Abstract
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.
Highlights
The Segway robot is an electric, two-wheeled self-balancing human transporter with a computercontrolled gyroscopic stabilization and control system
The single-seat robot faces many challenges related to nonlinearity, high order variables, strong coupling and inherent unstable dynamics of the system
It is worth considering that by using controller weighting matrices Q(t) and R(t) which govern the behavior of the robot system states and control effort respectively, the optimal linear quadratic regulator (LQR) gain matrix K is computed based on the Matlab command ” lqr ”
Summary
The Segway robot is an electric, two-wheeled self-balancing human transporter with a computercontrolled gyroscopic stabilization and control system. The LQR controller is adopted to implement the control system used to balance the two-round human vehicle as it seeks basically a trade off between the best control performance and minimum power input [12, 13]. Using this controller approach includes many problems and disadvantages such as relying on the designer experience and skill and the trial and error procedure in the determine of the controller weighting matrices Q and R.
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