In this study, a theoretical framework for reconfigurable flight control is developed and applied to near space vehicle (NSV) attitude dynamics. First, NSV reentry mode is described. Second, an adaptive neural network observer is proposed, which ensures asymptotic convergence of the state observer error to zero under control effector damage and uncertainty. Next, a reconfigurable command-filter backstepping controller is designed based on the adaptive neural network observer. The authors focus is on the accommodation of the control effector damage, uncertainty and resulting disturbances. It is shown that the presented new control design results in asymptotic convergence of the attitude tracking error to zero. Finally, simulation results are given to demonstrate the effectiveness and potential of the proposed fault tolerant control scheme.

This study studies the fault-tolerant control (FTC) design based on the Takagi-Sugeno (T-S) fuzzy system models and terminal-sliding-mode control (TSMC). This hybrid scheme can keep the advantages of both methods. By using the T-S fuzzy models to approximate the original non-linear system, the online computation burden can be alleviated since most of the T-S parameters can be offline computed. Moreover, TSMC not only owns the merits, including robustness to uncertainties and/or disturbances, fast response and easy implementation, but also performs better than conventional sliding-mode control (SMC) since the system states of TSMC will converge in finite time to the control objective point, that is, equivalent point, after the system states intersect sliding surface. Both of the active and passive FTC design schemes are presented. The proposed analytical results are also applied to the FTC for the attitude stabilisation of a spacecraft. Simulation results demonstrate the benefits of the proposed scheme.

This study investigates the speed trajectory tracking problem of high-speed trains with actuator failures and unknown speed delays as well as control input saturations. New adaptive iterative learning fault-tolerant control (AILFTC) strategy is derived without the need for precise system parameters or analytically estimating bound on actuator failures variables. It is shown that with the proposed method, both actuator failures can be accommodated and the unknown time-varying speed delays and control input saturations can be analysed by means of Lyapunov–Krasovskii function. As such, the resultant control algorithms are able to achieve the L _[0,T] ² convergence of the train speed to desired profile during operations repeatedly in the presence of non-linearities and parametric uncertainties, as validated by the theoretical analysis and numerical simulations.

This study deals with the problem of finite-time reliable ℒ₂ − ℒ_∞/ℋ_∞ control for non-linear systems with actuator faults through Takagi–Sugeno fuzzy model approach. The actuator failure model under consideration is assumed to be governed by a homogenous Markov chain. The focus is on the design of a fuzzy Markov switching fault-tolerant controller such that the resulting closed-loop system is stochastically finite-time bounded with a mixed ℒ₂ − ℒ_∞/ℋ_∞ performance level over a finite-time interval. Some sufficient conditions for the solvability of the above problem are given in terms of linear matrix inequalities by introducing a new mixed ℒ₂ − ℒ_∞/ℋ_∞ performance function. Finally, a quarter-vehicle suspension model is employed to demonstrate the effectiveness of our proposed approach.

In this study, the problem of fault detection for discrete systems is considered. For control inputs, unknown bounded disturbances and sensor faults, an observer-based fault detection filter is designed based on a descriptor system method such that the error dynamic system is convergent, the effect of the disturbances on the residuals satisfies the H _∞ performance index and the effect of the faults on the residuals satisfies the H ₋ performance index. By a Lyapunov functional approach, a sufficient condition for the solvability of the fault detection problem is expressed in terms of linear matrix inequalities. A numerical example shows the effectiveness of the proposed method.

This study presents a passive fault-tolerant (FT) controller to preserve stability of a class of over-actuated non-linear systems in spite of actuator faults. In order to deal with different types of actuator faults, a generalised fault model is adopted such that the FT controller may have the advantage of being able to deal with different types of actuator faults. By grouping the control efforts which have similar effects on the system together, the original over-actuated system can be transferred into a square system, which makes the proposed FT controller has no requirement of the control effort distribution ratios for the actual control effects. The disturbances and uncertainties caused by the actuator faults are attenuated by an FT controller which is designed based on the linear-parameter varying method. The eigenvalue positions of the closed-loop system are constrained into a disk to obtain better transient responses. The proposed FT control method is applied to control a four wheel independently-actuated electric ground vehicle. Experimental results show the effectiveness of the proposed FT control approach.

This work focuses on modelling and fault tolerant algorithm design of a damaged near space vehicle. A novel radial basis function (RBF) neural network backstepping fault-tolerant control methodology is developed for attitude control system with damage, which can make system stable and accurately track the desired signals in the presence of vertical tail loss. First, the changing aerodynamic parameters caused by vertical tail loss are used to model damaged attitude dynamics. Second, in view of the established damaged model, a nominal backstepping controller is designed. Then a RBF neural network technology is designed to update the related parameters, which can compensate effects caused by damage and make the system still stable and track the desired signals. Finally, simulation results are provided to show the effectiveness of the proposed RBF neural network backstepping fault-tolerant control scheme.

This study deals with the design of a robust fault estimation and fault-tolerant control for vehicle lateral dynamics subject to external disturbance and unknown sensor faults. Firstly, a descriptor state and fault observer is designed to achieve the system state and sensor fault estimates simultaneously. Secondly, based on the information of on-line fault estimates, a robust fault-tolerant controller based on static output-feedback controller (SOFC) design approach is developed. To provide linear matrix inequalities of less conservatism, the results are conducted in the non-quadratic framework dealing with unmeasurable premise variables case. Simulation results show the effectiveness of the proposed control approach when the vehicle road adhesion conditions change and the sideslip angle is unavailable for measurement.

This study investigates the problem of output-feedback H _∞ control for Takagi–Sugeno fuzzy systems with input time-varying delay. Firstly, a new type of dynamic output-feedback controller is constructed for fuzzy systems with input time-varying delay. Secondly, by using Lyapunov stability theory, a condition with H _∞ performance is developed. Based on the condition, a desired dynamic output-feedback H _∞ controller is designed to guarantee that the closed-loop system is asymptotically stable with H _∞ disturbance attenuation. The existence condition of the controller can be obtained in terms of linear matrix inequalities, which can be solved by the standard software. The difference between the results proposed in the study and other existing ones is that a new dynamic output-feedback controller can be designed for fuzzy systems with input delay. Finally, a numerical example is provided to show the effectiveness of the proposed results.

Active suspension systems have received increased importance for improving automotive safety and comfort. In active suspensions, actuators are placed between the car body and wheel-axle, and are able to both add and dissipate energy from the system, which enables the suspension to control the attitude of the vehicle, to reduce the effects of the vibrations, and then to increase ride comfort and vehicle road handling. However, the attained benefits are paralleled with the increasing possibility of component failures. In this study, a fault-tolerant control approach is proposed to deal with the problem of fault accommodation for unknown actuator failures of active suspension systems, where an adaptive robust controller is designed to adapt and compensate the parameter uncertainties, external disturbances and uncertain non-linearities generated by the system itself and actuator failures. Comparative simulation studies are then given to illustrate the effectiveness of the proposed controllers.

This study is concerned with the generalised ℋ₂ fault detection problem for a class of discrete-time switched systems with repeated scalar non-linearities. To reduce the overdesign of the quadratic framework, this study proposes a switching-sequence-dependent Lyapunov function approach to the fault detection filter design procedure. Sufficient conditions are obtained for the existence of admissible generalised ℋ₂ fault detection filter. Since these conditions involve matrix equalities, the cone complementarity linearisation procedure is employed to cast the non-convex feasibility problem into a sequential minimisation problem subject to linear matrix inequalities, which can be readily solved by using the standard numerical software. If these conditions are feasible, a desired fault detection filter can be easily constructed. Finally, a numerical example is given to illustrate the effectiveness of the proposed theory.

This paper investigates the problem of leader-following consensus of single-integrator agents subject to saturation constraints. Global leader-following consensus protocol is developed with proper choice of the relative coupling gain. Both the case of input saturation and the case of actuator saturation are considered. It is shown that global consensus of the multiagent system can be reached under the general undirected graph provided that its augmented graph contains a directed spanning tree. Numerical examples are provided to demonstrate the theoretical results.