Search Results (25)

In previous studies, a valveless piezoelectric pump with arc-shaped tubes (VPPAST) based on the Coriolis Effect was proposed. To promote the application of VPPAST in the field of navigation and guidance, it is vital to further explore the influences of the layout and structural parameters of arc-shaped tubes on the flow rate. Accordingly, in this study, the analysis of flow characteristics of fluid in arc-shaped tubes was conducted, and the velocity difference between the clockwise and counterclockwise flow of the liquid was reduced. Eventually, the flow equations of three layout modes of arc-shaped tubes were established. VPPAST with anomalous-direction arc-shaped tubes, single-arc-shaped tube, and same-direction arc-shaped tubes were produced using 3D printing technology. In addition, the valveless piezoelectric pump with the anomalous-direction arc-shaped tubes (VLPPADA) with different parameter flow tubes were also fabricated. Based on the resultant flow rates of each piezoelectric pump, it was demonstrated that the flow rate of the VLPPADA was the highest under the same driving conditions, and the flow rate can be determined as 1.72 mL/min when the driving voltage was set as 160 V at 14 Hz. It indicated that the pump flow rate of VLPPADA was directly proportional to the base radius and width of the arc-shaped tube. Full article

In recent decades, missile guidance and control have advanced significantly, with methods like pure pursuit (PP), command to line-of-sight (CLOS), and proportional navigation (PN) enabling accurate target interception in uncertain environments through line-of-sight (LOS) tracking. In this work, we propose a novel 3D sliding pure pursuit guidance (3DSPP) law for controlling a surface-to-air missile against a maneuvering target. The algorithm is compared with established guidance laws such as zero-effort miss distance “ZEM-PN” and “3D-PP”, with performance metrics including the miss distance $M_d$ and time of closest approach $t_{cap}$. The results demonstrate that the 3DSPP outperforms the conventional methods by achieving the lowest $M_d=$ 0.1497 m and the fastest $t_{cap}=$ 7.3853 s, ensuring more precise and rapid interception. The algorithm also exhibits superior robustness to noise and efficient energy management, making it a promising solution for real-world missile guidance systems. Full article

Networked cooperation of multi-unmanned-aerial-vehicle (UAV) is significant, but their cooperative guidance presents challenges due to weak cross-domain communication. Given the difficulties in information transmission security, this paper proposes a prescribed-time cooperative guidance law (PTCGL) for networked multiple UAVs with intermittent communication. Supported by the directed internal communication, the proposed PTCGL can ensure the simultaneous arrival in the case of the pinning external communication is time-triggered intermittent. In the first stage, PTCGL is designed to combine with a time-scaling function and second-order intermittent pinning consensus. With the desired convergence performance, the convergence time can be pre-specified independent of initial conditions and parameter tuning in theory. In the second stage, begin with the suitable initial conditions at the prescribed convergence time, the multiple UAVs are organized by proportional navigation guidance, so as to ensure the cooperation regardless of weak communication. Finally, simulations are conducted to verify the effectiveness and robustness of the proposed cooperative guidance law. Full article

Advanced computational methods are being applied to address traditional guidance problems, yet research is still ongoing regarding how to utilize them effectively and scientifically. A numerical root-finding method was proposed to determine the bias in biased proportional navigation to achieve the impact time control without time-to-go estimation. However, the root-finding algorithm in the original method might experience efficiency and convergence issues. This paper introduces an enhanced method based on neural networks, where the bias is directly output by the neural networks, significantly improving computational efficiency and addressing convergence issues. The novelty of this method lies in the development of a reasonable structure that appropriately integrates off-the-shelf machine learning techniques to effectively enhance the original iteration-based methods. In addition to demonstrating its effectiveness and performance of its own, two comparative scenarios are presented: (a) Evaluate the time consumption when both the proposed and the original methods operate at the same update frequency. (b) Compare the achievable update frequencies of both methods under the condition of equal real-world time usage. Full article

To increase the hit efficiency and lethality of a flight vehicle, it is necessary to consider the vehicle’s guidance law concerning both impact time and angle constraints. In this study, a novel and straightforward impact time and angle control guidance law that is independent of time-to-go and small angle approximations is proposed with two stages using a data-driven method and proportional navigation guidance. First, a proportional navigation guidance-based impact angle control guidance law is designed for the second stage. Second, from various initial conditions on the impact angle control guidance simulation with various initial conditions, the input and output datasets are obtained to build a mapping network. Using the neural network technique, a mapping network model that can output the ideal flight path angle in flight is constructed for impact time control in the first stage. The proposed impact time and angle control guidance law reduces to the proportional navigation guidance law when the flight path angle error converges to zero. The simulation results show that the proposed guidance law delivers excellent performance under various conditions (including cooperative attack) and features better acceleration performance and less control energy than does the comparative impact time and angle control guidance law. The results of this research are expected to supplement those exploring various paradigms to solve the impact time and angle control guidance problem, as concluded in the current literature. Full article

This paper investigates the impact-angle-control guidance problem for varying-speed flight vehicles with constrained acceleration. A learning-based bias proportional navigation guidance (L-BPN) law is proposed to achieve impact-angle-constrained impact by constructing a deep neural network (DNN) for nonlinear mapping between the impact angle and the bias term. During the process of dataset establishment, the impact of state variables is evaluated by sensitivity analysis to minimize the quantity of training data. This approach also effectively accelerates sample generation and improves the training efficiency. The simulation results verify the effectiveness of the proposed L-BPN law and demonstrate its advantages over the existing algorithms. Full article

This paper proposes a dynamic encircling cooperative guidance (DECG) law to enable multiple interceptors to cooperatively intercept a superior target, considering low velocity, limited overload, impact angle and simultaneous arrival constraints. First, the feasible escaping area of the target is analyzed and a dynamic encircling strategy for the target is established. This strategy efficiently provides virtual escaping points, allowing interceptors to dynamically encircle the target without excessive energy consumption, ultimately leading to a successful interception. Second, to enhance the physical feasibility of the kinematic equations governing the interaction between interceptors and target at the virtual escaping points, the independent variable is substituted and the kinematic equations are remodeled. Convex optimization is employed to address the multi-constraint optimal guidance problem for each interceptor, thereby facilitating simultaneous interception. Compared with the existing guidance laws, DECG has a more practical and feasible cooperative strategy, is able to handle more constraints including the interceptor’s own constraints and cooperative constraints, and does not rely on the precise calculation of explicit remaining flight time in the guidance law implementation. Lastly, the effectiveness, superiority and robustness of the DECG law are evaluated through a series of numerical simulations, and its performance is compared with that of the cooperative proportional navigation guidance law (CPNG). Full article

Guidance commands of flight vehicles can be regarded as a series of data sets having fixed time intervals; thus, guidance design constitutes a typical sequential decision problem and satisfies the basic conditions for using the deep reinforcement learning (DRL) technique. In this paper, we consider the scenario where the escape flight vehicle (EFV) generates guidance commands based on the DRL technique, while the pursuit flight vehicles (PFVs) derive their guidance commands employing the proportional navigation method. For every PFV, the evasion distance is described as the minimum distance between the EFV and the PFV during the escape-and-pursuit process. For the EFV, the objective of the guidance design entails progressively maximizing the residual velocity, which is described as the EFV’s velocity when the last evasion distance is attained, subject to the constraint imposed by the given evasion distance threshold. In the outlined problem, three dimensionalities of uncertainty emerge: (1) the number of PFVs requiring evasion at each time instant; (2) the precise time instant at which each of the evasion distances can be attained; (3) whether each attained evasion distance exceeds the given threshold or not. To solve the challenging problem, we propose an innovative solution that integrates the recurrent neural network (RNN) with the proximal policy optimization (PPO) algorithm, engineered to generate the guidance commands of the EFV. Initially, the model, trained by the RNN-based PPO algorithm, demonstrates effectiveness in evading a single PFV. Subsequently, the aforementioned model is deployed to evade additional PFVs, thereby systematically augmenting the model’s capabilities. Comprehensive simulation outcomes substantiate that the guidance design method based on the proposed RNN-based PPO algorithm is highly effective. Full article

This paper addresses the guidance of various flight vehicles under multiple constraints in three-dimensional space. A cooperative guidance strategy that satisfies both time and angle constraints is designed to reach a moving target. The strategy is organized into two parts: modeling and programming calculations. First, a nonlinear motion model for guidance is established and normalized, including both the vehicle and the target. Later, the arrival method is automatically determined according to the strategy and depending on the type of target. The cooperative terminal time is determined based on an augmented proportional navigation method. An improved model predictive static programming (MPSP) algorithm was designed as a means of adjusting the adaptive terminal time. Then, the algorithm was used to update the control quantity iteratively until the off-target quantity and the angle of constraints were satisfied. The simulation results showed that the strategy could enable multiple flight vehicles at different initial positions to reach the target accurately at the same time and with the ideal impact angle. The strategy boasts a high computational efficiency and is capable of being implemented in real time. Full article

The target can deceive the flight vehicle by releasing an infrared decoy to make the line-of-sight (LOS) angle rate deflect greatly, thus causing the flight vehicle to miss the target. Therefore, in order to accurately strike the target in complex adversarial scenarios, this paper proposes a finite-time convergence guidance law (FTCG) combined with a finite-time disturbance observer (FTDO). The complex adversarial scenario is established by combining the relative motion model between the flight vehicle and the target and the motion model of the infrared decoy. Based on this, considering the dynamic characteristic of the flight vehicle’s autopilot, a guidance model is obtained. Utilizing sliding mode control theory and finite-time control theory, an FTCG of the LOS angle rate is designed. Then, the finite-time convergence of the guidance law is proved and the total convergence time is derived. Finally, for the target maneuvering that is difficult to measure in the guidance law, an FTDO is used to estimate and compensate for the target maneuvering in the guidance law. Simulation results show that the FTCG can make the LOS angle rate quickly converge and accurately strike the target in different scenarios, with a good guidance accuracy and robustness. Compared with the sliding mode guidance law (SMGL) and the adaptive sliding mode guidance law (ASMGL) based on an extended state observer (ESO), the advantages of the designed guidance law are illustrated. Finally, FTCG is extended to be three dimensional and compared with the proportional navigation guidance law (PNG) to further illustrate its effectiveness in a three-dimensional coordinate system. Full article

Complex farmland backgrounds and varying light intensities make the detection of guidance paths more difficult, even with computer vision technology. In this study, a robust line extraction approach for use in vision-guided farming robot navigation is proposed. The crops, drip irrigation belts, and ridges are extracted through a deep learning method to form multiple navigation feature points, which are then fitted into a regression line using the least squares method. Furthermore, deep learning-driven methods are used to detect weeds and unhealthy crops. Programmed proportional–integral–derivative (PID) speed control and fuzzy logic-based steering control are embedded in a low-cost hardware system and assist a highly maneuverable farming robot in maintaining forward movement at a constant speed and performing selective spraying operations efficiently. The experimental results show that under different weather conditions, the farming robot can maintain a deviation angle of 1 degree at a speed of 12.5 cm/s and perform selective spraying operations efficiently. The effective weed coverage (EWC) and ineffective weed coverage (IWC) reached 83% and 8%, respectively, and the pesticide reduction reached 53%. Detailed analysis and evaluation of the proposed scheme are also illustrated in this paper. Full article

Repeated and missed spraying are common problems during the working of boom sprayers, especially in the breakpoint continuous process. Therefore, the present study investigated a breakpoint continuous spraying system for automatic-guidance boom sprayers based on a hysteresis compensation algorithm for spraying. An operational breakpoint identification algorithm, which combines a real-time kinematic global navigation satellite system (RTK-GNSS) and wheel odometer, was proposed; a pre-adjusted proportional-integral-derivative (PID) control algorithm for the opening degree of the proportional control valve was designed in thus study. Tests were conducted to establish equations correlating the opening degree of the proportional control valve, pump output flow rate, and main pipeline flow rate, with an R² ≥ 0.9525. The time to adjust to the target flow rate was experimentally tested. The breakpoint identification accuracy of the RTK-GNSS and RTK-GNSS + wheel odometer was experimentally assessed. A field spraying deposition variation experiment was conducted. According to the results, the system effectively eliminated missed spraying, with a maximum repeated spraying distance of ≤3.3 m, and it achieved a flow control error within 3%. This system also reduced the repeated spraying area and enhanced the pesticide spraying quality of breakpoint continuous spraying for automatic-guidance boom sprayers. Full article

Aiming at the penetration problem of high-speed vehicles against a modified proportional guidance interceptor, a three-dimensional mathematical model of attack–defense confrontation between the high-speed vehicle and the interceptor is established in this paper. The modified proportional navigation guidance law of the interceptor is included in the model, and control constraints, pitch angle velocity constraints, and dynamic delay are introduced. Then, the performance index of the optimal penetration of high-speed vehicles is established. Under the condition of considering the 180-degree BTT control, the analytical solutions of the optimal speed roll angle and the optimal overload of high-speed vehicles are obtained according to symmetric Hamilton principle. The simulation results show that the overload switching times of high-speed vehicles to achieve optimal penetration are N − 1, where N is the modified proportional guidance coefficient of the interceptor. When the maximum speed roll angle velocity is [60, 90] degrees per second, the penetration effect of high-speed vehicles is good. Finally, the optimal penetration guidance law proposed in this paper can achieve a miss distance of more than 5 m when the overload capacity ratio is 0.33. Full article

This paper proposes an impact time control guidance law based on exact nonlinear kinematics equations. To address the impact time control problem of providing enhanced intercept accuracy, we formulated an error variable whose regulation ensures the fulfillment of the required tasks without time-to-go estimation. Based on the Lyapunov stability theory, a desired line-of-sight rate profile that satisfies the convergence of the error variable was constructed, from which the guidance command was designed using the optimal tracking formulation. The simple structure of the proposed guidance law enables the prediction of interceptor behavior during homing, thereby allowing the interceptor to maneuver along feasible trajectories. In addition, although the structure of the proposed guidance law is simple and similar to that of proportional navigation, it is theoretically guaranteed to execute the required mission precisely at the end of homing. Numerical simulations demonstrated that the proposed guidance law achieved effective target interception under various terminal constraint settings. Full article

This study applies deep-reinforcement-learning algorithms to integrated guidance and control for three-dimensional, high-maneuverability missile-target interception. Dynamic environment, reward functions concerning multi-factors, agents based on the deep-deterministic-policy-gradient algorithm, and action signals with pitch and yaw fins as control commands were constructed in the research, which control the missile in order to intercept targets. Firstly, the missile-interception system includes dynamics such as the inertia of the missile, the aerodynamic parameters, and fin delays. Secondly, to improve the convergence speed and guidance accuracy, a convergence factor for the angular velocity of the target line of sight and deep dual-filter methods were introduced into the design of the reward function. The method proposed in this paper was then compared with traditional proportional navigation. Next, many simulations were carried out on high-maneuverability targets with different initial conditions by randomization. The numerical-simulation results showed that the proposed guidance strategy has higher guidance accuracy and stronger robustness and generalization capability against the aerodynamic parameters. Full article