An Adaptive Cruise Control Method Based on Improved Variable Time Headway Strategy and Particle Swarm Optimization Algorithm

The angle control during the flight of UAV is the most important factor which affects its stability and safety. Since the traditional PID control method is difficult to automatically adjust the control parameters, a particle swarm optimization algorithm based on traditional PID control (PSO-PID), is proposed to construct a mathematical model of the flanking flight of the UAV. Based on the full analysis of the PID control principle, the UAV’s flanking flight controller based on PID control is constructed. The particle swarm optimization algorithm is introduced to optimize the PID parameters. The simulation model is built in MATLAB to investigate the position and altitude angle change of the UAV’s flank and compare it with the traditional PID control method. The experimental results show that the PSO-PID control strategy has a good control effect, which enables UAV’s flanking flight to reach the specified position more quickly and accurately than traditional PID controller alone.

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A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm

Algorithms ◽

10.3390/a14060173 ◽

2021 ◽

Vol 14 (6) ◽

pp. 173

Author(s):

Zhuo-Qiang Zhao ◽

Shi-Jian Liu ◽

Jeng-Shyang Pan

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Control Method ◽

Particle Swarm ◽

Parameter Tuning ◽

Experimental Results ◽

Control Effect ◽

Swarm Optimization ◽

Tuning Method

The PID (proportional–integral–derivative) controller is the most widely used control method in modern engineering control because it has the characteristics of a simple algorithm structure and easy implementation. The traditional PID controller, in the face of complex control objects, has been unable to meet the expected requirements. The emergence of the intelligent algorithm makes intelligent control widely usable. The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a new evolutionary algorithm. Compared with other intelligent algorithms, the QUATRE algorithm has a strong global search ability. To improve the accuracy of the algorithm, the adaptive mechanism of online adjusting control parameters was introduced and the linear population reduction strategy was adopted to improve the performance of the algorithm. The standard QUATRE algorithm, particle swarm optimization algorithm and improved QUATRE algorithm were tested by the test function. The experimental results verify the advantages of the improved QUATRE algorithm. The improved QUATRE algorithm was combined with PID parameters, and the simulation results were compared with the PID parameter tuning method based on the particle swarm optimization algorithm and standard QUATRE algorithm. From the experimental results, the control effect of the improved QUATRE algorithm is more effective.

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Inventory Control Method of Reverse Logistics for Shipping Electronic Commerce Based on Improved Multi-objective Particle Swarm Optimization Algorithm

Journal of Coastal Research ◽

10.2112/si83-128.1 ◽

2019 ◽

Vol 83 (sp1) ◽

pp. 786 ◽

Cited By ~ 1

Author(s):

Wei Yang ◽

Qianglai Xie ◽

Ming Li

Keyword(s):

Particle Swarm Optimization ◽

Electronic Commerce ◽

Inventory Control ◽

Optimization Algorithm ◽

Reverse Logistics ◽

Particle Swarm Optimization Algorithm ◽

Control Method ◽

Particle Swarm ◽

Swarm Optimization ◽

Multi Objective

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The LQR Controller Design of Two-Wheeled Self-Balancing Robot Based on the Particle Swarm Optimization Algorithm

Mathematical Problems in Engineering ◽

10.1155/2014/729095 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 10

Author(s):

Jian Fang

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Control Method ◽

Particle Swarm ◽

System Stability ◽

Particle Swarm Algorithm ◽

Swarm Optimization ◽

Lqr Controller ◽

Balancing Robot

The dynamics model is established in view of the self-designed, two-wheeled, and self-balancing robot. This paper uses the particle swarm algorithm to optimize the parameter matrix of LQR controller based on the LQR control method to make the two-wheeled and self-balancing robot realize the stable control and reduce the overshoot amount and the oscillation frequency of the system at the same time. The simulation experiments prove that the LQR controller improves the system stability, obtains the good control effect, and has higher application value through using the particle swarm optimization algorithm.

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A Robust Large Envelope Flight Controller Design Method Based on the Particle Swarm Optimization Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.908 ◽

2013 ◽

Vol 347-350 ◽

pp. 908-911

Author(s):

Zhan Qi Fan ◽

Lin Liu ◽

Xun Sun

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Flight Control ◽

Particle Swarm Optimization Algorithm ◽

Control Method ◽

Controller Design ◽

Weighting Function ◽

Design Method ◽

Particle Swarm ◽

Swarm Optimization

A robust flight control method based on the particle swarm optimization (PSO) algorithm is approved in this paper. Because of non-modeling dynamic character and parameter uncertainty are taken into consideration during the controller design process, the flight controller has strong robustness, excellent control performance and one robust controller could realize the large envelope flight control. In order to design the robust flight controller automatically and rapidly, the particle swarm optimization algorithm is used to select the weighting function. The simulation result shows that the weighting function could be designed automatically and rapidly, the flight controller has good performance and robustness.

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