Tracking Guidance Research Articles

Development and Evaluation of Control Architectures for a Mechanically Deployed Entry Vehicle

The NASA-funded Pterodactyl Project seeks to advance the current state of the art for atmospheric entry vehicles by developing novel guidance and control technologies for deployable entry vehicles (DEVs). This paper details the efforts of the Pterodactyl team to develop, implement, and evaluate different control hardware effectors on an asymmetric, mechanically deployed DEV. Multiple control effector designs are developed for a baseline vehicle including propulsive control systems (reaction control systems) and nonpropulsive control systems (aerodynamic control surfaces and internal moving masses). For each control system, state-feedback integral controllers based on linear quadratic regulator optimal control methods are designed to track the guidance commands of either the bank angle or the angle of attack and sideslip angle. Using a lunar return reference mission, a comparative analysis of the performance and maneuverability of the DEV using the different control system configurations is conducted. The aerodynamic control surface design is demonstrated to be the most suitable architecture, primarily due to its ability to achieve acceleration rates that supersede the other designs. Considering performance, maneuverability, and guidance tracking during entry, the aerodynamic control surface design is recommended for this DEV and chosen reference mission.

Adaptive discrete-time NN controller design for automobile steer-by-wire systems with angular velocity observer

As one of steering systems, the steer-by-wire (SbW) system plays a significant role in the automobile. This paper investigates the study of the adaptive discrete-time neural network (NN) steering tracking control of the SbW system based on angular velocity observer. First, the continuous second-order SbW system is discretized by the Euler approximation approach. Before the control design, an adaptive discrete-time angular velocity observer is constructed to estimate the value of angular velocity signal. Then, an adaptive discrete-time control via a backstepping scheme is proposed to achieve the exact steering tracking performance, where the unknown nonlinear function, as well as the control coefficient, are incorporated into the lumped uncertainty and approximated by one NN. The main advantages of the proposed control scheme can be concluded that: (1) without using the exact prior knowledge of system uncertainty and angular position signal, the discrete-time tracking control is achieved and (2) the noncausal problem is overcome without transforming the original system into a predictor form. Finally, the Lyapunov stability theory shows that the tracking error converges to a small neighborhood of zero. Simulations and experiments illustrate the effectiveness and superiority of the proposed adaptive discrete-time NN control.

A Systematic Pipelaying Control Method Based on the Sliding Matrix for Dynamically Positioned Surface Vessels

A pipelaying control method is presented in this paper which includes path planning, path guidance, and path tracking controller for dynamically positioned (DP) surface vessels based on the characteristics of the predefined path in marine pipelaying operation. The pipelaying control method depends on path coding, path selection logic system, and a sliding matrix. The sliding matrix contains a vessel local path and its specified control requirements, which can be updated by sliding down the waypoint table line by line as the vessel is traveling from one path to the next. A line of sight (LOS) algorithm is developed to calculate the desired vessel position and heading on a circular arc path. The motion controller, which can simultaneously control the vessel speed at the directions of surge and sway, is designed by decomposing the desired inertial resulting velocity into the desired body velocity components. A DP simulator for pipelaying operation is developed, and in order to verify the proposed method, a pipelaying simulation is carried out. The simulation results show that the proposed method enables the vessel to move along the desired path while maintaining a set crab angle, a specified speed, and a turning radius. The pipeline can be laid onto the specified waypoints even when the vessel is subjected to drift forces caused by ocean currents, wind, and waves.

Task coupling based layered cooperative guidance: Theories and applications

Task coupling based layered cooperative guidance design and analysis issues for multiple missiles system are investigated. First, to deal with cooperative guidance problems with multiple task requirements, a task coupling based layered cooperative guidance structure is constructed by the leader layer and the follower layer. Then, for the leader missile layer, the cooperative detection guidance law is designed to achieve the coordination of the detection field of view (FOV) and cooperative interception, while providing the reference trajectories. Third, for the follower missile layer, the time-varying line of sight (LOS) angle formation tracking guidance law is proposed based on the leader missiles’ LOS angle trajectories convex combination. In addition, the design process and stability analysis of the closed-loop system are given. Finally, an equivalent experiment platform based on multiple mobile robots is introduced, which consists of two leaders, three followers, and a target is constructed. The theoretical results are verified by numerical simulation examples, and experimental results are presented on the platform.

Dually Supervised Track-Before-Detect Processing of Multichannel Video SAR Data

Track-Before-Detect (TBD) algorithm has been used to track weak moving target shadow in video synthetic aperture radar (SAR), where strong maneuverability may deteriorate tracking performance. Fortunately, the Doppler characteristic of target can additionally provide velocity guidance for tracking. This article presents ground moving target indication (GMTI) results of the multi-channel video SAR raw data, and a tracking approach is proposed based on the Doppler supervision in an improved dynamic programming-based TBD (DP-TBD) framework that uses a dual-domain merit function. Both the sequential high-resolution SAR images and low-resolution Range-Doppler (RD) spectra are used, and the clutter in RD spectrum is suppressed by using an adaptive displaced phase center antenna (ADPCA) technique. Fine states can be searched in dual-domain through resampling from random expansion in state initialization and transition. The inverse shadow amplitude and Doppler energy of the same potential target are simultaneously integrated to determine whether the target exists. The proposed Dual-DP-TBD can deal with the tracking of a time-varying number of targets in successive measurements. Compared to other TBD algorithms, this approach has fewer missing alarms and false alarms thanks to precise velocity estimation constrained from diverse features both in SAR image and RD spectrum.

Improving multi-target cooperative tracking guidance for UAV swarms using multi-agent reinforcement learning

Multi-Target Tracking Guidance (MTTG) in unknown environments has great potential values in applications for Unmanned Aerial Vehicle (UAV) swarms. Although Multi-Agent Deep Reinforcement Learning (MADRL) is a promising technique for learning cooperation, most of the existing methods cannot scale well to decentralized UAV swarms due to their computational complexity or global information requirement. This paper proposes a decentralized MADRL method using the maximum reciprocal reward to learn cooperative tracking policies for UAV swarms. This method reshapes each UAV's reward with a regularization term that is defined as the dot product of the reward vector of all neighbor UAVs and the corresponding dependency vector between the UAV and the neighbors. And the dependence between UAVs can be directly captured by the Pointwise Mutual Information (PMI) neural network without complicated aggregation statistics. Then, the experience sharing Reciprocal Reward Multi-Agent Actor-Critic (MAAC-R) algorithm is proposed to learn the cooperative sharing policy for all homogeneous UAVs. Experiments demonstrate that the proposed algorithm can improve the UAVs’ cooperation more effectively than the baseline algorithms, and can stimulate a rich form of cooperative tracking behaviors of UAV swarms. Besides, the learned policy can better scale to other scenarios with more UAVs and targets.

Modeling and Design of an Aircraft-Mode Controller for a Fixed-Wing VTOL UAV

Vertical takeoff and landing (VTOL) is an essential feature of unmanned aerial vehicles (UAVs). On the one hand, VTOL can expand and enhance the applications of UAVs; yet, on the other hand, it makes the design of control systems for UAVs more complicated. The most challenging demand in designing the control system is to achieve satisfactory response sharpness of fixed-wing UAVs to control commands and ensure that the aircraft mode channels are effectively decoupled. In this work, a six-degree-of-freedom (6-DoF) model with forces and moments is established based on the aerodynamic analysis, which is carried out through computational fluid dynamics (CFD) numerical simulation. The improved proportional derivative (PD) controller based on the extended state observer (ESO) is proposed to design the inner-loop attitude control, which increases the anti-interference ability for internal and external uncertainty of the UAV system. The motion equations of the UAV are established and divided into independent components of longitudinal and lateral motion to design the outer loop control law under minor disturbance conditions. A total energy control system (TECS) for the longitudinal height channel is proposed, which separates speed control and track control. L1 nonlinear path tracking guidance algorithm is used for lateral trajectory tracking so as to improve curve tracking ability and wind resistance. Effectiveness of this approach is proved by actual flight experiment data. Finally, a controller based on angular velocity control is designed to prevent the attitude and head reference system (AHRS) from malfunctioning. Its effectiveness is verified by the response test of the control system.

Collision avoidance guidance and control scheme for vector propulsion unmanned surface vehicle with disturbance

In this paper, a collision avoidance guidance and control (CAGC) scheme for fully autonomous unmanned surface vehicle (USV) according to the guidance, navigation and control (GNC) architecture is studied. The CAGC scheme is divided into guidance layer and control layer. To avoid the possibility of collision in tracking guidance, the proposed guidance layer needs remove the tracking guidance and realize the direct guidance to the control layer, which is designed according to the theory of finite control set model predictive control (FCS-MPC) with control ability. Next, to balance the contradiction between calculation and guidance accuracy, the methods of delay compensation and control set normal distribution are designed. In addition, in the control layer, the vector propulsion USV as the control object is non-minimum phase system because of its small size and significant sway thrust, so the virtual control point is used to solve this system, and the relationship between virtual control point and vessel speed is determined by zero-dynamics stability analysis. And the control layer adopts adaptive sliding mode control theory to resist the influence of strong disturbances. Finally, simulation results are provided to illustrate the efficacy of the CAGC scheme.

CACLA-Based Trajectory Tracking Guidance for RLV in Terminal Area Energy Management Phase.

This paper focuses on the trajectory tracking guidance problem for the Terminal Area Energy Management (TAEM) phase of the Reusable Launch Vehicle (RLV). Considering the continuous state and action space of this guidance problem, the Continuous Actor–Critic Learning Automata (CACLA) is applied to construct the guidance strategy of RLV. Two three-layer neuron networks are used to model the critic and actor of CACLA, respectively. The weight vectors of the critic are updated by the model-free Temporal Difference (TD) learning algorithm, which is improved by eligibility trace and momentum factor. The weight vectors of the actor are updated based on the sign of TD error, and a Gauss exploration is carried out in the actor. Finally, a Monte Carlo simulation and a comparison simulation are performed to show the effectiveness of the CACLA-based guidance strategy.

Distributed adaptive cooperative time-varying formation tracking guidance for multiple aerial vehicles system

Distributed adaptive cooperative time-varying formation tracking guidance problems of multiple aerial vehicles system against a maneuvering target are investigated. Different from the former results, the proposed cooperative guidance strategies are based on distributed time-varying formation tracking theories. Firstly, the leader aerial vehicle is designed to achieve interception with desired terminal attack angle, which provides a reference trajectory. The uncertain dynamic items and target's uncertain maneuver have been compensated. For the follower aerial vehicles, the distributed cooperative time-varying formation tracking guidance laws are raised. Adaptive distributed extended state observers are introduced to estimate the leader vehicle's states and input information. Then, an algorithm is shown for determining guidance parameters of cooperative guidance laws. Thirdly, the properties and the stabilities of the algorithm are discussed in detail. Finally, a simulation experiment is performed for showing the feasibility and effectiveness of achieved results.

Discrete-time adaptive neural network control for steer-by-wire systems with disturbance observer

This paper investigates the design and implementation of the discrete-time adaptive neural network control with disturbance observer (DO) on a steer-by-wire (SbW) system, to simultaneously realize accurate tracking and anti-interference performance. Specifically, to approximate the lumped system uncertainty including the friction torque and self-aligning torque, the neural network is employed. To improve the steering tracking performance, the discrete-time identification model is proposed so that the tracking error and modeling error can be utilized to adjust the neural network updating law. Then, the unknown compound disturbances caused by external disturbance, Euler approximation errors and neural network approximation error are restrained by two DOs. Finally, the Lyapunov stability theory shows that the system tracking error is uniformly ultimately bounded. Both numerical simulations and experiments are implemented to show the superiority of the proposed controller.

Development Of A First-Person View-Based Korean Sign Language Education System Using VIVE Hand Tracking

In this study, using VIVE and Tracking, we developed a ‘first-person view-based Korean sign language education system’ that guides sign language handshapes using a 3D virtual hand from the user's point of view and then evaluates the accuracy of the handshapes that learners perform. The learning effects of a Korean sign language education system that guides sign language handshapes using 3D virtual hands in a virtual space and a system that only learns with a teacher's lecture video in a virtual space were compared and analyzed. The number of times the two groups of participants tried to make the sign language correctly was measured. Then, the measurement results were compared and analyzed and interviews also conducted. As a result of the experiment, the ‘first-person view-based Korean sign language education system’ demonstrated that some sign language handshapes, which are not easy for participants to make, were implemented faster and more accurately than traditional learning methods. Therefore, if this system is used, it is expected that sign language learners will be able to learn more intuitively through the first-person perspective. Furthermore, it is expected that learning effects can be improved through the ability of being able to judge whether users are making the correct shapes with their hands. In addition, since it is possible to evaluate the accuracy of the hand shapes, it is believed that single-person learning, which was not easy in the existing sign language education paradigm, will become possible. However, for the sign language examined in the experiment, some showed significant differences while others were less meaningful. These were analyzed because the difficulty of the learning contents used in the experiment was not high. In future research, in order to more accurately verify the effectiveness of the sign language education system using hand tracking and 3D virtual hand guidance, we intend to conduct an experiment implementing learning content with more complex sentences

Hybrid Trajectory Optimization Method and Tracking Guidance for Variable-Sweep Missiles

In this paper, an offline hybrid trajectory optimization approach is proposed for variable-sweep missiles to explore the superiority in the diving phase. Aiming at the maximal terminal velocity with the impact angle constraint, the trajectory optimization model is formulated under multiple constraints, and the aerodynamic analysis in different sweep angles is discussed. Unlike only the attack angle used for the optimization process traditionally, the two-variable optimization scheme on both the attack angle and sweep angle is investigated for variable-sweep missiles. Then, the trajectory optimization problem is transformed into the nonlinear programming problem via a hybrid optimization strategy combining the Gauss pseudospectral method and direct shooting method to obtain the high precision and fast convergence solution. Finally, to verify the feasibility of the optimal trajectory under uncertainties, the tracking guidance law is designed on basis of the gain scheduled linear quadratic regulator control. Numerical simulation results reveal not only of the proposed hybrid optimization strategy but also of the superiority of variable-sweep missiles compared with traditional missiles.

A Novel IGC Scheme for RHV with the Capabilities of Online Aerodynamic Coefficient Estimation and Trajectory Generation

A novel integrated guidance and control (IGC) scheme for a Re-entry Hypersonic Vehicle (RHV) is proposed with the capabilities of online aerodynamic coefficient estimation based on an Unscented Kalman Filter and online trajectory generation based on the Gaussian pseudospectral method. A linear quadratic regulator is adopted for trajectory tracking guidance and a second-layer sliding mode controller is designed for attitude control. The variation of lift and drag coefficients are modeled and estimated online, based on which a new trajectory can be generated. The commands of trajectory generation are set as moments of actuators and their extremums pose more constraints on the rate and acceleration of flow angles. Comprehensive simulations are conducted and comparable IGC performances with normal conditions are obtained under large aerodynamic coefficient errors according to online generated trajectory, which proves the effectiveness and advantages of the proposed IGC scheme.

Adaptive Integral Terminal Sliding Mode Control for an Unmanned Surface Vehicle Against External Disturbances

Abstract This paper addresses a robust controller for an unmanned surface vehicle subject to external disturbances. An adaptive integral terminal sliding mode technique is designed to control the surge speed and yaw dynamics of the vehicle. Such controller is robust against bounded disturbances, allows finite-time convergence of the tracking error variable, and attenuates the chattering effect common to sliding mode approaches by adapting its control gain. Furthermore, a trajectory tracking guidance law is designed to provide speed and heading references. Simulation results illustrate and compare the advantages and feasibility of the proposed approach in the presence of wind, waves, and currents disturbances.

Neural network-based adaptive trajectory tracking control of underactuated AUVs with unknown asymmetrical actuator saturation and unknown dynamics

In this paper neural-network (NN) based adaptive trajectory tracking control scheme has been designed for underactuated Autonomous Underwater Vehicles (AUVs) which are subjected to unknown asymmetrical actuator saturation and unknown dynamics. First, some control preliminaries and assumptions along with AUV kinematic & kinetic models and trajectory tracking problems are elaborated. Secondly, the tracking error model and tracking guidance law are derived based on the theory of relative motion and the principle of approach angle respectively. Then the kinematic controller is designed by using the backstepping technique and Lyapunov theory. Similarly, AUV kinetic controller is designed by using the NN compensation and adaptive estimation techniques which is termed as NN-based adaptive controller (NNAC). Pertinently, a novel bounded saturation function has been developed to describe the unknown asymmetrical actuator saturation. The NN is adopted to approximate the complex AUV hydrodynamics and differential of desired tracking velocities. The bound of the generalized disturbance, which is composed of NN approximation error and ocean disturbances, are approximated based on the adaptive estimation technique. To analyze the stability of the developed NNAC, Lyapunov theory and backstepping technique are utilized by considering the control actions on different saturation sections. Finally, effectiveness and superiority of the proposed NNAC are validated through two sets of comparative simulation studies.

Modified trajectory tracking guidance for artillery rocket

1. Cao X. B., Xu Y. C., Rui C., Y., Shan Z., Y., 2013, Simulation of trajectory correction for an impulse control mortar projectile with a strapdown laser seeker, Applied Mechanics and Materials, 367, 377-381, DOI: https://doi.org/10.4028/www.sc.... CrossRef Google Scholar

Nonlinear Disturbance Observer-Based Standoff Target Tracking for Small Fixed-Wing UAVs

Small fixed-wing UAVs have attracted significant attention thanks to its pragmatic features such as small size, light weight, and low cost. Although many researchers have extensively studied small fixed-wing UAVs, there still remains a room for improvement. One of the improvements is to deal with external disturbances such as wind as small fixed-wing UAVs are susceptible to such external forces. This makes it difficult for UAVs to follow desired paths or moving targets, which may lead to a mission failure. In this paper, we present nonlinear disturbance observer-based standoff tracking guidance for multiple small fixed-wing UAVs in the presence of wind. The nonlinear disturbance observer is used to estimate the wind disturbance and the Lyapunov guidance vector field technique is used to compensate the effect of the wind and follow a ground target. The numerical simulations are carried out and outdoor flight experiments are performed to demonstrate the performance of the proposed approach in a real-world environment.

Leveraging Acoustic Signals for Vehicle Steering Tracking with Smartphones

Given the increasing popularity, mobile devices are exploited to enhance active driving safety nowadays. Among all safety services provided for vehicles, tracking the rotation angle of steering wheel in real time can monitor the vehicles’ dynamics and drivers’ behaviors at the same time. In this paper, we propose a steering tracking system, SteerTrack , which tracks the rotation angle of the steering wheel in real time leveraging audio devices on smartphones. SteerTrack seeks a device-free approach for steering tracking without requiring installation of specialized sensors on the steering wheels nor asking drivers to wear sensors on their wrists. Since the steering wheel is operated by a driver's hands, the rotation angle of the steering wheel can be tracked based on movements of the driver's hands. SteerTrack first builds an acoustic signal field inside of a vehicle and then analyzes the echoes reflected from the driver's hands with relative correlation coefficient (RCC) and reference frame to track the movement trajectory of hands under different steering maneuvers. Given the tracked movement trajectory, SteerTrack further develops a geometrical transformation -based method for estimating the rotation angle of the steering wheel in 3D driving environments by projecting the steering wheel to a 2D ellipse. Through extensive experiments in real driving environments with five volunteers for several weeks, SteerTrack can achieve an average steering wheel estimation error of 1.48 degree during driving, and 4.61 degree for turns.

Adaptive guidance and control of uncertain lunar landers in terminal landing phases

A novel double-loop guidance and control strategy for under-actuated lunar landers in terminal landing phases is developed by using adaptive nonlinear control approach in this study. To derive the main thrust input and inner-loop desired attitude trajectory, the outer-loop position tracking guidance law is firstly developed based on the hyperbolic tangent functions to guarantee the constrained thrust and singularity avoidance of the desired attitudes. Then, to avoid the complicated analytic-derivative computing of the desired attitude trajectory, a stable second-order filter is employed to generate the command attitude trajectory for the inner-loop attitude motion. Finally, an adaptive attitude tracking controller is designed by combining the barrier Lyapunove function and the backstepping technique to get rid of the singularities of the Euler angles-based attitude kinematic Jacobian matrix. In addition, tuning rules for designing parameters in guidance law and attitude controller are derived based on the Lyapunov analysis, and the pose tracking errors in the closed-loop system ultimately converge to the small neighborhoods of the origin. An example is simulated to verify the effectiveness of the proposed control design approach.