Characteristic model-based robust predictive control for reentry hypersonic vehicle with constraints
| Autor: | Tairen Sun, Yahui Li, Shengzhen Zhu, Jun Zhang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Engineering business.industry Linear matrix inequality Feed forward System identification 020207 software engineering 02 engineering and technology Reentry Model predictive control Nonlinear system 020901 industrial engineering & automation Control theory Robustness (computer science) Control system 0202 electrical engineering electronic engineering information engineering business |
| Zdroj: | 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). |
| DOI: | 10.1109/cgncc.2016.7828860 |
| Popis: | Hypersonic vehicle reentry dynamics exists strong coupling, nonlinear, multiple constraints features. Based on the low order equivalence of characteristics model, the reentry dynamics is modeled by linear time-varying model, and the three-channel coupling and disturbance is considered as a generalized composite disturbance. Based on the characteristic model, the robust predictive controller is designed to improve the robustness of control system, and to ensure that meet the state and input constraints. In this paper, the main work includes three parts: Firstly, the accumulative sum idea in the grey theory is introduced into the model identification algorithm, which weakens the effect again into the random noise factor and strengthens the regularity of identification data, the time-varying parameters and the disturbance are on-line estimated based on grey identification. Secondly, the robust predictive control law is proposed by LMI (linear matrix inequality) receding horizon optimization technique, and the compensator of grey identification is feedforward compensated, that ensures the stability of the closed-loop system, and satisfies the input and output constraints. At last, the validity of the algorithm is verified through the simulation results. |
| Databáze: | OpenAIRE |
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