Suthar Haresh A, Gadit Jagrut J
Electronics & Communication Engineering Department of Parul Institute of Technology, Parul University, Po. Limda, Vadodara, Gujarat, India.
Electrical Engineering Department of Faculty of Technology & Engineering, The M.S.U of Baroda, Vadodara, Gujarat, India.
Heliyon. 2019 Apr 4;5(4):e01410. doi: 10.1016/j.heliyon.2019.e01410. eCollection 2019 Apr.
In this paper, Evolutionary (NSGA-II and NSGA-III) and Swarm Intelligence (MOPSO) based algorithms enhanced with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to optimize five parameters of Two Degree Of Freedom (2DOF) controller. Three objective functions, one for set point tracking and two for disturbance rejections (flow variation of input fluid and temperature variation of input fluid both are in conflict) are deployed for the problem of shell and tube heat exchanger. Three test criteria IAE, ISE and ITAE function of error (set point tracking and disturbance rejection) and time are used for evaluation of objective functions. The Pareto set of solutions are obtained after optimizing all the five parameters of 2DOF controller. In order to obtain the comparative analysis of optimization algorithms (NSGA-II, NSGA-III, and MOPSO) all the Pareto optimal solutions are combined under three separate evaluation criteria IAE, ISE, and ITAE. TOPSIS a multiple criteria decision making method is used to rank the set of Pareto optimal solutions for reducing number of Pareto optimal solutions to a single solution. The best rank solution obtain for 2DOF controller parameters after applying TOPSIS on set of Pareto optimal solutions using Evolutionary (NSGA-II and NSGA-III) algorithms are compared with Swarm Intelligence (MOPSO) algorithm. To evaluate the performance optimization of 2DOF controller tuning, we compared the values of peak overshoot of step response, set point tracking error, disturbance rejection (both flow and temperature), settling time, and the percentage of solutions obtained from optimization algorithms under all three evaluation criteria IAE, ISE, and ITAE. MATLAB software tool is used to implement the above algorithms.
在本文中，采用基于进化算法（NSGA-II和NSGA-III）以及群体智能算法（MOPSO）并结合理想解相似度排序法（TOPSIS）对二自由度（2DOF）控制器的五个参数进行优化。针对管壳式换热器问题，部署了三个目标函数，一个用于设定值跟踪，两个用于干扰抑制（输入流体的流量变化和输入流体的温度变化相互冲突）。使用误差（设定值跟踪和干扰抑制）与时间的三个测试准则IAE、ISE和ITAE函数来评估目标函数。在对2DOF控制器的所有五个参数进行优化后，得到了帕累托解集。为了对优化算法（NSGA-II、NSGA-III和MOPSO）进行对比分析，所有帕累托最优解在IAE、ISE和ITAE这三个单独的评估准则下进行合并。TOPSIS作为一种多准则决策方法，用于对帕累托最优解集进行排序，以便将帕累托最优解的数量减少到单个解。将基于进化算法（NSGA-II和NSGA-III）的帕累托最优解集应用TOPSIS后得到的2DOF控制器参数的最佳排序解与群体智能算法（MOPSO）进行比较。为了评估2DOF控制器整定的性能优化效果，我们在IAE、ISE和ITAE这三个评估准则下，比较了阶跃响应的超调峰值、设定值跟踪误差、干扰抑制（流量和温度）、调节时间以及从优化算法中获得的解的百分比。使用MATLAB软件工具来实现上述算法。
