A novel fault-tolerant control strategy for near space hypersonic vehicles via least squares support vector machine and backstepping method

Near Space Hypersonic Vehicle (NSHV) could play significant roles in both military and civilian applications. It may cause huge losses of both personnel and property when a fatal fault occurs. It is therefore paramount to conduct fault-tolerant research for NSHV and avoid some catastrophic events. Toward this end, this paper presents a novel fault-tolerant control strategy by using the LSSVM (Least Squares Support Vector Machine)-based inverse system and Backstepping method. The control system takes advantage of the superiority of the LSSVM in solving the problems with small samples, high dimensions and local minima. The inverse system is built with an improved LSSVM. The adaptive controller is designed via the Backstepping which has the unique capability in dealing with nonlinear control systems. Finally, the experiment results demonstrate that the proposed method performs well

Adaptive fault-tolerant attitude tracking control for hypersonic vehicle with unknown inertial matrix and states constraints

This paper proposes an adaptive fault-tolerant control (FTC) method for hypersonic vehicle (HSV) with unexpected centroid shift, actuator fault, time-varying full state constraints, and input saturation. The occurrence of unexpected centroid shift has three main effects on the HSV system, which are system uncertainties, eccentric moments, and variation of input matrix. In order to ensure the time-varying state constraints, a novel attitude state constraint control strategy, to keep the safe flight of HSV, is technically proposed by a time-varying state constraint function (TVSCF). A unified controller is designed to handle the time-varying state constraints according to the proposed TVSCF. Then, the constrained HSV system can be transformed into a novel free-constrained system based on the TVSCF. For the variation of system input matrix, input saturation and actuator fault, a special Nussbaum-type function is designed to compensate for those time-varying nonlinear terms. Additionally, the auxiliary systems is designed to compensate the constraint of system control inputs. Then, it is proved that the proposed control scheme can guarantee the boundedness of all closed-loop signals based on the Lyapunov stability theory. At last, the simulation results are provided to demonstrate the effectiveness of the proposed fault-tolerant control scheme.</p

Six-DOF spacecraft optimal trajectory planning and real-time attitude control: a deep neural network-based approach

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time

Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

In this article, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNNs) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of the Lyapunov theory, we show that our proposed methods guarantee both finite-time convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples

NASA LaRC Workshop on Guidance, Navigation, Controls, and Dynamics for Atmospheric Flight, 1993

This publication is a collection of materials presented at a NASA workshop on guidance, navigation, controls, and dynamics (GNC&D) for atmospheric flight. The workshop was held at the NASA Langley Research Center on March 18-19, 1993. The workshop presentations describe the status of current research in the GNC&D area at Langley over a broad spectrum of research branches. The workshop was organized in eight sessions: overviews, general, controls, military aircraft, dynamics, guidance, systems, and a panel discussion. A highlight of the workshop was the panel discussion which addressed the following issue: 'Direction of guidance, navigation, and controls research to ensure U.S. competitiveness and leadership in aerospace technologies.