基于IEM的动力定位PID控制器参数整定
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摘要
由于船舶动力定位控制系统是一个复杂的非线性系统,常规整定的PID参数难以取得理想的控制效果,由此提出将免疫类电磁机制(IEM)算法用于PID控制器的参数自整定。针对类电磁机制(EM)算法易陷入局部最优的缺陷,引入免疫信息处理机制,利用其特有的浓度选择机制保留优良的粒子并通过免疫算子使粒子靠近最优位置。使用IEM、EM和PSO算法整定PID控制器参数,分析结果可以得出IEM算法具有更优的稳定性、更高的收敛精度。最后在IEM-PID和常规PID控制器作用下分别对船舶DP的位置和艏向进行仿真,仿真结果表明,相比常规PID控制器,IEM-PID控制器响应速度更快、稳定性更优、稳态误差更小。
Due to the ship dynamic positioning control system is a complex nonlinear system, the conventional PID parameters are difficult to achieve the desired control effect, and the immune electromagnetism-like mechanism( IEM) algorithm is proposed for PID controller parameter self-tuning. In view of the weakness that the electromagnetism-like mechanism(EM) algorithm is easy to fall into the local optimum, the immune information processing mechanism is introduced, and the unique concentration selection mechanism is used to preserve the fine and the particles are close to the optimal position by the immune operator. The performance analysis shows that the IEM algorithm has better stability and better convergence in the self-tuning of PID parameters compared to EM and PSO algorithm. Finally, the position and bow of DP four stage sea condition model are simulated. The simulation results show that compared with the conventional PID controller, the PID controller solved by IEM method has faster response, better stability and smaller steady-state error.
Due to the ship dynamic positioning control system is a complex nonlinear system, the conventional PID parameters are difficult to achieve the desired control effect, and the immune electromagnetism-like mechanism( IEM) algorithm is proposed for PID controller parameter self-tuning. In view of the weakness that the electromagnetism-like mechanism(EM) algorithm is easy to fall into the local optimum, the immune information processing mechanism is introduced, and the unique concentration selection mechanism is used to preserve the fine and the particles are close to the optimal position by the immune operator. The performance analysis shows that the IEM algorithm has better stability and better convergence in the self-tuning of PID parameters compared to EM and PSO algorithm. Finally, the position and bow of DP four stage sea condition model are simulated. The simulation results show that compared with the conventional PID controller, the PID controller solved by IEM method has faster response, better stability and smaller steady-state error.
引文
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[11]ISTIN M,VISAN A,POP F,et al.Near-optimal scheduling based on immune algorithms in distributed environments[C].CISIS′10:Proceedings of the 2010 International Conference on Complex,Intelligent and Software Intensive Systems,2010:439-444.
[12]贾文生,向淑文,杨剑锋,等.基于免疫粒子群算法的非合作博弈Nash均衡问题求解[J].计算机应用研究,2012,29(1):28-31.
[2]KASILINGAM G,PASUPULETI J.A comparative study of the Z-N,adaptation law and PSO methods of tuning the PID controller of a synchronous machine[J].Canadian Journal of Cardiology,2014,7(6):918.
[3]RUANO A E,AZEVEDO A B.B-splines neural network assisted PID autotuning[J].International Journal of Adaptive Control&Signal Processing,2015,13(4):291-306.
[4]譈NAL M,AK A,TOPUZ V,et al.Optimization of PID controllers using ant colony and genetic algorithms[M].Berlin,Heidelberg:Springer,2013.
[5]Liu Xiaoyong.Optimization design on fractional order PID controller based on adaptive particle swarm optimization algorithm[J].Nonlinear Dynamics,2016,84(1):379-386.
[6]姜建国,王双记,刘永青,等.一种实用的类电磁机制算法[J].西安电子科技大学学报(自然科学版),2013,40(2):48-53.
[7]TAN J D,DAHARI M,KOH S P,et al.An improved electromagnetism-like algorithm for numerical optimization[J].Theoretical Computer Science,2016,641:75-84.
[8]Wang Liyun,Xiao Jianmei,Wang Xihuai.Ship dynamic positioning systems based on fuzzy predictive control[J].Telkomnika Indonesian Journal of Electrical Engineering,2013,11(11).
[9]谢业海.海况变化时的船舶定点定位切换自适应控制研究[D].哈尔滨:哈尔滨工程大学,2013.
[10]龙秀萍.类电磁机制算法研究[D].西安:西安电子科技大学,2012.
[11]ISTIN M,VISAN A,POP F,et al.Near-optimal scheduling based on immune algorithms in distributed environments[C].CISIS′10:Proceedings of the 2010 International Conference on Complex,Intelligent and Software Intensive Systems,2010:439-444.
[12]贾文生,向淑文,杨剑锋,等.基于免疫粒子群算法的非合作博弈Nash均衡问题求解[J].计算机应用研究,2012,29(1):28-31.