Electronic braking system (EBS) of commercial vehicle is developed based on Anti-lock Braking System (ABS), for the purpose of enhancing the braking performance. Based on the previous study, this paper aims at the development and research on the control strategy of advanced electronic braking system for commercial vehicle, which mainly includes braking force distribution and multiple targets control strategy. In the study of braking force distribution control strategy, the mass of vehicle and the axle loads will be calculated dynamically and the braking force of each wheel will be distributed regarding to the axle loads. The braking intention recognition takes the brake pad wear into account when braking uncritically, so it can detect a difference in the pads between the front and the rear axles. The brake assist strategy supports the driver during emergency braking and the braking distance is shortened by the reduction of the braking system response time. In the multiple targets stability control algorithm, a simplified vehicle model, a Kalman filter estimator and an Adaptive Kalman filter estimator of heavy duty vehicles are built, by which the parameters and states can be estimated successfully. A modified Time To Rollover (TTR) warning algorithm based on model forecast is presented. This algorithm can forecast the rollover, shimmy and enfoldment accidents, and give a firm foundation on multiple targets control. The stability control strategy based on Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) allows unknown disturbances exist during running, enhances the system robustness and the anti-noise performance, and is effective in multiple conditions.In this paper, the hardware-in-loop simulation test rigs is established for the development, experiment, calibration and evaluation of the vehicle dynamic models and simulation systems used in the advanced electronic braking system. The results of the hardware-in-loop simulation tests show that the control strategy in different conditions can achieve the expected results.