Optimal feedback control for the identification of two-wheeled mobile robot

The ability to obtain the maximum amount of information from a system while achieving the desired control performance highly depends on the applied excitation signal. In this work, a novel strategy is proposed for the parameter identification and control of two-wheeled mobile robot. The proposed method combines the nonlinear system identification paradigm and model predictive control (MPC) in a single framework in order to design the optimal feedback control algorithm. The fundamental objective of optimal input signal is to minimize the cost related to the identification experiment and provide a system model which gives the desired control performance. In the proposed strategy, Extended Kalman Filter (EKF) or Unscented Kalman filter (UKF) are proposed to be used together with an MPC strategy to yield the optimal control signal. Based on the system performance and quality of the information obtained, a comparison study is presented. The effectiveness of the proposed strategy is demonstrated on a two-wheeled mobile robot.