scholarly journals A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm

Algorithms ◽  
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.

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 Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhaojuan Zhang ◽  
Wanliang Wang ◽  
Ruofan Xia ◽  
Gaofeng Pan ◽  
Jiandong Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Experimental Results ◽  
The Other ◽  
Ancestral Genome ◽  
Swarm Optimization ◽  
Median Approach

Abstract Background Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. Results In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. Conclusions Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

Quantum Particle Swarm Optimization Algorithm

2011 ◽  
Vol 63-64 ◽  
pp. 106-110 ◽  
Author(s):  
Yu Fa Xu ◽  
Jie Gao ◽  
Guo Chu Chen ◽  
Jin Shou Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Quantum Theory ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Experimental Results ◽  
Swarm Optimization ◽  
Local Optima ◽  
Quantum Particle Swarm Optimization

Based on the problem of traditional particle swarm optimization (PSO) easily trapping into local optima, quantum theory is introduced into PSO to strengthen particles’ diversities and avoid the premature convergence effectively. Experimental results show that this method proposed by this paper has stronger optimal ability and better global searching capability than PSO.

Sign in / Sign up

Export Citation Format

Share Document