Robust Predictive Tracking Control for Mobile Robots With Intermittent Measurement and Quantization

The vision sensors are usually used to obtain the posture of mobile robots for trajectory tracking. However, jittering and external light will cause the anomaly of image feature extraction of vision sensors, which can be modeled as intermittent measurement. In this article, the robust predictive tracking control problem is studied for network mobile robots with intermittent measurement and quantization. First, the tracking error model of trajectory tracking is established by taking into account of image feature extraction anomaly and quantization. Based on the prediction model, the optimization problem with a random variable, an uncertain parameter, and a terminal penalty function in the performance index is presented. By using the "min-max" least-squares method, a parameter-dependent predictive controller is designed if the optimization problem is feasible, and the mean-square asymptotic stability of the system is analyzed. The terminal weighted matrix is designed offline by applying linear matrix inequality technique, and the controller is designed online by using an iterative algorithm. Finally, the effectiveness of the proposed method is verified by numerical and experimental examples.