Tuning of Two-Degrees-of-Freedom PID Controllers via the Multiobjective Genetic Algorithm NSGA-II

2006 ◽  
Author(s):  
Ruben Lagunas-Jimenez ◽  
Guillermo Fernandez-Anaya ◽  
J. Carlos Martinez-garcia
Keyword(s):  
Genetic Algorithm ◽  
Degrees Of Freedom ◽  
Pid Controllers ◽  
Nsga Ii ◽  
Two Degrees Of Freedom ◽  
Multiobjective Genetic Algorithm

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals Single-reservoir operating rules for a year using multiobjective genetic algorithm

2008 ◽  
Vol 10 (2) ◽  
pp. 163-179 ◽  
Author(s):  
Taesoon Kim ◽  
Jun-Haeng Heo ◽  
Deg-Hyo Bae ◽  
Jin-Hoon Kim
Keyword(s):  
Genetic Algorithm ◽  
Piecewise Linear ◽  
Search Space ◽  
Optimization Method ◽  
Optimal Number ◽  
Current Status ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Operating Rules ◽  
Operating Rule

A monthly operating rule for single-reservoir operation is developed in this study. Synthetic inflow data over 100 years are generated by using a time series model, AR(1), and piecewise-linear operating rules consisting of 4 and 5 linear lines are found using the implicit stochastic optimization method. In order to consider multiobjective functions in reservoir system operation, a multiobjective genetic algorithm (NSGA-II) is adopted to obtain the optimization results. The search space of NSGA-II is carefully refined using frequency analysis of historical data, and the relationship between inflow and constraints is also investigated. It is determined that 4 and 5 segments are the optimal number of segments for the piecewise-linear operating rule, and the effect of random number seeding on NSGA-II is evaluated. Six years of historical inflow data are used for the simulation model and the results show that the developed operating rule would handle various inflow series. As a result, probabilistic reservoir storage forecasts can be provided to a system operator so as to enable the operator to evaluate the current status of a reservoir quantitatively.

Dynamic Optimization of a Three Translational Degrees of Freedom Parallel Robot Based on a Multi-Objective Genetic Algorithm

2015 ◽  
Vol 789-790 ◽  
pp. 723-734
Author(s):  
Xing Guo Lu ◽  
Ming Liu ◽  
Min Xiu Kong
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Degrees Of Freedom ◽  
Optimization Problem ◽  
Parallel Robot ◽  
Nsga Ii ◽  
Multi Objective ◽  
Global Dynamic ◽  
Design Variables ◽  
Dynamic Fluctuation

This work tends to deal with the multi-objective dynamic optimization problem of a three translational degrees of freedom parallel robot. Two global dynamic indices are proposed as the objective functions for the dynamic optimization: the index of dynamic dexterity, the index describing the dynamic fluctuation effects. The length of the linkages and the circumradius of the platforms were chosen as the design variables. A multi-objective optimal design problem, including constrains on the actuating and passive joint angle limits and geometrical interference is then formulated to find the Pareto solutions for the robot in a desired workspace. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is adopted to solve the constrained nonlinear multi-objective optimization problem. The simulation results obtained shows that the robot can achieve better dynamic dexterity and less dynamic fluctuation simultaneously after the optimization.

A Novel Two-Degrees-of-Freedom Voice Coil Motor and its PID Controllers for Linear and Rotary Motions

2013 ◽  
Vol 303-306 ◽  
pp. 1635-1640
Author(s):  
Xi Qing Jia ◽  
Shou Bin Liu
Keyword(s):  
Pid Control ◽  
High Speed ◽  
Control Algorithm ◽  
Degrees Of Freedom ◽  
Voice Coil Motor ◽  
Settling Time ◽  
Servo Control ◽  
Pid Controllers ◽  
Two Degrees Of Freedom ◽  
Actual Environment

A two-degrees-of-freedom voice coil motor and its control system are proposed for linear and rotary motions with high speed. This VCM consists of two individual motion parts driven by two separated DSP embedded controllers. For servo control of the VCM, an improved PID control algorithm is adopted. The performance of the control algorithm is evaluated under actual environment. When the targets of position and angle are set at 5mm and 15°, the time response shows 48ms and 70.4ms settling time with 5μm and 3’ steady state error without overshoot. Settling time is reduced to 36% as compared to that of original PID controller.

2DOF Controller Tuning

2015 ◽  
Vol 18 ◽  
pp. 57-64
Author(s):  
Miluse Viteckova ◽  
Antonin Vitecek
Keyword(s):  
Time Delay ◽  
Analytical Method ◽  
Degrees Of Freedom ◽  
Point Of View ◽  
Pid Controllers ◽  
Two Degrees Of Freedom ◽  
First Order ◽  
Quality Of Control ◽  
Dominant Pole

At present the two degrees of freedom (2DOF) controllers are more often available. These controllers enable to tune both from point of view of the desired variable and the disturbance variable as well. It allows increase the overall quality of control. For the tuning of the 2DOF controllers to date there are only a few suitable methods. This paper is devoted to the tuning of the 2DOF PI and PID controllers by the multiple dominant pole method (MDPM) for first order plants and integrating plants with time delay. It is an analytical method and it is shown that by the corresponding tuning of the 2DOF controllers it is possible to obtain the non-oscillatory servo and regulatory step responses without overshoots. The use is demonstrated in the example.

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