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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]

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]