基于松弛约束Hammerstein非线性模型的增程式APU预测控制

针对增程式辅助动力单元（APU）工作点切换过程的转速控制，提出了一种基于Hammerstein非线性模型的预测控制策略。通过稀疏最小二乘支持向量机-自适应混沌粒子群优化（SLSSVM-ACPSO）算法辨识激励响应数据建立了发动机Hammerstein非线性模型，在模型预测控制求解最优控制序列时，采用松弛因子松弛约束边界，并设计了有效集（ASM）-ACPSO组合算法求解，在控制过程中应用了变预测时域策略。建立系统仿真模型，仿真结果显示:在热机点切换至低负荷点及低负荷点切换至中负荷点的过程中稳定时间分别为2.57 s和2.77 s，转速最大超调率分别为2%和1.6%，均优于两种对比策略；在中负荷点向高负荷点切换过程中，转速超调率较大，但控制过程转矩变化更平缓。仿真结果表明模型预测控制策略控制APU系统转速响应快、转速超调率小，发动机转矩超调量小，具有良好的动态控制效果。 

Predictive control of extended-range APU based on relaxed constrained Hammerstein nonlinear model

In view of the speed control of the working point switching process of the extended-range auxiliary power unit （APU），a predictive control strategy based on the Hammerstein nonlinear model was proposed.The excitation data were identified by sparse least squares support vector machine-adaptive chaotic particle swarm optimization （SLSSVM-ACPSO） algorithm，and the Hammerstein nonlinear model of engine was established.When solving the optimal control sequence in model predictive control，the relaxation factor was used to relax the constraint boundary，and the active set method （ASM）-ACPSO combination algorithm was used to obtain the solution.The variable predictive time domain strategy was applied in the control process.The system simulation model was established，and the simulation results showed that in the processes of switching from the warming-up point to low load point and from the low load point to the medium load point，the stabilization times were 2.57 s and 2.77 s respectively，and the speed overshoot rates were 2% and 1.6% respectively，which were better than the two comparison strategies.In the process of switching from medium load point to the high load point，the speed overshoot rate was relatively larger，but the change of torque was more smooth in the control process.The simulation result showed that the model predictive control strategy for APU system had fast speed response，small speed and torque overshoot rate，and exhibited good dynamic control effect.