Model predictive control (MPC) algorithm for tip-jet reaction drive systems

Model predictive control reaction drive

Kestner, Brian ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: Model predictive control reaction drive;   

Others  :  https://smartech.gatech.edu/bitstream/1853/31802/1/kestner_brian_k_200912_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Modern technologies coupled with advanced research have allowed model predictive control (MPC) to be applied to new and often experimental systems.The purpose of this research is to develop a model predictive control algorithm for tip-jet reaction drive system.This system's faster dynamics require an extremely short sampling rate, on the order of 20ms, and its slower dynamics require a longer prediction horizon.This coupled with the fact that the tip-jet reaction drive system has multiple control inputs makes the integration of an online MPC algorithm challenging.In order to apply a model predictive control to the system in question, an algorithm is proposed that combines multiplexed inputs and a feasible cooperative MPC algorithm.In the proposed algorithm, it is hypothesized that the computational burden will be reduced from approximately Hp(Nu + Nx)3 to pHp(Nx+1)3 while maintaining control performance similar to that of a centralized MPC algorithm.To capture the performance capability of the proposed controller, a comparison its performance to that of a multivariable proportional-integral (PI) controller and a centralized MPC is executed.The sensitivity of the proposed MPC to various design variables is also explored.In terms of bandwidth, interactions, and disturbance rejection, the proposed MPC was very similar to that of a centralized MPC or PI controller.Additionally in regards to sensitivity to modeling error, there is not a noticeable difference between the two MPC controllers.Although the constraints are handled adequately for the proposed controller, adjustments can be made in the design and sizing process to improve the constraint handling, so that it is more comparable to that of the centralized MPC.Given these observations, the hypothesis of the dissertation has been confirmed.The proposed MPC does in fact reduce computational burden while maintaining close to centralized MPC performance.