scholarly journals Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Andrej Sarjaš ◽  
Rajko Svečko ◽  
Amor Chowdhury
Keyword(s):  
Genetic Algorithm ◽  
Controller Design ◽  
Optimization Procedure ◽  
Pole Placement ◽  
Objective Functions ◽  
Motion Controller ◽  
Multiobjective Genetic Algorithm ◽  
And Performance ◽  
Regional Pole Placement ◽  
Arbitrary Selection

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers’ structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers’ stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution.

scholarly journals Pareto Design of Decoupled Sliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
M. J. Mahmoodabadi ◽  
A. Bagheri ◽  
N. Nariman-zadeh ◽  
A. Jamali ◽  
R. Abedzadeh Maafi
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Systems ◽  
Fourth Order ◽  
Inverted Pendulum ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Objective Functions ◽  
Software Environment ◽  
Multiobjective Genetic Algorithm ◽  
Sliding Mode Controllers

This paper presents Pareto design of decoupled sliding-mode controllers based on a multiobjective genetic algorithm for several fourth-order coupled nonlinear systems. In order to achieve an optimum controller, at first, the decoupled sliding mode controller is applied to stablize the fourth-order coupled nonlinear systems at the equilibrium point. Then, the multiobjective genetic algorithm is applied to search the optimal coefficients of the decoupled sliding-mode control to improve the performance of the control system. Considered objective functions are the angle and distance errors. Finally, the simulation results implemented in the MATLAB software environment are presented for the inverted pendulum, ball and beam, and seesaw systems to assure the effectiveness of this technique.

Mathematical Modeling and Multi-Objective Optimization of a Mini-Channel Heat Exchanger Via Genetic Algorithm

2013 ◽  
Vol 5 (3) ◽  
Author(s):  
Tommaso Selleri ◽  
Behzad Najafi ◽  
Fabio Rinaldi ◽  
Guido Colombo
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Procedure ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Objective Functions ◽  
Pressure Drops ◽  
Overall Heat Transfer Coefficient

In the present paper a mathematical model for a mini-channel heat exchanger is proposed. Multiobjective optimization using genetic algorithm is performed in the next step in order to obtain a set of geometrical design parameters, leading to minimum pressure drops and maximum overall heat transfer coefficient. Multiobjective optimization procedure provides a set of optimal solutions, called Pareto front, each of which is a trade-off between the objective functions and can be freely selected by the user according to the specifications of the project. A sensitivity analysis is also carried out to study the effects of different geometrical parameters on the considered functions. The whole system has been modeled based on advanced experimental correlations in matlab environment using a modular approach.

scholarly journals Fuzzy Descriptor System Modeling and Control of Lagrange Dynamics with Regional Pole-Placement Constraint

2004 ◽  
Vol 8 (4) ◽  
pp. 362-368
Author(s):  
Jin-Shig Kang ◽  
Keyword(s):  
Controller Design ◽  
System Modeling ◽  
Fuzzy Model ◽  
Pole Placement ◽  
Descriptor System ◽  
Linear Matrix ◽  
Simple Extension ◽  
Design Algorithm ◽  
Modeling And Control ◽  
Regional Pole Placement

We present a descriptor fuzzy model for Lagrange dynamics and a controller design algorithm based on state feedback pole placement. The fuzzy descriptor system (FDS) model is a simple extension of the original Takagi-Sugeno fuzzy model for which a new controller is designed based on the linear matrix inequality (LMI) theory. We show that LMI-based regional pole-placement design for the FDS is easily solved. Two examples explain the controller’s simplicity and easy design.

scholarly journals Performance Enhancement of the Micromixer by the Multiobjective Genetic Algorithm and Surrogate Model Based on a Navier–Stokes Analysis Using Trade-Off Objective Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shakhawat Hossain ◽  
Farzana Islam ◽  
Nass Toufik Tayeb ◽  
Muhammad Aslam ◽  
Jin-Hyuk Kim
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Performance Enhancement ◽  
Channel Width ◽  
Navier Stokes ◽  
Design Parameters ◽  
Design Domain ◽  
Objective Functions ◽  
Trade Off ◽  
Multiobjective Genetic Algorithm

Optimal structure of the micromixer with a two-layer serpentine crossing device was accomplished by a multiobjective genetic algorithm and surrogate modeling based on a Navier–Stokes analysis using the trade-off objective functions behavior. The optimization analysis was conducted with three design parameters, i.e., channel width to the pitch span ( w / P ) ratio, major channel width to the pitch span (H/P) ratio, and channel depth to the pitch span (d/P) ratio. Two objective functions (i.e., mixing index and pressure drop) with trade-off characteristics have been used to solve the multiobjective optimization problem. The design domain was predetermined by a parametric investigation; afterward, the Latin hypercube sampling method was employed to select the appropriate design points surrounded by the design domain. The numerical data of the thirty-two design points were used to create the surrogate model; among the different surrogate models, in this study, the Kriging metamodel has been used. The concave pareto-optimal curve signifies the trade-off characteristics linking the objective functions.

New results on restricted static output feedback H ∞ controller design with regional pole placement

2019 ◽  
Vol 13 (8) ◽  
pp. 1095-1104 ◽  
Author(s):  
Pradosh Ranjan Sahoo ◽  
Jitendra Kumar Goyal ◽  
Sandip Ghosh ◽  
Asim Kumar Naskar
Keyword(s):  
Output Feedback ◽  
Controller Design ◽  
Pole Placement ◽  
Static Output Feedback ◽  
Regional Pole Placement ◽  
Static Output

scholarly journals Optimization of Collocated/Noncollocated Sensors and Actuators along with Feedback Gain Using Hybrid Multiobjective Genetic Algorithm-Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Deepak Chhabra ◽  
Gian Bhushan ◽  
Pankaj Chandna
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Closed Loop ◽  
Damping Ratio ◽  
Optimal Number ◽  
Objective Functions ◽  
Sensors And Actuators ◽  
Trade Off ◽  
Multiobjective Genetic Algorithm ◽  
Artificial Neural

A multiobjective optimization procedure is proposed to deal with the optimal number and locations of collocated/noncollocated sensors and actuators and determination of LQR controller gain simultaneously using hybrid multiobjective genetic algorithm-artificial neural network (GA-ANN). Multiobjective optimization problem has been formulated using trade-off objective functions ensuring good observability/controllability of the structure while minimizing the spillover effect and maximizing closed loop average damping ratio. Artificial neural networks (ANNs) are used to train the input as varying numbers and placements of sensors and actuators and the outputs are taken as the three objective functions (i.e., controllability, observability, and closed loop average damping ratio), thus forming three ANN models. The trained mathematical models of ANN are fed into the multiobjective GA. The hybrid multiobjective GA-ANN maintains the trade-off among the three objective functions. The ANN3 model is used experimentally to provide the control inputs to the piezoactuators. It is shown that the proposed method is effective in ascertaining the optimal number and placement of actuators and sensors with consideration of controllability, observability, and spillover prevention such that the performance on dynamic responses is also satisfied. It is also observed that damping ratio obtained with hybrid multiobjective GA-ANN and found with ANN experimentally/online holds well in agreement.

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