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The purpose of this paper is to present a solution for the uncertain fault with the propulsion subsystem of satellite formation, using the Lur’e differential inclusion linear state observers (DILSOs) and fuzzy wavelet neural network (FWNN) to perform fault detection and diagnosis.

The uncertain fault system cannot be described based on the accurate differential equations. The set-value mapping is introduced into the state equations to solve the problem of uncertainty, but it will cause output uncertainty. The problem can be solved by linearization of Lur’e differential inclusion state observers. The Lur’e DILSOs can be used to detect uncertain fault. The fault isolation and estimation can be performed using the FWNN.

The mixed approach from fault detection and diagnosis has featured fast and correct to found the uncertain fault. The simulation results to indicate that the methods of design are not only effective but also have the advantages of good approximation effect, fast detection speed, relatively simple structure and prior knowledge and realization of adaptive learning.

The hybrid algorithm can be extensively applied to engineering practice and find uncertain faults of the propulsion subsystem of satellite formation promptly.

This paper provides a fast, effective and simple mixed fault detection and diagnosis scheme for satellite formation.

This paper aims to develop a novel trajectory optimization algorithm which is capable of producing high accuracy optimal solution with superior computational efficiency for the hypersonic entry problem.

A two-stage trajectory optimization framework is constructed by combining a convex-optimization-based algorithm and the pseudospectral-nonlinear programming (NLP) method. With a warm-start strategy, the initial-guess-sensitive issue of the general NLP method is significantly alleviated, and an accurate optimal solution can be obtained rapidly. Specifically, a successive convexification algorithm is developed, and it serves as an initial trajectory generator in the first stage. This algorithm is initial-guess-insensitive and efficient. However, approximation error would be brought by the convexification procedure as the hypersonic entry problem is highly nonlinear. Then, the classic pseudospectral-NLP solver is adopted in the second stage to obtain an accurate solution. Provided with high-quality initial guesses, the NLP solver would converge efficiently.

Numerical experiments show that the overall computation time of the two-stage algorithm is much less than that of the single pseudospectral-NLP algorithm; meanwhile, the solution accuracy is satisfactory.

Due to its high computational efficiency and solution accuracy, the algorithm developed in this paper provides an option for rapid trajectory designing, and it has the potential to evolve into an online algorithm.

The paper provides a novel strategy for rapid hypersonic entry trajectory optimization applications.

A novel vision-based relative navigation system (VBRNS) plays an important role in aeronautics and astronautics fields, and the filter is the core of VBRNS. However, most of the existing filtering algorithms used in VBRNS are derived based on Gaussian assumption and disregard the non-Gaussianity of VBRNS. Therefore, a novel robust filtering named as cubature Huber-based filtering (CHF) is proposed and applied to VBRNS to improve the navigation accuracy in non-Gaussian noise case.

Under the Bayesian filter framework, the third-degree cubature rule is used to compute the cubature points which are propagated through state equation, and then the predicted mean and the associated covariance are taken. A combined minimum l₁ and l₂-norm estimation method referred as Huber’s criterion is used to design the measurement update. After that, the vision-based relative navigation model is presented and the CHF is used to integrate the line-of-sight measurements from vision camera with inertial measurement of the follower to estimate the precise relative position, velocity and attitude between two unmanned aerial vehicles. During the design of relative navigation filter, the quaternions are used to represent the attitude and the generalized Rodrigues parameters are used to represent the attitude error. The simulation is conducted to demonstrate the effectiveness of the algorithm.

By this means, the VBRNS could perform better than traditional VBRNS whose filter is designed by Gaussian filtering algorithms. And the simulation results demonstrate that the CHF could exhibit robustness when the system is non-Gaussian. Moreover, the CHF has more accurate estimation and faster rate of convergence than extended Kalman Filtering (EKF) in face of inaccurate initial conditions.

A novel robust nonlinear filtering algorithm named as CHF is proposed and applied to VBRNS based on cubature Kalman filtering (CKF) and Huber’s technique. The CHF could adapt to the non-Gaussian system effectively and perform better than traditional Gaussian filtering such as EKF.

The purpose of this paper is to develop a distributed and integrated method to get a fast and feasible solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs).

In this study, the planning process is conducted in a distributed framework; the cooperative mission planning problem is reformulated with some specific constraints in the real mission; a distributed genetic algorithm is the algorithm proposed for searching for the optimal solution; genes of the chromosome are modified to adapt to the heterogeneous characteristic of UAVs; a fixed-wing UAV’s six degrees-of-freedom (DOF) model with a path following method is used to test the proposed mission planning method.

This method not only has the ability to obtain good feasible solutions but also improves the operating rate vastly.

This study is only applied to the case where the communication among UAVs is linked during the mission.

This study is expected to be practical for a real mission because of its fast operating rate and good feasible solution.

This solution is tested on a fixed-wing UAV’s 6-DOF model by a path following method, so it is believable from the perspective of an autonomous UAV guidance and control system.

The purpose of this paper is to develop a distributed task allocation method for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) based on the consensus algorithm and the online cooperative strategy.

In this paper, the allocation process is conducted in a distributed framework. The cooperative task allocation problem is proposed with constraints and uncertainties in a real mission. The algorithm based on the consensus algorithm and the online cooperative strategy is proposed for this problem. The local chain communication mode is adopted to restrict the bandwidth of the communication link among the UAVs, and two simulation tests are given to test the optimality and rapidity of the proposed algorithm.

This method can handle both continuous and discrete uncertainties in the mission space, and the proposed algorithm can obtain a feasible solution in allowable time.

This study is only applied to the case that the total number of the UAVs is less than 15.

This study is expected to be practical for a real mission with uncertain targets.

The proposed algorithm can go beyond previous works that only deal with continuous uncertainties, and the Bayesian theorem is adopted for estimation of the target.

The purpose of this paper is to present a novel guidance law that is able to control the impact time while the seeker’s field of view (FOV) is constrained.

The new guidance law is derived from the framework of Lyapunov stability theory to ensure interception at the desired impact time. A time-varying guidance gain scheme is proposed based on the analysis of the convergence time of impact time error, where finite-time stability theory is used. The circular trajectory assumption is adopted for the derivation of accurate analytical estimation of time-to-go. The seeker’s FOV constraint, along with missile acceleration constraint, is considered during guidance law design, and a switching strategy to satisfy it is designed.

The proposed guidance law can drive missile to intercept stationary target at the desired impact time, as well as satisfies seeker’s FOV and missile acceleration constraints during engagement. Simulation results show that the proposed guidance law could provide robustness against different engagement scenarios and autopilot lag.

The presented guidance law lays a foundation for using cooperative strategies, such as simultaneous attack.

This paper presents further study on the impact time control problem considering the seeker’s FOV constraint, which conforms better to reality.

This paper aims to investigate the problem of on-line orbit planning and guidance for an advanced upper stage.

The double impulse optimal transfer orbit is planned by the Lambert algorithm and the improved particle swarm optimization (IPSO) method, which can reduce the total velocity increment of the transfer orbit. More specially, a simplified formula is developed to obtain the working time of the main engine for two phases of flight based on the theorem of impulse. Subsequently, the true anomalies of the start position and the end position for both two phases are planned by the Newton iterative algorithm and the Kepler equation. Finally, the first phase of flight is guided by a novel iterative guidance (NIG) law based on the true anomaly update with respect to the geometrical relationship. Also, a completely analytical powered explicit guidance (APEG) law is presented to realize orbital injection for the second phase of flight.

Simulations including Monte Carlo and three typical orbit transfer missions are carried out to demonstrate the efficiency of the proposed scheme.

A novel on-line orbit planning algorithm is developed based on the Lambert problem, IPSO optimization method and Newton iterative algorithm. The NIG and APEG are presented to realize the designed transfer orbit for the first and second phases of flight. Both two guidance laws achieve higher orbit injection accuracies than traditional guidance laws.

This paper aims to propose Bayesian filtering based on solving the Fokker–Planck equation, to improve the accuracy of filtering in non-Gauss case. Nonlinear filtering plays an important role in many science and engineering fields for estimating the state of dynamic system, but the existing filtering algorithms are mainly used for solving the problem of Gauss system.

Under the Bayesian framework, the time update of this filtering is based on solving Fokker–Planck equation, while the measurement update uses the Bayes formula directly. Therefore, this novel algorithm can be applied to nonlinear, non-Gaussian estimation. To reduce the computational complexity due to standard meshing, an adaptive meshing algorithm proposed which includes the coarse meshing, significant domain determination that is generated using extended Kalman filtering and Chebyshev’s inequality theorem, and value assignment for significant domain. Simulations are conducted on a reentry body tracking problem to demonstrate the effectiveness of this novel algorithm.

In this way, finer grid points can be placed in the regions with high conditional probability density, while the grid points with low conditional probability density can be neglected. The simulation results indicate that the novel algorithm can reduce the computational burden significantly compared to the standard meshing, while achieving similar accuracy.

A novel Bayesian filtering based on solving the Fokker–Planck equation using adaptive meshing is proposed, and the simulations show that algorithm can reduce the computational burden significantly compared to the standard meshing, while achieving similar accuracy.

A novel nonlinear filtering based on solving the Fokker–Planck equation is proposed. The novel algorithm is suitable for non-Gauss system, and can achieve similar accuracy compared to the standard meshing with the significant reduction of computational burden.

The purpose of this paper is to present a high accuracy path following method for low-cost fixed-wing UAVs.

The original vector field (VF) algorithm is condensed. A spatial integration mechanism is added to the existing VF and nonlinear guidance law, aiming to decrease steady-state cross-track-error and cope with long-term disturbance.

Numerical simulations show the proposed method could diminish steady-state cross-track-error effectively. Test flights show the proposed method is applicable on low-cost fixed-wing UAVs.

The path following accuracy shown in simulations and test flights indicates the proposed method could be deployed in scenarios including inflight rendezvous, formation, trafficway take-off and landing.

This paper provides an improved high-accuracy path following method for low-cost fixed-wing UAVs.

The time delay would occurs when the measurements of multiple unmanned aerial vehicles (UAVs) are transmitted to the date processing center during cooperative target localization. This problem is often named as the out-of-sequence measurement (OOSM) problem. This paper aims to present a nonlinear filtering based on solving the Fokker–Planck equation to address the issue of OOSM.

According to the arrival time of measurement, the proposed nonlinear filtering can be divided into two parts. The non-delay measurement would be fused in the first part, in which the Fokker–Planck equation is utilized to propagate the conditional probability density function in the forward form. The time delay measurement is fused in the second part, in which the Fokker–Planck is used in the backward form approximately. The Bayes formula is applied in both parts during the measurement update.

Under the Bayesian filtering framework, this nonlinear filtering is not only suitable for the Gaussian noise assumption but also for the non-Gaussian noise assumption. The nonlinear filtering is applied to the cooperative target localization problem. Simulation results show that the proposed filtering algorithm is superior to the previous Y algorithm.

In this paper, the research shows that a better performance can be obtained by fusing multiple UAV measurements and treating time delay in measurement with the proposed algorithm.

In this paper, the OOSM problem is settled based on solving the Fokker–Planck equation. Generally, the Fokker–Planck equation can be used to predict the probability density forward in time. However, to associate the current state with the state related to OOSM, it would be used to propagate the probability density backward either.