Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment

Authors:
João Viana Da Fonseca Neto;Ivanildo Silva Abreu;Fábio Nogueira Da Silva
Affiliations:
Department of Electrical Engineering, Electrical Engineering and Computation, Federal University of Maranhão, São Luís, MA, Brazil;Department of Mathematics and Computer Engineering, State University of Maranhão, São Luís, MA, Brazil and Electrical Engineering, Federal University of Pará, Belém, PA, B ...;Department of Electrical Engineering, Electrical Engineering and Computation, Federal University of Maranhão, São Luís, MA, Brazil
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2010

Citing 23
Cited 0

Parallel and distributed computation: numerical methods

Parallel and distributed computation: numerical methods
Computational methods for linear control systems

Computational methods for linear control systems
Performance predictions for parallel diagonal-implicitly iterated Runge-Kutta methods

PADS '95 Proceedings of the ninth workshop on Parallel and distributed simulation
Robust and optimal control

Robust and optimal control
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
A multilayer recurrent neural network for solving continous-time algebraic Riccati equations

Neural Networks
Computational Intelligence in Control Engineering

Computational Intelligence in Control Engineering
Computational Intelligence: An Introduction

Computational Intelligence: An Introduction
Multivariable Feedback Design

Multivariable Feedback Design
Parallel and Distributed Computation: Numerical Methods

Parallel and Distributed Computation: Numerical Methods
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications

Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications
Adaptive Control Systems

Adaptive Control Systems
Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy)

Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy)
Artificial neural network software for real-time estimation of olive oil qualitative parameters during continuous extraction

Computers and Electronics in Agriculture
Experimental evaluation of linear time-invariant models for feedback performance control in real-time systems

Real-Time Systems
Genetic tolerance fuzzy neural networks: From data to fuzzy hyperboxes

Neurocomputing
Computational complexity and evolutionary computation

Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms

Neurocomputing
Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network

Expert Systems with Applications: An International Journal
Quantifying and suppressing the measurement disturbance in feedback controlled real-time systems

Real-Time Systems
Analysis and comparison of aircraft landing control using recurrent neural networks and genetic algorithms approaches

Neurocomputing
Parallelism and evolutionary algorithms

IEEE Transactions on Evolutionary Computation
Statistical analysis of the parameters of a neuro-genetic algorithm

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance. The genetic search convergence evaluation is performed in terms of the fitness function statistics and the RNN convergence, which is evaluated by landscapes of the energy and norm, as a function of the parameter deviations. The control problem solution is evaluated in the time and frequency domains by the impulse response, singular values, and modal analysis.