scholarly journals Multiparameter Control Strategy and Method for Cutting Arm of Roadheader

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Pengjiang Wang ◽  
Yang Shen ◽  
Xiaodong Ji ◽  
Kai Zong ◽  
Weixiong Zheng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Control Strategy ◽  
Simulation Analysis ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Good Control ◽  
Control Effect ◽  
Swarm Optimization ◽  
Operation Analysis ◽  
Swing Speed

A multiparameter control strategy and method for the cutting arm of a roadheader is proposed through the operation analysis of roadheader. The method can address the problems of low intelligence and low cutting efficiency faced by the roadheader in the cutting process. The control strategy is divided into two parts: the cutting load identification part and the swing speed control part. The former part is designed using a backpropagation neural network that is optimized by an improved particle swarm optimization algorithm. The latter part is optimally designed using a fuzzy PID controller with improved simulated annealing particle swarm optimization. The simulation analysis in SIMULINK showed that the response time was reduced, proving the robustness of the method. In addition, experimental studies verified the good control effect of the method under different cutting states. The proposed method uses multiparameter to intelligently change the swing speed, providing a theoretical and practical basis for the realization of intelligent and unmanned cutting of roadheader.

Intelligent Control Method for Flanking Flight of Unmanned Aerial Vehicles

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Rui Wang ◽  
Xin-Li Yu ◽  
Nian-Chu Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Pid Control ◽  
Particle Swarm Optimization Algorithm ◽  
Control Method ◽  
Particle Swarm ◽  
Good Control ◽  
Control Effect ◽  
Swarm Optimization ◽  
Full Analysis

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.

scholarly journals An Optimal Longitudinal Control Strategy of Platoons Using Improved Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhizhou Wu ◽  
Zhibo Gao ◽  
Wei Hao ◽  
Jiaqi Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Control Strategy ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Experimental Simulation ◽  
Driving Safety ◽  
Relative Speed ◽  
Longitudinal Control ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Most existing longitudinal control strategies for connected and automated vehicles (CAVs) have unclear adaptability without scientific analysis regarding the key parameters of the control algorithm. This paper presents an optimal longitudinal control strategy for a homogeneous CAV platoon. First of all, the CAV platoon models with constant time-headway gap strategy and constant spacing gap strategy were, respectively, established based on the third-order linear vehicle dynamics model. Then, a linear-quadratic optimal controller was designed considering the perspectives of driving safety, efficiency, and ride comfort with three performance indicators including vehicle gap error, relative speed, and desired acceleration. An improved particle swarm optimization algorithm was used to optimize the weighting coefficients for the controller state and control variables. Based on the Matlab/Simulink experimental simulation, the analysis results show that the proposed strategy can significantly reduce the gap error and relative speed and improve the flexibility and initiative of the platoon control strategy compared with the unoptimized strategies. Sensitivity analysis was provided for communication lag and actuator lag in order to prove the applicability and effectiveness of this proposed strategy, which will achieve better distribution of system performance.

Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 35-43
Author(s):  
K. M. Ang ◽  
Z. S. Yeap ◽  
C. E. Chow ◽  
W. Cheng ◽  
W. H. Lim
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Test Problems ◽  
Initial Population ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Initialization Scheme ◽  
Chaotic Initialization

Different variants of particle swarm optimization (PSO) algorithms were introduced in recent years with various improvements to tackle different types of optimization problems more robustly. However, the conventional initialization scheme tends to generate an initial population with relatively inferior solution due to the random guess mechanism. In this paper, a PSO variant known as modified PSO with chaotic initialization scheme is introduced to solve unconstrained global optimization problems more effectively, by generating a more promising initial population. Experimental studies are conducted to assess and compare the optimization performance of the proposed algorithm with four existing well-establised PSO variants using seven test functions. The proposed algorithm is observed to outperform its competitors in solving the selected test problems.

Research on control method of Maglev vehicle-guideway coupling vibration system based on particle swarm optimization algorithm

2019 ◽  
Vol 25 (16) ◽  
pp. 2237-2245
Author(s):  
Qin Li ◽  
Hui Wang ◽  
Gang Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Control Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Test Rig ◽  
Swarm Optimization ◽  
Coupling Vibration ◽  
Maglev Vehicle ◽  
Track Stiffness

To solve the problem of vehicle-guideway coupling vibration, a new control approach for the Maglev vehicle-guideway coupled system was investigated. A simplified model of the system was built and a control strategy based on full state feedback and particle swarm optimization algorithm was designed. The robustness of the system considering different track stiffness and the maximum voltage of the magnet were considered when the cost function of the particle swarm algorithm was designed. A real-time test rig using dSPACE was built to test the control strategy. The result from the test rig shows that the new designed control strategy can keep the system stable and has a better response than the traditional linear quadratic optimal method, the control voltage is much lower, the settling time of step response is decreased and the maximum overshoot of the air gap is decreased more than 88%. The robustness of the system in different track stiffness conditions is also much better; that is, when the magnet and the track move relative to each other, the maximum amplitude of vibration of both the track and the magnet is 40–70% lower, and the oscillation caused by the shifting of the track beam converges much more quickly.

Sign in / Sign up

Export Citation Format

Share Document