Intelligent Control of Dynamic Target Tracking of a Car-Like Wheeled Robot in a Sensor-Network Environment

In this paper, the dynamic target tracking of a car-like wheeled robot within a sensor-network environment by an intelligent control is developed. The proposed intelligent control is called fuzzy decentralized sliding-mode grey prediction control (FDSMGPC). For implementing dynamic target tracking, two distributed CCD (charge-coupled device) cameras are set up to capture the poses of the tracking and target cars, which have the front-wheel for the steering orientation and the rear-wheel for the translation motion. Based on the control authority of these two CCD cameras, a suitable reference command for the proposed controller of the tracking car is planned on a personal computer and then transmitted to the tracking car by a wireless device. The reference command contains the reference steering angle of the front-wheel and the reference velocity of the rear-wheel. Only the information of the upper bound of system knowledge is required to select the suitable scaling factors and the coefficients of sliding surface for the proposed controller. Since the target car is dynamic and the tracking car possesses dynamics, a grey prediction for the pose of the target car is employed to plan an effective reference command. Finally, a sequence of experiments confirms the usefulness of the proposed