On the issue of low utilization and acceptance of commercial adaptive cruise control(ACC), a multi-objective adaptive cruise control(MO-ACC)algorithm is developed in this paper. Based on model predictive control(MPC)theory, comprehensively considering the coordination among various conflicting objectives such as tracking capability, fuel economy, ride comfort and rear-end safety, the decision of desired longitudinal acceleration is transformed into online quadratic programming(QP)problem which could be formulated as a quadratic cost function with linear multi-constraints. In order to compensate for prediction error caused by modeling mismatch, the robustness of control system is improved by introducing an error feedback correction mechanism. Meanwhile, vector management method is adopted to deal with the non-feasible solution owing to hard constraints during the process of optimization. Further, under different kinds of traffic scenarios, the focusing performance index along with constraint space varies, and therefore different ACC modes are established to meet the demand of skilled driving groups by means of slightly adjusting performance index, constraint space as well as slack relaxation. The simulations show that under multiple traffic scenarios of preceding car, the following car can realize seamless switching among various working modes, and also is able to achieve the good expectation during car-following, which will help to enhance the adaptability of the ACC system against the complex road traffic environment. © 2018, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
