Optimization of automatic steering control on a vehiclewith a steer-by-wire system using particle swarm optimization

Fuzzy Logic includes a technique are widely applied to the vehicle steering control system, however, to get the parameters required by a reliable Fuzzy Logic Control (FLC), needed training and learning process. Quantum behaved Particle Swarm Optimization (QPSO) is a simple optimization method that guarantees the achievement of global convergence quickly. This paper aimed to optimize of the steering control system on vehicle with steer-by-wire system using QPSO. The vehicle steering control system consists of Fuzzy Logic Control (FLC) and the Proportional, Integral and Derivative (PID) control are built in cascade, in which FLC is used to minimize the lateral motion error and PID control is used to suppress yaw motion error of the vehicle. The parameters of the control system are optimized by QPSO consists of three parameters to determine the position of the centre and the width of the triangle membership function of FLC and three constant gain of PID control. The optimization is done through the software in the loop simulation of vehicle models represented by 10 Degree of Freedom (DOF) of the vehicle dynamics. Simulation results showed that optimization using QPSO on the parameters of the control system can guarantee the movement of the vehicle is constantly maintained at the desired trajectory with a smaller error and higher vehicle speeds compared to the control system without tuned. The results obtained will be used as the basis for testing of the hardware in the loop simulation (HILS) so it can further improve the performance of steer-by-wire system.

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan ◽

10.32492/jintake.v9i1.720 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1-5

Author(s):

Fachrudin Hunaini ◽

Imam Robandi ◽

Nyoman Sutantra

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Control System ◽

Membership Function ◽

Fuzzy Logic Control ◽

Particle Swarm ◽

Optimization Method ◽

Swarm Optimization ◽

Motion Error ◽

Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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