Sensor placement and moving horizon state/parameter estimation for flexible hypersonic vehicles

Abstract

Considering the nonlinearity, uncertainty, and rigid/elastic coupling, a state/parameter joint estimation method is essential for control system design or fault diagnosis of flexible hypersonic vehicles. With the goal of improving state/parameter estimation accuracy, this paper proposes a sensor placement strategy and a moving horizon estimation algorithm with a QR-decomposition-based arrival cost update strategy (MHE-QR). To enhance observability, a novel double sensor placement strategy, in which the sensor positions are obtained via solving a constrained nonlinear optimization problem, is developed. The MHE-QR algorithm transforms the arrival cost update problem into a least square problem and solves it utilizing QR decomposition. With this QR-decomposition-based arrival update strategy, the state/parameter estimation problem is solved as a nonlinear programming problem in the framework of moving horizon estimation. Finally, the performance of sensor placement strategy and MHE-QR is evaluated by Monte Carlo simulations in 10 different scenarios. Simulation results demonstrate that the sensor placement strategy and MHE-QR algorithm can effectively improve the estimation accuracy, convergence speed and computation rate. Additionally, the CPU time of MHE-QR validates its real-time applicability.

Moving-horizon-estimator-integrated adaptive hierarchical sliding mode control for flexible hypersonic vehicles considering aeroservoelastic effect

“…Remark 10. Although Chen et al 36 have proved that the moving horizon estimator is stable, which is also demonstrated by the simulation results, the estimation error still exists. It is important that the estimation result of SMDO and the stability of control system are both affected by the estimation error of moving horizon estimator.…”

“…Since the significant flexible effects are caused by the longitudinal bending of fuselage, 35 the structure of FHV is modeled as a freebeam. Under the assumption that the earth is flat, the longitudinal dynamics of FHV whose parameters are given in Chen et al 36 are as follows 14,37…”

“…To solve this sensor placement problem, Popov-Belevitch-Hautus (PBH) rank test is utilized to optimize a dual sensor placement scheme of gyroscopes. 36,38 For two gyroscopes whose positions are x m;1 and x m;2 respectively, the measured pitch rate x m;1 and x m;2 are processed as follows…”

“…Based on dynamic model (1) and measurement model ( 6), we can obtain the optimal dual sensor placement scheme (x m;1 ¼0:1 m; x m;2 ¼ 3:15 m) with the parameters and optimization methods given in Chen et al 36…”

“…L . According to equation ( 26) and the principle of dynamic programming, 36,40 the arrival cost should be…”