摘要
由于压燃式航空活塞发动机工作过程具有强非线性等特点,只采用模型预测控制(MPC)算法来实现压燃式航空活塞发动机的转矩控制,会导致基于状态空间模型的转矩预测精度不理想。采用径向基(RBF)神经网络结合MPC的发动机转矩复合预测控制能解决上述问题。首先,通过脉谱(MAP)控制方式获得发动机的运行数据,以此作为自行搭建的发动机联合仿真平台上的神经网络训练样本集。其次,在粒子群优化(PSO)算法中引入模拟退火(SA)机制,训练RBF神经网络转矩预测模型。最后,通过联合仿真不断修正控制算法,验证了SA+PSO算法在RBF神经网络上训练发动机转矩预测模型的优越性,还能有效改善PSO算法容易陷入局部最优的问题,并通过发动机台架实验验证了转矩复合预测控制的有效性。
Due to the strong nonlinearity of the working process of compression-ignition aero piston engine, only using model predictive control(MPC) algorithm to realize the torque control of compression ignition aero-piston engine would lead to the unsatisfactory accuracy of torque prediction based on state space model. The above problems could be solved by the compound predictive control of engine torque based on radial basis function(RBF) neural-network and MPC. Firstly, the engine operation data obtained by the MAP control method were used as the neural network training sample-set on the self-built engine joint simulation platform. Secondly, the simulated annealing(SA) mechanism introduced into particle swarm optimization(PSO) algorithm was to train the RBF neural network torque prediction model. Finally, through joint simulation, the control algorithm was continuously modified to verify the superiority of SA+PSO algorithm in training engine torque prediction model on RBF neural network. Also it could effectively improve the problem that PSO algorithm was easy to fall into local optimization. The effectiveness of torque compound predictive control was verified by the engine bench experiment.
作者
叶桐
黄国勇
徐劲松
Ye Tong;Huang Guoyong;Xu Jinsong(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Faculty of Civil Aviation and Aeronautics,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量技术》
北大核心
2022年第9期56-67,共12页
Electronic Measurement Technology
基金
国家自然科学基金(61863017)项目资助。
关键词
压燃式航空活塞发动机
转矩复合预测控制
RBF神经网络
模拟退火粒子群优化算法
compression-ignition aero piston engine
compound predictive control of torque
RBF neural network
simulated annealing(SA)and particle swarm optimization(PSO)algorithm
作者简介
叶桐,硕士研究生,主要研究方向为压燃式航空活塞发动机的控制。E-mail:824508846@qq.com;黄国勇,工学博士,教授,硕士生导师,主要研究方向为航空发动机的控制与故障诊断。E-mail:42427566@qq.com;通信作者:徐劲松,工学博士,教授,硕士生导师,主要研究方向为航空发动机的燃烧与控制。E-mail:372606249@qq.com。
