Application of a Multi-Strategy Improved Sparrow Search Algorithm in Bridge Crane PID Control Systems

To address the anti-swing issue of the payload in bridge cranes, Proportional–Integral–Derivative (PID) control is a commonly used method. However, parameter tuning of the PID controller relies on empirical knowledge and often leads to system overshoot. This paper proposes an Improved Sparrow Search...

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Published inApplied sciences Vol. 14; no. 12; p. 5165
Main Authors Zhang, Youyuan, Liu, Lisang, Liang, Jingrun, Chen, Jionghui, Ke, Chengyang, He, Dongwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2024
Subjects
Accuracy
Algorithms
anti-swing control
Automation
bridge crane
Control algorithms
Control systems
Controllers
Cranes & hoists
Efficiency
fitness function
Kalman filters
Light rail transit
Mathematical optimization
Methods
Neural networks
Optimization algorithms
PID control
sparrow search algorithm
Systems stability
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ISSN2076-3417
2076-3417
DOI10.3390/app14125165

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Summary:To address the anti-swing issue of the payload in bridge cranes, Proportional–Integral–Derivative (PID) control is a commonly used method. However, parameter tuning of the PID controller relies on empirical knowledge and often leads to system overshoot. This paper proposes an Improved Sparrow Search Algorithm (ISSA) to optimize the gains of PID controllers, alleviating adverse effects on payload oscillation and trolley positioning during the operation of overhead cranes. First, tent map chaos mapping is introduced to initialize the sparrow population, enhancing the algorithm’s global search capability. Then, by integrating sine and cosine concepts along with nonlinear learning factors, the updating mechanism of discoverer positions is dynamically adjusted, expediting the solving process. Finally, the Lévy flight strategy is employed to update follower positions, thereby enhancing the algorithm’s local escape capability. Additionally, a fitness function containing overshoot penalties is proposed to address overshoot issues. Simulation results indicate that the overshoot rates of all algorithms remain less than 3%. Moreover, compared with the Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Whale optimization Algorithm (WOA), the optimized PID control system with the ISSA algorithm exhibits superior control performance and possesses certain robustness and adaptability.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app14125165