Nonlinear Control Technology Research For Micro Turbine Engine

Compared with the large aero-engine, the micro turbine engine(MTE) has the same operating principle and structure. Moreover, the control system analysis and experimentation study based on MTE can also improve the development efficiency of the control system for large aero-engine with lower cost and risk. This paper is mainly concentrated on the research of the nonlinear characteristic and nonlinear control technique of MTE.Firstly, The engine modeling study and nonlinear characteristic analysis were carried out. According to the C-language and the S-function interface specification in Matlab, the component-level nonlinear dynamic model of MTE was modified and compiled into S-function(“.MEX” document) by the compilation tools in Matlab, which laid the foundation for the simulation experiment of MTE in Matlab environment. The MTE linear models at different operating points were built via small disturbance method. Based on the prepared linear models, the nonlinear characteristic in the transient processes from idle to maximum power of MTE was analyzed. Meanwhile, the necessity of nonlinear controller design was demonstrated.Then, the improvement research of the traditional PID controller design was performed. The control performances at different operating points can be guaranteed by applying the parameter-varying PID control with off-line optimization. The nonlinear PID control algorithm based on the error and error changing rate not only achieved the on-line adjustment of controller parameters, but also acquired good control effect in a wide operating range. As the feedforward control quantity can provide a control action of the nonlinear steady compensation, the combination of nonlinear control and feedforward control can improve the tracking performance and avoid the excessive overshoot.Lastly, the research on fuzzy self-adaptive PID control using radial basis function(RBF) neural network to compute feedforward compensation was conducted. Samples of the steady state data of the micro turbine engine were used in the off-line training for the RBF neural network. Besides, the traditional PID controller and fuzzy self-adaptive PID controller were introduced into the control system respectively to carry out simulation test. Results show that, in contrast to the traditional PID controller, the RBF neural network feedforward PID controller acquires a better tracking performance. What’s more, fuzzy self-adaptive PID controller based on the RBF neural network feedforward control has the ability of controller parameters online adjustment and can improve the control quality of the system in a wide operating range, which can be used to overcome the control difficulty caused by the nonlinear characteristic of aero-engine effectively.