abstract

Electric vehicles with hub motors have integrated the motor into the wheel, which increase the unsprung mass of the vehicle, and intensifies the vibration of the underspring components. The motor excitation during driving also intensifies the wheel vibration. The coupling effect between the two makes the performance of electric vehicles deteriorate. The article employed a disc-type permanent-magnet motor as the hub motor, taking into consideration the increase in sprung mass caused by the hub motor and the adverse effects of vertical vibration from motor excitation. Based on random road-surface excitation, and considering the secondary excitation caused by wheel motor drive and vehicle-road coupling, a coupled-dynamics model of a semi-active-suspension vehicle-road system for vertical vehicle motion is investigated under multiple excitations. Using body acceleration, suspension deflection, and dynamic tire load as evaluation indicators, a BP neural network PID controller based on the sparrow search algorithm optimization is proposed for the semi-active-suspension system. Compared with PID control and particle swarm optimization (PSO-BPNN-PID), the research findings indicate that the optimized semi-active suspension significantly improves the ride comfort of hub-motor electric vehicles, and meets the requirements for control performance under different vehicle driving conditions.

Integrated Design and Control of a Sustainable Stormwater Treatment System

“…This work discusses the combination of a fuzzy PID controller with obtaining gains using the PSO method applied to an induction motor. Very similar cases can be seen in [31][32][33][34].…”

“…In Figure 5, you can see what is described. The profits obtained are as follows: P = 61.3731892931105, I = 2.425D = 59.298 Some works that talk about genetic processes and algorithms include [31][32][33][34]. where Equation ( 12) can be represented in frequency as follows:…”

“…In Figure 5, you can see what is described. The profits obtained are as follows: 𝑃 61.3731892931105, 𝐼 2.425 𝐷 59.298 Some works that talk about genetic processes and algorithms include [31][32][33][34]. In general, the genetic algorithm (GA) starts with several solutions randomly to generate new answers until reaching the most optimal solution after placing the weights and setting the limits ([0.1, 01, 01] and [100, 100, 100]).…”