With the increasing precision of guidance, the impact of autopilot dynamic characteristics and target maneuvering abilities on precision guidance is becoming more and more significant. In order to reduce or even eliminate the autopilot dynamic operation and the target maneuvering influence, this paper suggests a guidance system model involving a novel integral sliding mode guidance law (ISMGL). The method utilizes the dynamic characteristics and the impact angle, combined with a sliding mode surface scheme that includes the desired line-of-sight angle, line-of-sight angular rate, and second-order differential of the angular line-of-sight. At the same time, the evaluation scenario considere the target maneuvering in the system as the external disturbance, and the non-homogeneous disturbance observer estimate the target maneuvering as a compensation of the guidance command. The proposed system’s stability is proven based on the Lyapunov stability criterion. The simulations reveale that ISMGL effectively intercepted large maneuvering targets and present a smaller miss-distance compared with traditional linear sliding mode guidance laws and trajectory shaping guidance laws. Furthermore, ISMGL has a more accurate impact angle and fast convergence speed.
The design of mini-missiles (MMs) presents several novel challenges. The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision. The miniaturization of the size of MMs makes the design of the guidance, navigation, and control (GNC) have a larger-than-before impact on the main-body design (shape, motor, and layout design) and its design objective, i.e., flight performance. Pursuing a trade-off between flight performance and guidance precision, all the relevant interactions have to be accounted for in the design of the main body and the GNC system. Herein, a multi-objective and multidisciplinary design optimization (MDO) is proposed. Disciplines pertinent to motor, aerodynamics, layout, trajectory, flight dynamics, control, and guidance are included in the proposed MDO framework. The optimization problem seeks to maximize the range and minimize the guidance error. The problem is solved by using the nondominated sorting genetic algorithm II. An optimum design that balances a longer range with a smaller guidance error is obtained. Finally, lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.
In order to effectively defend against the threats of the hypersonic gliding vehicles (HGVs), HGVs should be tracked as early as possible, which is beyond the capability of the ground-based radars. Being benefited by the developing mega-constellations in low-Earth orbit, this paper proposes a relay tracking mode to track HGVs to overcome the above problem. The whole tracking mission is composed of several tracking intervals with the same duration. Within each tracking interval, several appropriate satellites are dispatched to track the HGV. Satellites that are planned to take part in the tracking mission are selected by a new derived observability criterion. The tracking performances of the proposed tracking mode and the other two traditional tracking modes, including the stare and track-rate modes, are compared by simulation. The results show that the relay tracking mode can track the whole trajectory of a HGV, while the stare mode can only provide a very short tracking arc. Moreover, the relay tracking mode achieve higher tracking accuracy with fewer attitude controls than the track-rate mode.
The formation control of multiple unmanned aerial vehicles (multi-UAVs) has always been a research hotspot. Based on the straight line trajectory, a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption. In order to avoid the collision between UAVs in the formation process, the concept of safety ball is introduced, and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs. Based on the idea of game theory, a method of UAV motion form setting based on the maximization of interests is proposed, including the maximization of self-interest and the maximization of formation interest is proposed, so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance. Finally, through simulation verification, the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length, and the UAV motion selection method based on the maximization interests can effectively complete the task formation.
In this paper, a bandwidth-adjustable extended state observer (ABESO) is proposed for the systems with measurement noise. It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise, which conflicts with observation accuracy. Therefore, we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system. The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error. When the tracking error decreases, the bandwidth decreases to suppress the noise, otherwise the bandwidth does not change. It is proven that the error dynamics are bounded and converge in finite time. The relationship between the upper bound of the estimation error and the scaling factor is given. When the scaling factor is less than 1, the ABESO has higher estimation accuracy than the linear extended state observer (LESO). Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments. The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.
The robotic airship can provide a promising aerostatic platform for many potential applications. These applications require a precise autonomous trajectory tracking control for airship. Airship has a nonlinear and uncertain dynamics. It is prone to wind disturbances that offer a challenge for a trajectory tracking control design. This paper addresses the airship trajectory tracking problem having time varying reference path. A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed parameters. It uses extended Kalman filter (EKF) for uncertainty and disturbance estimation. The estimated parameters are used by sliding mode controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, EKF based SMC (ESMC), is used as a robust solution to track airship trajectory. The proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model inaccuracies. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. The simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC under model uncertainties and wind disturbances.
Satellites with altitudes below 400 km are called super low altitude satellites (SLAS), often used to achieve responsive imaging tasks. Therefore, it is important for the manipulation of its ground track. Aiming at the problem of ground track manipulation of SLAS, a control method based on tangential impulse thrust is proposed. First, the equation of the longitude difference between SLAS and the target point on the target latitude is derived based on Gauss’s variational equations. On this basis, the influence of the tangential impulse thrust on the ground track’s longitude is derived. Finally, the method for ground track manipulation of SLAS under the tangential impulse thrust is proposed. The simulation results verify the effectiveness of the method, after manipulation, the satellite can visit the target point and revisit it for multiple days.
To ensure safe flight of multiple fixed-wing unmanned aerial vehicles (UAVs) formation, considering trajectory planning and formation control together, a leader trajectory planning method based on the sparse A* algorithm is introduced. Firstly, a formation controller based on prescribed performance theory is designed to control the transient and steady formation configuration, as well as the formation forming time, which not only can form the designated formation configuration but also can guarantee collision avoidance and terrain avoidance theoretically. Next, considering the constraints caused by formation controller on trajectory planning such as the safe distance, turn angle and step length, as well as the constraint of formation shape, a leader trajectory planning method based on sparse A* algorithm is proposed. Simulation results show that the UAV formation can arrive at the destination safely with a short trajectory no matter keeping the formation or encountering formation transformation.
In this paper, we study scene image recognition with knowledge transfer for drone navigation. We divide navigation scenes into three macro-classes, namely outdoor special scenes (OSSs), the space from indoors to outdoors or from outdoors to indoors transitional scenes (TSs), and others. However, there are difficulties in how to recognize the TSs, to this end, we employ deep convolutional neural network (CNN) based on knowledge transfer, techniques for image augmentation, and fine tuning to solve the issue. Moreover, there is still a novelty detection problem in the classifier, and we use global navigation satellite systems (GNSS) to solve it in the prediction stage. Experiment results show our method, with a pre-trained model and fine tuning, can achieve 91.3196% top-1 accuracy on Scenes21 dataset, paving the way for drones to learn to understand the scenes around them autonomously.
For the underwater long baseline (LBL) positioning systems, the traditional distance intersection algorithm simplifies the sound speed to a constant, and calculates the underwater target position parameters with a nonlinear iteration. However, due to the complex underwater environment, the sound speed changes with time and space, and then the acoustic propagation path is actually a curve, which inevitably causes some errors to the traditional distance intersection positioning algorithm. To reduce the position error caused by the uncertain underwater sound speed, a new time of arrival (TOA) intersection underwater positioning algorithm of LBL system is proposed. Firstly, combined with the vertical layered model of the underwater sound speed, an implicit positioning model of TOA intersection is constructed through the constant gradient acoustic ray tracing. And then an optimization function based on the overall TOA residual square sum is advanced to solve the position parameters for the underwater target. Moreover, the particle swarm optimization (PSO) algorithm is replaced with the traditional nonlinear least square method to optimize the implicit positioning model of TOA intersection. Compared with the traditional distance intersection positioning model, the TOA intersection positioning model is more suitable for the engineering practice and the optimization algorithm is more effective. Simulation results show that the proposed methods in this paper can effectively improve the positioning accuracy for the underwater target.
The performance of a strapdown inertial navigation system (SINS) largely depends on the accuracy and rapidness of the initial alignment. A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper. The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions, the SINS alignment is heuristically established as an optimization problem of finding the minimum eigenvector. In order to further improve the alignment precision, an adaptive recursive weighted least squares (ARWLS) curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics. Simulation studies and experimental results favorably demonstrate its rapidness, accuracy and robustness.
To solve the path following control problem for unmanned surface vehicles (USVs), a control method based on deep reinforcement learning (DRL) with long short-term memory (LSTM) networks is proposed. A distributed proximal policy optimization (DPPO) algorithm, which is a modified actor-critic-based type of reinforcement learning algorithm, is adapted to improve the controller performance in repeated trials. The LSTM network structure is introduced to solve the strong temporal correlation USV control problem. In addition, a specially designed path dataset, including straight and curved paths, is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible. Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.
The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation, while its influencing factors are complex and mutually coupled. Existing calculation methods have very limited analysis of the influence mechanism of influencing factors, and none of them has analyzed the influence of the guidance law. This paper considers the influencing factors of both the interceptor and the target more comprehensively. Interceptor parameters include speed, guidance law, guidance error, fuze error, and fragment killing ability, while target performance includes speed, maneuverability, and vulnerability. In this paper, an interception model is established, Monte Carlo simulation is carried out, and the influence mechanism of each factor is analyzed based on the model and simulation results. Finally, this paper proposes a classification-regression neural network to quickly estimate the interception probability based on the value of influencing factors. The proposed method reduces the interference of invalid interception data to valid data, so its prediction accuracy is significantly better than that of pure regression neural networks.
The dynamic weapon target assignment (DWTA) problem is of great significance in modern air combat. However, DWTA is a highly complex constrained multi-objective combinatorial optimization problem. An improved elitist non-dominated sorting genetic algorithm-II (NSGA-II) called the non-dominated shuffled frog leaping algorithm (NSFLA) is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints. In NSFLA, the shuffled frog leaping algorithm (SFLA) is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm (GA), displaying low optimization speed and heterogeneous space search defects. Two improvements have also been raised to promote the internal optimization performance of SFLA. Firstly, the local evolution scheme, a novel crossover mechanism, ensures that each individual participates in updating instead of only the worst ones, which can expand the diversity of the population. Secondly, a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search. Finally, the scheme is verified in various air combat scenarios. The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency, especially in many aircraft and the dynamic air combat environment.
This paper proposes an optimal maneuver strategy to improve the observability of angles-only rendezvous from the perspective of relative navigation. A set of dimensionless relative orbital elements (ROEs) is used to parameterize the relative motion, and the objective function of the observability of angles-only navigation is established. An analytical solution of the optimal maneuver strategy to improve the observability of angles-only navigation is obtained by means of numerical analysis. A set of dedicated semi-physical simulation system is built to test the performances of the proposed optimal maneuver strategy. Finally, the effectiveness of the method proposed in this paper is verified through the comparative analysis of the objective function of the observability of angles-only navigation and the performances of the angles-only navigation filter under different maneuver schemes. Compared with the cases without orbital maneuver, it is concluded that the tangential filtering accuracy with the optimal orbital maneuver at the terminal time is increased by 35% on average, and the radial and normal filtering accuracy is increased by 30% on average.
This paper investigates the problem of robust output regulation control with prospected transient property for strict feedback systems. By employing the internal model principle, the robust output regulation problem with a prospected property can be transformed to a robust stabilization problem with a new output constraint. Then, by constructing the speed function and adopting barrier Lyapunov function technique, the dynamic feedback controller can be designed not only to drive error output of the closed-loop system entering into a prescribed performance bound within a given finite time, but also to achieve that the error output converges to zero asymptotically. The effectiveness of the results is illustrated by a simulation example.
Airborne navigation database (NavDB) coding directly affects the result of analysis on the instrument flight procedure by the modern aircraft flight management computer (FMC). A reasonable flight track transition mode can improve the track tracking accuracy and flight quality of the aircraft. According to the path terminator (PT) and track transition characteristics of the performance based navigation (PBN) instrument flight procedure and by use of the world geodetic system (WGS)-84 ellipsoidal coordinate system, the algorithms for “fly by” and “fly over” track transition connections are developed, together with the algorithms for coordinates of fix-to-altitude (FA) altitude termination point and heading-to-an-intercept (VI) track entry point and for track transition display of the navigation display (ND). According to the simulation carried out based on the PBN instrument approach procedure coding of a certain airport and the PBN route data at a high altitude, the algorithm results are consistent with the FMC-calculated results and the actual ND results.
This paper presents a neighborhood optimal trajectory online correction algorithm considering terminal time variation, and investigates its application range. Firstly, the motion model of midcourse guidance is established, and the online trajectory correction-regenerating strategy is introduced. Secondly, based on the neighborhood optimal control theory, a neighborhood optimal trajectory online correction algorithm considering the terminal time variation is proposed by adding the consideration of terminal time variation to the traditional neighborhood optimal trajectory correction method. Thirdly, the Monte Carlo simulation method is used to analyze the application range of the algorithm, which provides a basis for the division of application domain of the online correction algorithm and the online regeneration algorithm of midcourse guidance trajectory. Finally, the simulation results show that the algorithm has high real-time performance, and the online correction trajectory can meet the requirements of terminal constraint change. The application range of the algorithm is obtained through Monte Carlo simulation.
In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named adaptive resource allocation scheme (ARAS) for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod’s lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in centrol processing unit (CPU) and memory resource usage rate.
To address the eccentric error of circular marks in camera calibration, a circle location method based on the invariance of collinear points and pole–polar constraint is proposed in this paper. Firstly, the centers of the ellipses are extracted, and the real concentric circle center projection equation is established by exploiting the cross ratio invariance of the collinear points. Subsequently, since the infinite lines passing through the centers of the marks are parallel, the other center projection coordinates are expressed as the solution problem of linear equations. The problem of projection deviation caused by using the center of the ellipse as the real circle center projection is addressed, and the results are utilized as the true image points to achieve the high precision camera calibration. As demonstrated by the simulations and practical experiments, the proposed method performs a better location and calibration performance by achieving the actual center projection of circular marks. The relevant results confirm the precision and robustness of the proposed approach.
Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game, which is an interception problem with a non-cooperative maneuvering target. The paper presents an automated machine learning (AutoML) based method to generate optimal trajectories in long-distance scenarios. Compared with conventional deep neural network (DNN) methods, the proposed method dramatically reduces the reliance on manual intervention and machine learning expertise. Firstly, based on differential game theory and costate normalization technique, the trajectory optimization problem is formulated under the assumption of continuous thrust. Secondly, the AutoML technique based on sequential model-based optimization (SMBO) framework is introduced to automate DNN design in deep learning process. If recommended DNN architecture exists, the tree-structured Parzen estimator (TPE) is used, otherwise the efficient neural architecture search (NAS) with network morphism is used. Thus, a novel trajectory optimization method with high computational efficiency is achieved. Finally, numerical results demonstrate the feasibility and efficiency of the proposed method.
Component failures can cause multi-agent system (MAS) performance degradation and even disasters, which provokes the demand of the fault diagnosis method. A distributed sliding mode observer-based fault diagnosis method for MAS is developed in presence of actuator and sensor faults. Firstly, the actuator and sensor faults are extended to the system state, and the system is transformed into a descriptor system form. Then, a sliding mode-based distributed unknown input observer is proposed to estimate the extended state. Furthermore, adaptive laws are introduced to adjust the observer parameters. Finally, the effectiveness of the proposed method is demonstrated with numerical simulations.
This paper presents an adaptive gain, finite- and fixed-time convergence super-twisting-like algorithm based on a revised barrier function, which is robust to perturbations with unknown bounds. It is shown that this algorithm can ensure a finite- and fixed-time convergence of the sliding variable to the equilibrium, no matter what the initial conditions of the system states are, and maintain it there in a predefined vicinity of the origin without violation. Also, the proposed method avoids the problem of overestimation of the control gain that exists in the current fixed-time adaptive control. Moreover, it shows that the revised barrier function can effectively reduce the computation load by obviating the need of increasing the magnitude of sampling step compared with the conventional barrier function. This feature will be beneficial when the algorithm is implemented in practice. After that, the estimation of the fixed convergence time of the proposed method is derived and the impractical requirement of the preceding fixed-time adaptive control that the adaptive gains must be large enough to engender the sliding mode at time $ t = 0 $ is discarded. Finally, the outperformance of the proposed method over the existing counterpart method is demonstrated with a numerical simulation.
This paper provides an improved model-free adaptive control (IMFAC) strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance. Firstly, the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization (local-CFDL). To take advantage of the resulting structure, use a discrete-time extended state observer (DESO) to estimate the unknown residual factor. Then, according to the study, the inclusion of a time delay has no effect on the linearization structure, and an improved control approach is provided, in which DESO is used to adjust for uncertainties. Furthermore, a DESO-based event-triggered model-free adaptive control (ET-DESO-MFAC) is established by designing event-triggered conditions to assure Lyapunov stability. Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated; otherwise, the control input will stay the same as it is at the last trigger moment. A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking. Finally, simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.
Motion planning is critical to realize the autonomous operation of mobile robots. As the complexity and randomness of robot application scenarios increase, the planning capability of the classical hierarchical motion planners is challenged. With the development of machine learning, the deep reinforcement learning (DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature. The DRL-based motion planner is model-free and does not rely on the prior structured map. Most importantly, the DRL-based motion planner achieves the unification of the global planner and the local planner. In this paper, we provide a systematic review of various motion planning methods. Firstly, we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features. Then, we concentrate on summarizing reinforcement learning (RL)-based motion planning approaches, including motion planners combined with RL improvements, map-free RL-based motion planners, and multi-robot cooperative planning methods. Finally, we analyze the urgent challenges faced by these mainstream RL-based motion planners in detail, review some state-of-the-art works for these issues, and propose suggestions for future research.
In multi-view image localization task, the features of the images captured from different views should be fused properly. This paper considers the classification-based image localization problem. We propose the relational graph location network (RGLN) to perform this task. In this network, we propose a heterogeneous graph construction approach for graph classification tasks, which aims to describe the location in a more appropriate way, thereby improving the expression ability of the location representation module. Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin. In addition, the proposed localization method outperforms the compared localization methods by around 1.7% in terms of meter-level accuracy.
This paper studies a finite-time adaptive fractional-order fault-tolerant control (FTC) scheme for the slave position tracking of the teleoperating cyber physical system (TCPS) with external disturbances and actuator faults. Based on the fractional Lyapunov stability theory and the finite-time stability theory, a fractional-order nonsingular fast terminal sliding mode (FO-NFTSM) control law is proposed to promote the tracking and fault tolerance performance of the considered system. Meanwhile, the adaptive fractional-order update laws are designed to cope with the unknown upper bounds of the unknown actuator faults and external disturbances. Furthermore, the finite-time stability of the closed-loop system is proved. Finally, comparison simulation results are also provided to show the validity and the advantages of the proposed techniques.
For the typical first-order systems with time-delay, this paper explors the control capability of linear active disturbance rejection control (LADRC). Firstly, the critical time-delay of LADRC is analyzed using the frequency-sweeping method and the Routh criterion, and the stable time-delay interval starting from zero is accurately obtained, which reveals the limitations of general LADRC on large time-delay. Then in view of the large time-delay, an LADRC controller is developed and verified to be effective, along with the robustness analysis. Finally, numerical simulations show the accuracy of critical time-delay, and demonstrate the effectiveness and robustness of the proposed controller compared with other modified LADRCs.
The attitude tracking control problem is addressed for hypersonic vehicles under actuator faults that may cause an uncertain time-varying control gain matrix. An adaptive compensation scheme is developed to ensure system stability and asymptotic tracking properties, including a kinematic control signal and a dynamic control signal. To deal with the uncertainties of the control gain matrix, a new positive definite one is constructed. The minimum eigenvalue of such a new control gain matrix is estimated. Simulation results of application to an X-33 vehicle model verify the effectiveness of the proposed minimum eigenvalue based adaptive fault compensation scheme.
In this paper, the formation control problem of second-order nonholonomic mobile robot systems is investigated in a dynamic event-triggered scheme. Event-triggered control protocols combined with persistent excitation (PE) conditions are presented. In event-detecting processes, an inactive time is introduced after each sampling instant, which can ensure a positive minimum sampling interval. To increase the flexibility of the event-triggered scheme, internal dynamic variables are included in event-triggering conditions. Moreover, the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive events. In addition, event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation (low-pass) filters. The asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.