Flight control for air-breathing hypersonic vehicles using linear quadratic regulator design based on stochastic robustness analysis

• Published in: Frontiers of information technology & electronic engineering Vol. 18; no. 7; pp. 882 - 897
• Main Authors: Cao, Lin, Tang, Shuo, Zhang, Dong
• Format: Journal Article
• Language: English
• Published: Hangzhou Zhejiang University Press 01.07.2017; Springer Nature B.V
• Subjects: Algorithms; Communications Engineering; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Design analysis; Electrical Engineering; Electronics and Microelectronics; Feedback linearization; Flight control; Hybrid systems; Hypersonic vehicles; Instrumentation; Iterative methods; Linear quadratic regulator; Networks; Optimization algorithms; Output feedback; Parameter robustness; Parameter uncertainty; Particle swarm optimization; Probability theory; Robust control; Linear-quadratic regulator (LQR); Particle swarm optimization (PSO); TP27; V24; Air-breathing hypersonic vehicles (AHVs); Improved hybrid PSO algorithm; Stochastic robustness analysis
• ISSN: 2095-9184; 2095-9230
• DOI: 10.1631/FITEE.1601363

The flight dynamics model of air-breathing hypersonic vehicles （AHVs） is highly nonlinear and multivariable cou- pling, and includes inertial uncertainties and external disturbances that require strong, robust, and high-accuracy controllers. In this paper, we propose a linear-quadratic regulator （LQR） design method based on stochastic robustness analysis for the longitudinal dynamics of AHVs. First, input/output feedback linearization is used to design LQRs. Second, subject to various system parameter uncertainties, system robustness is characterized by the probability of stability and desired performance. Then, the mapping rela- tionship between system robustness and LQR parameters is established. Particularly, to maximize system robustness, a novel hybrid particle swarm optimization algorithm is proposed to search for the optimal LQR parameters. During the search iteration, a Chernoff bound algorithm is applied to determine the finite sample size of Monte Carlo evaluation with the given prohabilily levels. Finally, simulation results show that the optimization algorithm can effectively find the optimal solution to the LQR parameters.