General uninhabited aerial vehicle (UAV) situation assessment methods do not consider the influence of complex external environment on the decision-maker, and usually only get the ranking results of the evaluation. Since the decision-maker needs to make decisions in a short period of time, misjudgments or missing strike windows often occur. To address this problem, a three-way decision model based on the cumulative prospect theory is proposed. First, the method the utilizes intuitionistic fuzzy technique for order preference by similarity to an ideal solution to estimate the conditional probability of each target and obtains the situation assessment result. Next, the method calculates the intuitionistic fuzzy situation information obtained by the UAV based on the cumulative prospect theory, and obtains the corresponding cumulative prospect value when each target performs different actions. Finally, based on the principle of maximizing the cumulative prospect value, a new three-way decision rule is derived to divide the situational assessment results into three regions. The experimental analysis shows that the method not only obtains the target threat ranking, but also classifies the target threat level objectively. At the same time, it considers the psychology of the decision-maker in the assessment process, and obtains the target threat assessment results that meet the traits of the decision-maker, providing a reasonable decision support for the complex and changing air combat.
Aimed at the modeling error of the integrated strapdown inertial navigation system(SINS)/global positioning system (GPS) navigation system and the particle degradation problem of particle filter(PF), an in-flight alignment method of integrated SINS/GPS navigation system based on the combined PF-UKF filter is proposed, in combiation with the unscented Kalman filter(UKF). First, the attitude angle is replaced by the error quaternion. The position and velocity differences between SINS and GPS are selected as the observation variables. In addition, a novel error equation of the integrated navigation system is established. Moreover, the sampled particles are divided into random particles and deterministic particles in the proposed combined PF-UKF filter. The random particles are collected by probability density function, and the determined particles are the state values obtained by collecting sigma point of UKF algorithm. Therefore, the proposed method can effectively reduce the complexity of PF and the degree of particle degradation. The simulation results show that compared with the UKF algorithm, the proposed method can effectively improve the error accuracy of integrated navigation system with a better robustness.
A linear parameter-varying (LPV) integrated control law is designed for a hypersonic vehicle to achieve trajectory control based on an altitude-horizontal trajectory control concept. The LPV output-feedback control theory and pole placement techniques are employed to design parameters of the control law within a Mach number envelope. Such a control law performs integrated control for longitudinal and lateral-directional dynamics of the vehicle, free from the scheme of inner and outer control loops of classical flight controls and ensuring robust and optimal control performance in the sense of L2-induced norm. A mathematical model of the hypersonic vehicle is developed in the Earth-centered-Earth-fixed reference frame. Earth rotation, Earth oblateness, and the second order harmonic perturbations of Earth are considered in the model. Numerical simulations are conducted to examine the performance of the LPV controller. The simulation results indicate that the closed-loop system of the hypersonic vehicle achieves D-stability. The LPV control law achieves a good performance in vehicle trajectory control and has sufficient robustness with respect to perturbations and sensor noise.
When the deep space probe approaches the target planet, due to the rapid increase of the gravity of the target planet, the orbital dynamics model will have a rapid acceleration change. Because the noise covariance is not completely known, the traditional filtering algorithm cannot obtain the optimal estimation of navigation parameters, which is difficult to meet the performance requirements of the approach navigation system. In order to meet the requirements of high stability and accuracy of the system, a sliding window adaptive nonlinear filtering algorithm based on system noise covariance is proposed. By constructing the system noise covariance update function and using the sliding window to smooth the noise covariance, the errors caused by velocity noise and position noise are separated, the filter parameter information used is updated in real time, and the system noise covariance is adjusted adaptively. Taking the Mars probe as an example, the simulation results show that, compared with the traditional unscented Kalman filtering method, the position accuracy and velocity accuracy of the proposed method are improved by 90.97% and 66.17% respectively, which suppresses the rapidly changing integral error on the system model, and solves the divergence problem of the traditional filtering method. In addition, the influence of filtering period and window size on navigation performance is analyzed, which provides a feasible new adaptive filtering method for autonomous navigation of deep space exploration.
Because the navigation errors of inertial navigation system accumulate with time, the unmanned aerial vehicle (UAV) formation that only relies on inertial navigation system for positioning cannot obtain precision navigation information in long time flight. To solve this problem, this paper proposes a cooperative navigation scheme for master-slave UAV formation. First, the UAV is equipped with relative navigation sensors to measure the relative velocity and position information between the members of the master-slave UAV formation. Then, considering the relative pose of formation members, the spatial unified transformation scheme is studied. The absolute navigation information measured by each member of UAV formation by inertial navigation system and the relative navigation information measured by relative sensors is unified into the same navigation coordinate system. Finally, a cooperative navigation scheme based on relative velocity and relative position assistance is given. The 30 min simulation results show that the speed and position errors of each cluster converge to 0.1 m/s and 5 m respectively under this scheme, which is more suitable than the inertial navigation system.
In this paper, a nonlinear control method is proposed based on the framework of gain adaptive sliding mode control to deal with the attitude control problem of an unmanned aerial vehicle (UAV), which shows a strong robustness with respect to dynamical uncertainties and external disturbance. In the proposed method, an adaptive gain schedule scheme is proposed to deal with dynamical uncertainties while suppressing the chattering in the sliding mode control. First, the UAV model is introduced and its mathematical model is given. Then, the error is used as the state variable to design a stably converging sliding mode surface, and the gain adaptive super-twisting sliding mode (ASTSM) algorithm is used to design a UAV attitude controller that can converge in finite time, and the stability of the closed-loop UAV system is demonstrated by the Lyapunov’s second method. Finally, the efficiency of the proposed method is demonstrated through comparative simulations.
Entry footprint is an essential manifestation of vehicle maneuverability, which can provide the basis for trajectory planning and guidance, landing point selection, etc. A fast footprint-generation method based on the pseudospectral method is proposed. The influencing factors of footprint are simulated and analyzed. In this method, the attack and bank angles are simultaneously discretized as control quantities to form the nonlinear programming problem of the pseudospectral method, and the footprint is obtained by solving the maximum transverse range problem for several different longitudinal conditions. Moreover, the affecting factors of the footprint are studied. The simulation results show that the mass, reference area, atmospheric density, etc., do not cause the change of the footprint within a specific range. Beyond a certain range, the short longitudinal trajectory would be significantly affected. The left half of the footprint is affected, while the right half is not changed. The effect of the lift-to-resistance ratio on the footprint is significant, and its size is positively related to the footprint range.
A decoupling controller based on linear/nonlinear switching active disturbance rejection control (SADRC) is designed for a class of continuous linear nominal multi-input multi-output (MIMO) system. The system model is introduced and a linear matrix inequality (LMI) based absolute stability analysis approach is proposed for the designed SADRC decoupling controller. Finally, the effectiveness of the proposed method is verified by numerical simulations.
