Multiobjective Weighting Selection for Optimization-Based Control Design

1997 ◽  
Vol 122 (3) ◽  
pp. 567-569 ◽  
Author(s):  
Ricardo H. C. Takahashi ◽  
Juan F. Camino and ◽  
Douglas E. Zampieri ◽  
Pedro L. D. Peres
Keyword(s):  
Control Design ◽  
Active Suspension ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Cost Functional ◽  
Design Variables ◽  
Optimal Controllers ◽  
Selection For ◽  
Multiobjective Design

A methodology for the multiobjective design of controllers is presented, motivated by the problem of designing an active suspension controller. This problem has, as a particular feature, the possibility of being defined with two design variables only. The multiobjective controller is searched inside the space of “optimal controllers” defined by a weighted cost functional. The weightings are taken as the optimization variables for the multiobjective design. The method leads to (local) Pareto-optimal solutions and allows the direct specification of controller constraints in terms of some primary objectives which are taken into account in the multiobjective search. [S0022-0434(00)01403-9]

Cluster Analysis and Switching Algorithm of Multi-Objective Optimal Control Design

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Zhi-Chang Qin ◽  
Jian-Qiao Sun
Keyword(s):  
Optimal Control ◽  
Cluster Analysis ◽  
Control Algorithm ◽  
Control Design ◽  
Optimal Designs ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Multi Objective ◽  
Switching Algorithm ◽  
Optimal Controllers

The multi-objective optimal control design usually generates hundreds or thousands of Pareto optimal solutions. How to assist a user to select an appropriate controller to implement is a postprocessing issue. In this paper, we develop a method of cluster analysis of the Pareto optimal designs to discover the similarity of the optimal controllers. After we identify the clusters of optimal controllers, we develop a switching strategy to select controls from different clusters to improve the performance. Numerical and experimental results show that the switching control algorithm is quite promising.

Multi-Objective Optimization of Film-Cooling Holes Considering Heat Transfer and Aerodynamic Loss

2011 ◽  
Author(s):  
Ki-Don Lee ◽  
Sun-Min Kim ◽  
Kwang-Yong Kim
Keyword(s):  
Film Cooling ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Aerodynamic Loss ◽  
Multi Objective ◽  
Design Variables ◽  
Expansion Angle ◽  
Objective Function Values ◽  
Film Cooling Holes

In the present work, multi-objective shape optimization of a row of laidback fan shaped film cooling holes has been performed using a hybrid multi-objective evolutionary approach in order to achieve an acceptable compromise between two competing objectives, i.e., enhancement of the film cooling effectiveness and reduction of the aerodynamic loss. In order to perform comprehensive optimization of film-cooling hole shape, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole and the pitch to hole diameter ratio, are chosen as design variables. Forty experimental designs within design spaces are selected by Latin hypercube sampling method. The response surface approximation method is used to construct the surrogate with objective function values for the experimental designs calculated through Reynolds-averaged Navier-Stokes analysis. The shear stress transport turbulence model is used as a turbulence closure. The optimization results are processed by the Pareto-optimal method. The Pareto optimal solutions are obtained using a combination of the evolutionary algorithm NSGA-II and a local search method. The optimum designs are grouped by k-means clustering technique and the six optimal points selected in the Pareto optimal solutions are evaluated by numerical analysis. The different trends in the variations of the design variables for each blowing ratios were found, and the optimum designs show enhanced objective function values.

Cluster Analysis and Switching Control: A Post-Processing of Multi-Objective Optimal Designs

2016 ◽  
Author(s):  
Zhi-Chang Qin ◽  
Jian-Qiao Sun
Keyword(s):  
Cluster Analysis ◽  
Control Algorithm ◽  
Switching Control ◽  
Optimal Designs ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Post Processing ◽  
Multi Objective ◽  
Optimal Controllers

The multi-objective optimal control design usually generates hundreds or thousands of Pareto optimal solutions. How to assist an user to select an appropriate controller to implement is a post-processing issue. In this paper, we develop a method of cluster analysis of the Pareto optimal designs to discover the similarity of the optimal controllers. After we identify the clusters of optimal controllers, we then develop a switching strategy to select controls from different clusters to improve the performance. Numerical results show that the switching control algorithm is quite promising.

MULTIOBJECTIVE DESIGN OF EQUIVALENT ACCELERATED LIFE TESTING PLANS

2008 ◽  
Vol 15 (06) ◽  
pp. 515-538 ◽  
Author(s):  
HAITAO LIAO ◽  
ZHAOJUN LI
Keyword(s):  
Solution Set ◽  
Accelerated Life Test ◽  
Test Time ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Pareto Solutions ◽  
Nsga Ii ◽  
Accelerated Life ◽  
Multiobjective Design

This paper is focused on the multiobjective design of equivalent accelerated life test (ALT) plans. Equivalent ALT plans are expected to achieve the same statistical performance as a baseline ALT plan yet lead to other desired performance measures such as reduced test time and total cost. Before determining the desired multiobjective equivalent ALT plans, an efficient fast non-dominated sorting genetic algorithm (NSGA-II) is utilized to identify a set of Pareto optimal solutions. To handle a large number of Pareto optimal solutions, a self-organizing map (SOM) and data envelopment analysis (DEA) are sequentially applied to classify the Pareto solutions and reduce the size of the suggested solution set. This integrated approach allows for the tradeoff of information among the Pareto solutions and the reduction in the size of the solution set. It provides a useful tool for practitioners to make meaningful decisions in planning ALT experiments.

scholarly journals Optimization Of Multi-Module CrN/CrCN Coatings

2015 ◽  
Vol 60 (2) ◽  
pp. 1037-1043
Author(s):  
Ł. Szparaga ◽  
P. Bartosik ◽  
A. Gilewicz ◽  
J. Ratajski
Keyword(s):  
Optimization Procedure ◽  
Stress And Strain ◽  
Pareto Optimal ◽  
Mutual Distance ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Decision Criteria ◽  
Decision Variables ◽  
42Crmo4 Steel ◽  
The Stability

Abstract In the paper was proposed optimization procedure supporting the prototyping of the geometry of multi-module CrN/CrCN coatings, deposited on substrates from 42CrMo4 steel, in respect of mechanical properties. Adopted decision criteria were the functions of the state of internal stress and strain in the coating and substrate, caused by external mechanical loads. Using developed optimization procedure the set of optimal solutions (Pareto-optimal solutions) of coatings geometry parameters, due to the adopted decision criteria was obtained. For the purposes of analysis of obtained Pareto-optimal solutions, their mutual distance in the space of criteria and decision variables were calculated, which allowed to group solutions in the classes. Also analyzed the number of direct neighbors of Pareto-optimal solutions for the purposes of assessing the stability of solutions.

A DECOMPOSITION-COORDINATION METHOD FOR COMPLEX MULTI-OBJECTIVE SYSTEMS

2009 ◽  
Vol 26 (06) ◽  
pp. 735-757 ◽  
Author(s):  
F. MIGUEL ◽  
T. GÓMEZ ◽  
M. LUQUE ◽  
F. RUIZ ◽  
R. CABALLERO
Keyword(s):  
Information Exchange ◽  
Exchange Process ◽  
Efficient Solutions ◽  
Pareto Optimal ◽  
Relative Importance ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Conflicting Objectives ◽  
Upper Level ◽  
Problem Instances

The generation of Pareto optimal solutions for complex systems with multiple conflicting objectives can be easier if the problem can be decomposed and solved as a set of smaller coordinated subproblems. In this paper, a new decomposition-coordination method is proposed, where the global problem is partitioned into subsystems on the basis of the connection structure of the mathematical model, assigning a relative importance to each of them. In order to obtain Pareto optimal solutions for the global system, the aforementioned subproblems are coordinated taking into account their relative importance. The scheme that has been developed is an iterative one, and the global efficient solutions are found through a continuous information exchange process between the coordination level (upper level) and the subsystem level (lower level). Computational experiments on several randomly generated problem instances show that the suggested algorithm produces efficient solutions within reasonable computational times.

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