本文提出了一种基于主动学习的增强模型预测控制方法.该方案克服了大多数基于学习的方法的缺点,即只能被动地利用可获得的系统数据并导致学习缓慢.首先应用高斯过程来评估残差模型的不确定性并构建多步预测模型.然后提出了一个两阶段主动学习策略,通过在优化问题中引入信息增益作为对偶目标来激励系统探测.最后,基于鲁棒不变集定义了安全控制输入集保证了状态约束满足与系统安全性.本文提出的方法在保证系统安全的情况下提高了学习能力和闭环控制性能,实验说明了本文方案的优越性.“,”This paper proposes an active learning-based MPC scheme that overcomes the shortcomings of most learning-based methods which passively leverage the available system data and result in slow learning. We first apply Gaussian process regression to assess the residual model uncertainty and construct multi-step predictive model. Then we propose a two-step active learning strategy and reward the system probing by introducing information gain as dual objective in the optimization problem. Finally, the safe control input set is defined based on robust admissible input set to robustly guarantee state constraint satisfaction. The proposed method improves the learning ability and closed-loop performance with safety guarantees. The advantages of our proposed active learning-based MPC scheme are illustrated in the experiments.