This paper investigates the impact-angle-constrained guidance problem for anti-ship missiles equipped with strapdown seekers with limited field-of-view. A homing guidance law with switching logic is proposed to satisfy impact-angle constraint and field-of-view limitation. Based on optimal control theory, the impact-angle-constrained term is derived analytically. Then a biased term is designed for restricting look angle when it tends to exceed the pre-designed boundary value. Numerical simulation results demonstrate that the proposed guidance law can work perfectly with different impact angle constraint or look angle limit. Furthermore, the proposed approach requires smaller acceleration capacity and less energy consumption than the other two comparative methods.
KEYWORDS: Unmanned aerial vehicles, 3D tracking, 3D acquisition, 3D modeling, Numerical analysis, Numerical simulations, Motion models, Detection and tracking algorithms, 3D projection, Lithium
This article investigates the problem of UAV tracking three-dimensional trajectory and an optimal guidance law is proposed to guarantee minimum energy consumption. Based on optimal control theory, we first derive two-dimensional optimal path-point tracking guidance law. Then, the guidance approach is extended to three-dimensional for real application. The proposed method first calculates feature points of the pre-designated trajectory, and then guides the UAV to sequentially pass feature points to track the expected trajectory. The energy optimization can be proved by theoretical analysis and the numerical simulation results also indicate that the pro-posed method is feasible.
This paper proposes a novel sliding mode guidance law with finite-time convergent theory against maneuvering targets with impact angle constraint and autopilot lag. To mitigate the external disturbance stemming from the target maneuver, an adaptive sliding mode disturbance observer(ASMDO) is used to estimate the target maneuver precisely. During the process of guidance law design, a fixed-time control theory is used during the reaching stage to guarantee the system state convergence time upper bound is relative to the flight time of the missile which can ensure that the stable time of the system state is less than the flight time to avoid large miss distance. Furthermore, the autopilot lag is approximated as a second-order link to be more practical in engineering. After the guidance commands are designed, a controller based on dynamic surface control is designed to track the designed acceleration commands. The Lyapunov method is applied to demonstrate the stability of the guidance law. Numerical simulations prove the effectiveness and progressiveness of the proposed guidance law.
KEYWORDS: Missiles, Education and training, Databases, Neural networks, Detection and tracking algorithms, Monte Carlo methods, Covariance matrices, Data modeling, Simulations, Nonlinear dynamics
The accurate trajectory prediction of an incoming missile enables defense systems to effectively neutralize potential threats, thereby protecting civilian populations, military personnel, and infrastructure. Current researches focus on prediction under the assumption of knowing the attack target at the beginning of the engagement, which is seldom the case in reality. To deal with this issue, an intent recognition model based on a Gated Recurrent Unit (GRU) neural net-work is proposed in this paper. The inputs of the network are the available measurement information between the target and incoming missile, while the outputs are one-hot labels. To increase the training speed of the network and enhance its generalization capability, the adaptive moment estimation (Adam) algorithm is adopted for the training process. Based on the information from the network, a cubature Kalman filter (CKF), which integrates a higher-degree cubature rule to approximate the state distribution of the nonlinear dynamic system, is introduced to estimate the state and predict the trajectory of the incoming missile. Simulations present the transition process of the network and demonstrate that the proposed method achieves faster convergence and higher prediction accuracy compared to traditional approaches that are solely based on Kalman filters.
Aiming at the problem that the guided projectile is difficult to implement in-flight alignment of roll angle with low-cost gyroscope, this paper proposes an in-flight alignment scheme based on the pitch angle velocity vector caused by gravity. The relationship between the rotation angular velocity measured by the gyroscope and the roll angle at the alignment time is established. In order to avoid the influence of uncalibrated MEMS gyroscope on alignment accuracy, the gyroscope error is modeled and included in the alignment equation. Then the gyroscope error is estimated and compensated by adaptive Adam algorithm. Consequently, the accuracy of alignment can be effectively improved. Mathematical simulations show that the alignment accuracy of the roll angle can be within 3°, and the bias and installation error angle of the gyroscope can be effectively calibrated, which can improve the accuracy of inertial navigation.
The unmanned air vehicle (UAV) can provide a wider field of view for tanks to improve their survivability. And an optimal guidance law that makes UAVs follow a circular trajectory around the tank is proposed by introducing a relative reference frame. The guidance law consists of two phases: the first phase is for surveillance orbit entry, in which the UAV enters the orbit from the direction that is tangent to the circular orbit around the tank; and the second phase is for orbit holding, in which the UAV tracking circular orbit even though the tank performs active maneuver. Mathematical methods are used to analyze the convergence of lead angles and acceleration in the first phase and the convergence trend of path errors in the second phase. Numerical simulations are performed to validate the proposed guidance law.
This paper investigates the issue of impact-time-constrained guidance problem for a gliding missile and proposes a machine learning-based approach. A three-hidden-layer Deep Neural Network (DNN) is adopted with each layer included 100 neurons to realize the accurate prediction of the time-to-go of proportional navigation guidance (PNG), and an analytical impact time constrained guidance (AITCG) law is developed using the outputs of the DNN. Then a bias term is developed to nullify the difference between the predicted time-to-go and its desired value. The main benefit of this approach lies in its accurate time-to-go prediction with DNN for a highly nonlinear system. Hence the impact time will be corrected in quite an efficient manner. Simulation results demonstrate that the trained DNN accurately estimates the time-to-go and the AITCG law can meet the need of the impact time control. Numerous Monte-Carlo simulations are performed to support our findings.
This paper is based on the theory of sliding mode control, proposing a cooperative guidance law considering target maneuver and impact angle constraints in a three-dimensional environment. Initially, a cooperative interception guidance dynamic model with target acceleration is established. Subsequently, to address the issue of estimating the maneuvering target acceleration, an adaptive second-order sliding mode observer is proposed, which autonomously adjusts design parameters based on estimation errors and provides real-time compensation in the acceleration command. Furthermore, on the normal direction of the line of sight (LOS) and the LOS direction, a cooperative guidance law, which is based on a novel consistency protocol and terminal sliding mode guidance law are introduced to achieve simultaneous saturation attack on the target, ensuring finite-time con-vergence of the LOS angular rate. The finite-time stability of the designed guidance law is demonstrated through Lyapunov theory. Finally, the effectiveness of the proposed guidance law is verified through numerical simulations.
This paper investigates the three-dimensional (3-D) homing guidance problem with blind cone constraint. And an optimal guidance approach is proposed to address this issue. The proposed optimal guidance law is designed based on the optimal 3-D pure proportional guidance (PPN) with an additional bias term by lever-aging the predictor-corrector concept and optimal event-trigged control. The terminal impact vector of PPN is first analytically predicted with the Rodrigues’ rotation formula and its impact vector dynamics is established driven by the bias term. Then the bias term is designed as an event-triggered controller with optimal control theory. The controller will be triggered if the impact vector will fall into the blind cone and force the impact vector to deviate from the blind cone. The main benefit of the proposed guidance law lies in its optimal nature and robust characteristics. Hence it outperforms the impact-angle-control guidance law with preassigned an-gle. Theoretical analysis is performed to demonstrate its command characteristics and reveal its working principle. And numerical simulation results are presented to support our findings.
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