A probabilistic robust mixed H₂/H_∞ fuzzy control method for hypersonic vehicles based on reliability theory

Abstract

Achieving balance between robustness and performance is always a challenge in the hypersonic vehicle flight control design. In this research, we focus on dealing with uncertainties of the fuzzy control system from the viewpoint of reliability. A probabilistic robust mixed H 2 =H 1 fuzzy control method for hypersonic vehicles is presented by describing the uncertain parameters as random variables. First, a Takagi-Sugeno fuzzy model is employed for the hypersonic vehicle nonlinear dynamics characteristics. Next, a robust fuzzy controller is developed by solving a reliability-based multiobjective linear matrix inequality optimization problem, in which the H 2 =H 1 performance is optimized under the condition that the system is robustly reliable to uncertainties. By this method, the system performance and reliability can be taken into account simultaneously, which reduces the conservatism in the robust fuzzy control design. Finally, simulation results of a hypersonic vehicle demonstrate the feasibility and effectiveness of the presented method.

A new adaptive fuzzy hybrid unscented Kalman/H‐infinity filter for state estimating dynamical systems

“…Therefore, Simon et al [4] presented two different methods to combine two filters, initially, using the steady gain linear combination of two filters and then extracting the gain of the hybrid filter from a Riccati equation [4]. Among other methods, optimization methods were used to combine the two objective functions of these filters [5][6][7]. Employing the linear combination of gains, the prior state estimation, and the magnitude of the two UKF and UH∞F filters, Tehrani et al [8] presented a dual filter that exhibited higher robustness relative to the UKF and UH∞F filters in both two Gaussian and non-Gaussian noise states as well as optimization ability.…”

Stochastic precision analysis of hypersonic flight vehicle attitude control system in the presence of uncertainties

“…One of the most important evaluation methods is the Monte Carlo method (Rubinstein & Kroese, 2016), which analyses the stochastic performance of uncertain systems through a large number of simulation experiments. When the number of simulations is large enough, the statistical results obtained are very reliable (Yin et al, 2018). In (Yin et al, 2018), 100,000 times Monte Carlo simulations are used to estimate the probability after the fuzzy controller for hypersonic vehicle is obtained.…”

“…When the number of simulations is large enough, the statistical results obtained are very reliable (Yin et al, 2018). In (Yin et al, 2018), 100,000 times Monte Carlo simulations are used to estimate the probability after the fuzzy controller for hypersonic vehicle is obtained. In (Rehman et al, 2012), a Monte-Carlo type simulation has been performed for randomly generated values of the uncertain parameters of an air-breathing hypersonic flight vehicle.…”