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Designing a robust aircraft control system used to achieve a good tracking performance and stable dynamic behavior against working disturbances problem has attracted attention of control engineers. In this paper, a pitch angle control system for aircraft is designed utilizing liner quadratic Gaussian (LQG) optimal controller technique with a numerical tuning algorithm method in the longitudinal plane through cruising stage. Main design approach of LQG controller includes obtaining best weighting matrices values using trial and error method that consumes effort and takes more time, in addition, there is no guarantees to obtain optimum values for weighting matrices elements. In this research, genetic algorithm (GA) is used to optimize the state and control weighting matrices and determine best values for their elements. The proposed traditional and optimized LQG pitch controller schemes are implemented utilizing Matlab simulation tool and their performance are presented and compared based on transient and steady state performance parameters. The simulation results reveal the ability of the optimized GA_LQG controller to reject the effect of the noises in the aircraft system dynamic and achieve a good and stable tracking performance compared with that of the conventional LQG pitch control system. [ABSTRACT FROM AUTHOR]

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency. [ABSTRACT FROM AUTHOR]

This research work introduces an efficient hybrid control methodology through combining the traditional proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) optimal controlher. The proposed hybrid control approach is adopted to design three degree of freedom (3DOF) stabilizing system for helicopter. The gain parameters of the classic PID controller are determined using the elements of the LQR feedback gain matrix. The dynamic behaviour of the LQR based PID controller, is modeled in state space form to enable utlizing state feedback controller technique. The performance of the proposed LQR based LQR controller is improved by using Genetic Algorithm optimization method which are adopted to obtain optimum values for LQR controller gain parameters. The LQR-PID hybrid controller is simulated using Matlab environment and its performance is evaluated based on rise time, settling time, overshoot and steady state error parameters to validate the proposed 3DOF helicopter balancing system. Based on GA tuning approach, the simulation results suggest that the hybrid LQR-PID controller can be effectively employed to stabilize the 3DOF helicopter system. [ABSTRACT FROM AUTHOR]

Copyright of Al-Rafadain Engineering Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Linear Quadratic Regulator (LQR) is one of the most interesting control techniques adopted as a control strategy in state feedback. These types of techniques achieve good results but suffer from the problem of trial and error involved in the computation of weight matrices. The trial and error technique leads to hard tuning of the LQR controller parameters. This of course will lead to difficulty in reaching the optimal system performance. The paper attempts to solve the above difficulty via the selection of the LQR weight matrices using Genetic Algorithm GA. This proposed solution will avoid the trail and error involved in the state feedback technique. The proposed solution has been adopted in the design of position controller of a robot arm and the results of computer simulation have shown that the proposed solution fulfill specifications, for minimum overshoot , settling and rising times. [ABSTRACT FROM AUTHOR]

This work demonstrates the application of Artificial Immunized System (AIS) in designing on-line the parameters of the Linear Quadratic Regulator (LQR) for SISO plant. The AIS is used in creating the adaptive critics which represent the matrix parameters in the solution of the Riccati equation of the LQR. It is found that the AIS possess promising features to deal on-line with the plant parameter variations and reflect acceptable regulating and tracking characteristics. [ABSTRACT FROM AUTHOR]