AbstractIn this paper, we present a genetic algorithm based modular reconfigurable control strategy for an over-actuated ADMIRE nonlinear aircraft system. The control law was based on multi-input multi-output (MIMO) linear quadratic regulator (LQR) strategy to produce virtual command signals. A pseudo-inverse based allocation method was used for effective distribution of commands produced by controller to redundant control surfaces in normal and fault condition. Actuator position limits can be considered for reconfiguration of virtual command signals. Simulation results show that satisfactory and improved performance even in fault scenario can be achieved quickly by using natural evolution based optimization technique in modular control des...
This paper presents a novel bio-inspired adaptive control technique that has been designed to mainta...
Genetic algorithms (GAs) are parameter search techniques that rely on analogies to natural biologica...
This research presents multi-objective optimization for control allocation problem based on the Evol...
AbstractIn this paper, we present a genetic algorithm based modular reconfigurable control strategy ...
AbstractThis paper presents an optimized reconfigurable control design methodology by separating con...
The control augmentation systems are very important to keep the stability and manipulability in the ...
The aim of this work is to demonstrate the capabilities of evolutionary methods in the design of rob...
Design and optimization of the flight controllers is a demanding task which usually requires deep en...
The design of Flight Control Systems for tilt-rotor UAVs can be challenging for the management of th...
This paper describes a novel approach to the design of a control system for an aircraft gas turbine ...
This paper presents the development and testing of a novel fault tolerant adaptive control system ba...
This paper describes a stochastic approach for comprehensive diagnostics and validation of control s...
The use of an evolutionary algorithm in the framework of H∞ control theory is being considered as a ...
The placement of actuators on a wing determines the control effectiveness of the airplane. One appro...
Abstract: This paper describes a new procedure to design robust H ∞ controller with time domain spec...
This paper presents a novel bio-inspired adaptive control technique that has been designed to mainta...
Genetic algorithms (GAs) are parameter search techniques that rely on analogies to natural biologica...
This research presents multi-objective optimization for control allocation problem based on the Evol...
AbstractIn this paper, we present a genetic algorithm based modular reconfigurable control strategy ...
AbstractThis paper presents an optimized reconfigurable control design methodology by separating con...
The control augmentation systems are very important to keep the stability and manipulability in the ...
The aim of this work is to demonstrate the capabilities of evolutionary methods in the design of rob...
Design and optimization of the flight controllers is a demanding task which usually requires deep en...
The design of Flight Control Systems for tilt-rotor UAVs can be challenging for the management of th...
This paper describes a novel approach to the design of a control system for an aircraft gas turbine ...
This paper presents the development and testing of a novel fault tolerant adaptive control system ba...
This paper describes a stochastic approach for comprehensive diagnostics and validation of control s...
The use of an evolutionary algorithm in the framework of H∞ control theory is being considered as a ...
The placement of actuators on a wing determines the control effectiveness of the airplane. One appro...
Abstract: This paper describes a new procedure to design robust H ∞ controller with time domain spec...
This paper presents a novel bio-inspired adaptive control technique that has been designed to mainta...
Genetic algorithms (GAs) are parameter search techniques that rely on analogies to natural biologica...
This research presents multi-objective optimization for control allocation problem based on the Evol...