Design of Anti-Swing PID Controller for Bridge Crane Based on PSO and SA Algorithm

• Published in: Electronics (Basel) Vol. 11; no. 19; p. 3143
• Main Authors: Li, Hui, Hui, Yan-Bo, Wang, Qiao, Wang, Hong-Xiao, Wang, Lin-Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Adaptive algorithms; Algorithms; Automation; Control systems design; Controllers; Cranes; Cranes & hoists; Gantry cranes; Machine learning; Mathematical optimization; Methods; Mutation; Neural networks; Optimization; Particle swarm optimization; Proportional integral derivative; Search algorithms; Simulated annealing; Simulated annealing (Mathematics); Simulation; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11193143

Since the swing of the lifting load and the positioning of the trolley during the operation of a bridge crane seriously affect the safety and reliability of its work, we have not only designed Proportional Integral Derivative (PID) controllers for the anti-swing and positioning control but also proposed a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm to optimize the gains of the controllers. In updating the PSO algorithm, a nonlinear adaptive method is utilized to update the inertia weight and learning coefficients, and the SA algorithm is also integrated when the PSO algorithm is searching for a global optimal solution, to reduce the probability of falling into the local optimal solution. The simulation results demonstrate that the PSO–SA algorithm proposed in this paper is prone to be a more effective method in searching for the optimal parameters for the controllers, compared with three other algorithms. As shown by the experimental results, the swing angle stabilization time of the novel algorithm is 6.9 s, while the values of the other algorithms range from 10.3 to 13.1 s under a common working condition. Simultaneously, the maximum swing angle of the novel algorithm is 7.8°, which is also better than the other algorithms.