摘要
针对传统模型预测控制在工况突变、大幅值故障等大幅度模型失配情况下,难以保证控制精度的问题,提出了一种基于深度强化学习的航空发动机改进模型预测控制方法。首先对MPC对模型失配的调节效果进行评估,针对不同类型不同程度的模型失配对MPC的影响进行分析。其次提出基于深度强化学习的模型预测控制方法(DRL-MPC),通过深度强化学习对预测模型与实际系统之间的模型偏差进行修正,提升预测模型的预测精度。最后,对MPC的代价函数进行重构,建立基于深度强化学习的航空发动机改进MPC控制器。对传统MPC方法与所提方法进行了比较仿真。仿真结果表明,在存在大幅模型失配的情况下,所提方法能够在不提升计算代价的情况下有效降低MPC控制器对模型失配的敏感程度。
Aiming at the problem that the traditional model predictive control is difficult to ensure the control accuracy in the case of large model mismatch such as sudden change of operating conditions and large value faults,an improved aero-engine model predictive control method based on deep reinforcement learning is proposed.Firstly,the regulation effect of MPC on model mismatch is evaluated,and the impact of different types and degrees of model mismatch on MPC is analyzed.Secondly,a model predictive control method based on deep reinforcement learning(DRLMPC)is proposed.Through deep reinforcement learning,the model deviation between the prediction model and the actual system is corrected to improve the prediction accuracy of the prediction model.Finally,the cost function of MPC is reconstructed,and an aero-engine improved MPC controller based on deep reinforcement learning is established.The traditional MPC method is compared with the proposed method.The simulation results show that the proposed method can effectively reduce the sensitivity of the MPC controller to the model mismatch without increasing the computational cost in the case of large model mismatch.
作者
刘策
白杰
LIU Ce;BAI Jie(College of Aeronautical Engineering,Civil Aviation University of China,Tianjin 300300,China;Key Laboratory for Civil Airworthiness Certification Technology,Tianjin 300300,China)
出处
《计算机仿真》
2024年第7期21-25,490,共6页
Computer Simulation
