基于非合作模型预测控制的人机共驾策略

提出一种基于非合作模型预测控制(model predictive control, MPC)的智能汽车人机共驾策略.首先,建立了驾驶员和控制系统两者共同控制车辆的人机共驾系统模型.接着,得到了驾驶员和控制系统的代价函数.然后,求解了非合作MPC人机共驾策略的纳什均衡解.最后,通过仿真验证了非合作MPC人机共驾策略的优点和有效性.证明了非合作MPC的纳什均衡解可以通过非迭代的方法求解,并通过驾驶员和控制系统置信度矩阵的更新实现了驾驶权的逐渐交接.Matlab仿真表明,非合作MPC人机共驾策略可以在智能车辆遇到危险时将驾驶权逐渐从驾驶员转交给控制系统,同时保证驾驶员实时在环.

Cooperative Driving Strategy Based on Non-cooperative Model Predictive Control

An intelligent vehicle cooperative driving strategy based on non-cooperative model predictive control (MPC) was proposed. Firstly, the cooperative driving model was presented, in which the shared control of the vehicle was realized. Next, the cost functions of the driver and the control system were obtained. Then, the Nash equilibrium solution of the non-cooperative MPC was achieved. At last, simulations were used to verify the advantages and effectiveness of the strategy. It is shown that the Nash equilibrium solution of the non-cooperative MPC can be achieved by a non-iterative method. And gradual handover of the driving privilege is realized by using the updated confidence matrixes of the driver and the control system. Simulations based on Matlab show that the non cooperative MPC cooperative driving strategy can deliver the driving privilege from the driver to the control system gradually when the intelligent vehicle encounters danger. And this strategy can guarantee that the driver is in the control loop all the time.