In this study, a model predictive control (MPC)‐based fault tolerant control (FTC) design method for air‐breathing hypersonic vehicles (AHVs) is proposed. The non‐linear model of AHVs is recalled and the FTC problem is discussed. The problem is challenging because the relationship between flight dynamics and the control input is not so clear. For the purpose of non‐linear FTC, an optimisation‐based reference reconfiguration method is given. Through the reconfiguration, a new reference command, which can be realised by the faulty system, is constructed. Actuator saturation has been take into account when constructing the new reference command. An MPC‐based controller reconfiguration method is proposed with respect to the new command and system faults. Finally, simulations are given to show the effectiveness of the proposed control method.

This paper proposes a robust adaptive fuzzy tracking controller for a hypersonic flight vehicle (HFV) subject to both parametric uncertainties and unmodeled dynamics. First, the nonlinear model of HFV is input/ouput linearization and the internal dynamics of HFV are established. Second, the ideal internal dynamics (IID) are calculated and then an IID-based controller designing method is utilized. With considering parameter uncertainties and unmodeled dynamics, a full state tracking error system is built. Fuzzy logical system (FLS) is utilized to identify the nonlinear system with parameter uncertainty and unmodeled dynamics. An adaptive fuzzy controller is proposed to guarantee the stability of the whole system with parameter uncertainty and unmodeled dynamics. Comparison simulations show remarkable efficiency of the proposed method.

Condition-based maintenance for a degrading system has been attached great importance in reducing unexpected failures and enabling the safe operation of the system. In this paper, we consider a condition-based replacement problem with observed degradation signals for the determination of the optimal replacement time of the system. The observed degradation signals contain some health related information of the system so that the system’s health state may be more accurately estimated and the future degradation progression of the system can be predicted. Based on the predicted degradation state, the concerned replacement problem is formulated in the framework of the Markov decision process and then a new degradation-modeling framework based on maximum likelihood estimation is proposed to analyze the degradation signals and to predict the future degradation state of the system. To solve the proposed condition-based replacement decision problem, we analyze structural properties of the optimal replacement policy and an optimal monotonic control limit solution policy is developed for condition-based replacement. Finally, a case study is provided to illustrate the proposed optimal replacement method.

This paper proposes a fuzzy integral sliding mode control strategy for flexible air-breathing hypersonic vehicles with input nonlinearity. Based on the complex nonlinear dynamics in the considered vehicles, a control-oriented model, which can retain the dominant features of the higher-fidelity model, is adopted for the control design. First, the T-S fuzzy approach is utilized to model the nonlinear dynamic of flexible air-breathing hypersonic vehicles, and the input nonlinearity model, which comprises dead-zones, sector nonlinearities, and actuator saturation, is considered. Then, based on the constructed T-S fuzzy model, a novel fuzzy integral sliding mode control method is proposed. This method can eliminate the reaching phase of the traditional sliding mode control by designing a novel sliding surface. Moreover, by the parallel-distributed compensation scheme, a sufficient condition is established to guarantee the global robust stability of the sliding mode dynamics in the specified surface in the presence of unmatched uncertainties and disturbance. The reachability of the specified sliding surface is guaranteed by an adaptive sliding mode controller under input nonlinearity. Finally, simulation results are given to show the effectiveness of the proposed control methods.

The tracking control of a multi-input multioutput nonlinear nonminimum phase system in general form is discussed. This system is assumed to be suffering from parameter uncertainties and unmodeled dynamics, and the priori information of them is unknown. By considering both the exact model and uncertain model, the sliding mode-based learning controller is proposed. By designing an appropriate sliding surface and a learning controller, the stability of the closed-loop system is guaranteed for both the exact model and uncertain model. To overcome the disadvantage caused by parameter uncertainties and unmodeled dynamics, a fuzzy logical system is adopted here. A numerical simulation result carried on vertical takeoff and landing aircraft is taken as an example to validate the effectiveness of the presented controller.