Target Tracking Control Research Articles

Sliding-mode control for target tracking of omnidirectional mobile robots subject to impulsive deception attacks

This article focuses on the target tracking of omnidirectional mobile robots (OMRs) subject to impulsive deception attacks. The impulsive deception attacks describe the cyber attacks causing instantaneous changes of system states at uncertain moments. To resist such kind of attacks, sliding-mode control (SMC) strategies are designed for the global asymptotic stability of the tracking errors of the OMRs. With the designed sliding-mode controller, the OMRs are capable of tracking the fixed and time-varying target in an experiment platform consisting of OMRs, wireless router and control center, which shows the robustness of the system with respect to impulsive deception attacks and the effectiveness of theoretical results.

Target Tracking and Circumnavigation Control for Multi-Unmanned Aerial Vehicle Systems Using Bearing Measurements

This paper addresses the problem of target tracking and circumnavigation control for a bearing-only multi-Unmanned Aerial Vehicle (UAV) system. First, using the bearing measurements, an adaptive algorithm in the form of a Proportional Integral (PI) controller is developed to estimate the target state. Subsequently, a distributed circumnavigation control protocol is established to evenly encircle the target. Then, we use the local information from each UAV in the network to calculate the relative position of the target, and further enhance the accuracy of estimation and circumnavigation algorithms by employing a Kalman filter. Finally, numerical simulation experiments are conducted to validate the effectiveness of the proposed tracking control algorithm.

Online Predictive Visual Servo Control for Constrained Target Tracking of Fixed-Wing Unmanned Aerial Vehicles

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

High-speed target tracking control system based on short-time rotational reflection imaging.

High-speed tracking technology has wide applications in the military and aerospace industry. However, existing approaches, such as camera arrays or Doppler radar systems, suffer from high cost and inconvenience. This paper reports a high-speed target tracking control system based on short-time rotational reflection imaging, specifically aimed at overcoming certain limitations. In the system we designed, a high-speed camera coupled with a rotating reflector is used to achieve reliable high-speed target tracking. This paper first introduces the working principle and mathematical model of the system, then analyzes the key technologies, including motor response delay time and rotational speed curve fitting, and, finally, verifies the feasibility of the system and the correctness of the theory based on a series of experiments. Experimental results demonstrated that our work is efficient and accurate in target tracking and image clarity. The developed system demonstrates significant potential for widespread use across military and aerospace sectors. Furthermore, the insights gained from our investigation into key technologies could act as a reference point for fellow researchers in related scientific areas.

Designing a Model Predictive Controller for Displacement Control of Axial Piston Pump

Variable displacement hydraulic pumps are widely used for energy efficiency, and they often have a mechanical feedback mechanism to ensure target tracking control performance and stability of tilt angle control. Furthermore, there are many examples which add electronic control to realize higher tracking control performance. However, in such cases, the control performance is significantly affected by the dynamic characteristics of the mechanical feedback mechanism, and this problem prevent its widespread use. Additionally, tilt angle control is susceptible to changes in dynamic characteristics and load pressure depending on the operating point, and there are constraints on the tilt angle. Hence, high control performance cannot be obtained without considering these nonlinearities. In this study, the variable displacement pump without mechanical feedback mechanism is focused on, and the objective of this study is to design a displacement control system for a hydraulic pump based on a model predictive control (MPC) that can consider various constraints on the design step. An adaptive system, which handles changes in dynamic characteristics and the effects of load pressure, is introduced. Additionally, the control performance of adaptive MPC is compared to adaptive model matching-based MPC with inverse optimization that can optimally design the weight matrices of the evaluation function without trial and error. Furthermore, in order to improve the transient response, a variable control input constraints are added in these two control systems. Experimental results of control performance have shown that the proposed method achieved a high tracking performance and short settling time, which confirmed the effectiveness of the variable control input constraints.

On the Stability of Nonlinear Model Predictive Control for 3D Target Tracking

In this study, we present the stability analysis of a Nonlinear Model Predictive Control (NMPC) of a 3D ground target tracking system model. The system model is derived considering the 3D kinematic system model referenced to the ground target. The NMPC adeptly track errors across five states: range, bearing angle, altitude, pitch angle, and speed. This objective is achieved through the utilization of yaw angle, pitch angle, and acceleration as control inputs. The contribution of this paper is the establishment of stability conditions within the Lyapunov framework for the closed-loop system. To demonstrate the efficacy of our proposed model, we conducted simulations, which shows high accuracy in tracking all designated states.

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

Reinforcement Learning-Based Cooperative Target Tracking Control of Unmanned Surface Vehicles Under Data Falsification Attacks

A Collision-Free Target Tracking Controller With Uncertain Disturbance Rejection for Quadruped Robots

A Collision-Free Target Tracking Controller With Uncertain Disturbance Rejection for Quadruped Robots

Target Tracking Control of UAV Through Deep Reinforcement Learning

This study presents an innovative reinforcement-learning-based control algorithm for a vertical take-off and landing (VTOL) aircraft under wind disturbances. In our approach, the tracking control problem of the VTOL aircraft is formulated as a Markov decision process, and the appropriate system state, reward function, and soft update method are presented. To improve the control accuracy under wind disturbances, three kinds of wind fields were added in the learning environment to expand the exploration space and simulate the effect of wind disturbances on the flight control. Moreover, to ensure the tracking accuracy and the practical implementation, a quantum-inspired experience replay strategy was developed based on quantum computation theory. In this strategy, the preparation operation scheme was designed to encourage the exploration and speed up the convergence. The depreciation operation method was developed to enrich the sample diversity, which increased the robustness of the controller and allowed the control strategy learned in the numerical simulations to be directly transferred into real-world environments. Numerical simulations, hardware-in-the-loop experiments, and real-world flight experiments were conducted to evaluate the performance and merits of the proposed method. The results demonstrated high accuracy and effectiveness and good robustness of the proposed control algorithm in terms of standoff target tracking control and flight stability.

Robust Cooperative Control of UAV Swarms for Dual-Camp Divergent Tracking of a Heterogeneous Target

Agents are used to exhibit swarm intelligence in the sense of convergence, while divergence is equivalently common in nature and useful in complex applications for multi-UAV systems. This paper proposes a robust target-tracking control algorithm, where UAV swarms are partitioned by a signed graph to perform opposite movements along or against the trajectory of the target. Uncertainties take place in both the fractional-order model of the target and the double-integrator dynamics of the UAVs. To tackle the challenge induced by the bipartite behavior and unknown components in the multi-UAV systems, the article comes up with a backstepping cascade controller and a new method for uncertainty estimation-compensation via a combined approach based on a neural network (NN) and an Uncertainty and Disturbance Estimator (UDE). Steered by the controller, UAVs in a structurally balanced network will display symmetry of their paths, pursuing or away from the target with respect to the origin. Theoretical derivation and numerical simulations have evidenced that the tracking errors converge to zero. Compared with the traditional NN method to solve such problems, this method is proposed for the first time, which can effectively improve the precision of cooperative target tracking and reduce the chattering phenomena of the controller.

Development of an Optronic Aiming System for Target Tracking on the S60 57mm Cannon Weapon Control System Using a Camera

The development of technological science towards the defense of a country is growing rapidly. The country needs increasingly advanced defense technology but is constrained by an increasingly large budget due to dependence on producing countries. The state provides opportunities for technocrats to carry out research that can later create a defense technology that is inexpensive and does not burden the state budget. The artificial S60 57mm cannon is one of the cannons that functions as an air attack deterrent. The operating system of this weapon is still manual. This research will design and prototype an automatic Weapon Control System on the S60 57mm gun. In this development using the Atmega 8 microcontroller is the controller and interface for transferring data from the camera (Optronic) to the servo motor. The result of the camera position (optronics) will be followed in real-time by the gun barrel. In designing a target tracking control system automatically using the proportional control method. The results of testing the ability of the servo motor as a camera driver to follow targets or moving objects with a maximum angular speed of 15.5 degrees/second at a speed of 0.3 seconds, the average frame rate of the camera are 60 fps. c. Based on testing the servo motor using the Atmega8 microcontroller support, the reading of the angle direction of the servo motor is displayed in the Delphi software. Based on the test results, there is a difference between mathematical calculations and measurements using an arc ruler. With mathematical calculations, there is a difference with a value of 1.06 degrees per 1 degree.

Robust Vision-Based Sliding Mode Control for Uncooperative Ground Target Searching and Tracking by Quadrotor

In this paper, the problem of searching and tracking uncooperative and unidentified mobile ground target using a quadcopter unmanned aerial vehicle (QUAV) is investigated. The proposed strategy is an Image-Based Visual Servoing (IBVS) approach, combined with the virtual camera concept and robust control. This strategy makes use of the rough prior information of the target, in contrast with existing strategies, which will alleviate the problem of altitude estimation noise and enhance the overall accuracy. Hence, a new vision-based sliding mode controller (SMC) is designed to control the quadcopter taking into account the flight phase’s heterogeneity, the external disturbances and parameters uncertainties as well as the target maneuverability. In order to get a better insight about the SMC tuning and adjustment, three different reaching laws are evaluated and compared. The proposed controller allows an automatic execution of the flight strategy whilst the searching phase relies on the Camera Coverage Area (CCA) technique. The vision-based technique allows an automatic QUAV altitude tuning for optimal target observation and tracking. Another contribution of this work is the fact that the designed controller validity and stability overspan the entire scenario to reach the universal and to smoothen out surges generated by control switching. Numerical simulations are conducted to compare the proposed SMC controllers and validate the effectiveness of the whole strategy.

Dynamic Target Tracking Control of Autonomous Underwater Vehicle Based on Trajectory Prediction.

Underwater dynamic target tracking technology has a wide application prospect in marine resource exploration, underwater engineering operations, naval battlefield monitoring, and underwater precision guidance. Aiming at the underwater dynamic target tracking problem, an autonomous underwater vehicle tracking control method based on trajectory prediction is studied. First, a deep learning-based target detection algorithm is developed. For the image collected by the multibeam forward-looking sonar image, this algorithm uses the YOLO v3 network to determine the target in a sonar image and obtain the position of the target. Then, a time profit Elman neural network (TPENN) is constructed to predict the trajectory information of the dynamic target. Compared with an ordinary Elman neural network, its accuracy of dynamic target prediction is increased. Finally, underwater tracking of the dynamic target is realized using the model predictive controller (MPC), and the tracking result is stable and reliable. Through simulations and experiment, the proposed underwater dynamic target tracking control method is demonstrated to be effective and feasible.

Event-triggered adaptive target tracking control for an underactuated autonomous underwater vehicle with actuator faults

In this paper, the target tracking control problem is investigated for an underactuated autonomous underwater vehicle (AUV) in the presence of actuator faults and external disturbances based on event-triggered mechanism. Firstly, the five degrees-of-freedom kinematic and dynamic models are constructed for an underactuated AUV, where the backstepping method is introduced as the major control framework. Then, radial basis function neural network (RBFNN) and adaptive control method are made full use of estimating and compensating the influences of uncertain information and actuator faults. Besides, the relative threshold event-triggered strategy is integrated into the tracking control to further reduce communication burden from the controller to the actuator. Moreover, through Lyapunov analysis, it is proved that the designed controllers guarantee that the tracking error variables of the underactuated AUV are uniformly ultimately bounded and can converge to a small neighborhood of the origin. Finally, the effectiveness and reasonableness of the designed tracking controllers are illustrated by comparative simulations.

GVF-based guidance and super-twisting control of autonomous surface vehicle for target tracking in obstacle environments with experiments

This paper addresses the target tracking problem of an under-actuated autonomous surface vehicle (ASV) subject to internal uncertainties and exogenous forces in ocean environments with static and dynamic obstacles. A collision-free target tracking (CFTT) control method is proposed based on a guiding vector field (GVF) guidance and a super-twisting algorithm (STA) control. Specifically, a resultant GVF is developed to guide the ASV towards the target position and to avoid multiple obstacles. Based on the resultant GVF, a saturated guidance law is designed to calculate the surge velocity and angular velocity commands. Then, robust exact differentiator (RED)-based observers are developed to estimate the lumped disturbances within finite time. By using the estimated lumped disturbances, STA-based anti-disturbance kinetic control laws are designed to track the given velocity commands within finite time. The finite-time input-to-state stability of the closed-loop system is proven based on cascade theory. Emulational and experimental validations are conducted to substantiate the efficacy of the proposed CFTT control method.

Target Tracking of Snake Robot with Double-Sine Serpentine Gait Based on Adaptive Sliding Mode Control

This paper studies the target tracking control strategy of a snake robot and proposes an adaptive sliding mode control method. The strategy ensures the robot follows the target path by controlling the joint angle through feedback, pushing the robot to reach the target position through gait function. In order to achieve target tracking, a kinematic model of a snake robot was first established in this paper. Then, we used double-sine serpentine gait to solve the problem of low steering efficiency caused by regular serpentine gait, and we explored the relationship between control parameters and robot steering. On the basis of gait, in order to further improve the efficiency of target tracking for the snake robot, an adaptive sliding mode control method, based on a new sliding mode reaching law, was proposed. Finally, the effectiveness and practicability of the proposed strategy was demonstrated by comparative analysis and simulation experiments.

Tracking Control of Quadrotor with Intermittent Target Measurements: A Stochastic Switched Systems Approach

This paper is devoted to the investigation of target tracking control of quadrotor with intermittent measurements, i.e., the measurements of target position are not continuously available. A stochastic switched systems approach is utilized to formulate such a tracking process of quadrotor by splitting it into two modes according to whether the measurements are available. The mode switching of the underlying system is subject to a Markov process to account for the inherent non-memory randomness of the dwell time of each mode, in contrast with the existing studies that impractically require the duration of the measurements to be greater than a given constant. The stability criteria for the underlying system are derived, upon which a switched controller is designed for the quadrotor position control, rather than the determination of the bounds of the dwell time for given pre-designed controllers in each mode. Simulations are presented to demonstrate the effectiveness of the developed theoretical results and the superiority of the designed switched controller for tracking control of quadrotor with intermittent target measurements.

Fast Convergent Tracking Control of Networked UAVs Over a Dynamic Interaction Topology

This brief investigates the target tracking control problem for a group of unmanned aerial vehicles (UAVs). Firstly, a neighbor selection mechanism is introduced to design the target state observer to guarantee the fast accessibility of target information to the uninformed UAVs. Then, a distributed tracking control protocol based on the estimated target information is proposed for dynamic target tracking. Meanwhile, proofs about the informed-to-uninformed reachability and the convergence of the group are provided. Finally, comparative simulations are conducted to verify the effectiveness of the proposed algorithm.

Online Learning of Minmax Solutions for Distributed Estimation and Tracking Control of Sensor Networks in Graphical Games

In this article, a new target tracking algorithm that is based on a distributed estimation-based control protocol is developed for multiple moving sensors subject to adversarial inputs. An augmented system composed of both the estimation error dynamics and the tracking error dynamics is introduced for the distributed estimation and target tracking control problem. We integrate estimation and control to simultaneously minimize infinite-horizon estimation and tracking errors in sensor networks with disturbances. A Minmax strategy as an alternative goal to Nash equilibrium is proposed to compute the sensor’s optimal motion controls in the presence of worst-case neighbor controls and disturbances. In contrast to Nash, Minmax yields a distributed algebraic Riccati equation that is easily solved locally by each sensor. A method based on reinforcement learning is given to compute sensor motion inputs so that all sensors estimate and track the target states. Value function approximation using neural networks is used to solve the distributed Bellman equations online based on the real-time observed data. Proofs are given to guarantee the performance of the combined distributed estimation and tracking algorithms. Finally, the simulation examples show the effectiveness of the proposed algorithm.

Deep Reinforcement Learning-Based End-to-End Control for UAV Dynamic Target Tracking.

Uncertainty of target motion, limited perception ability of onboard cameras, and constrained control have brought new challenges to unmanned aerial vehicle (UAV) dynamic target tracking control. In virtue of the powerful fitting ability and learning ability of the neural network, this paper proposes a new deep reinforcement learning (DRL)-based end-to-end control method for UAV dynamic target tracking. Firstly, a DRL-based framework using onboard camera image is established, which simplifies the traditional modularization paradigm. Secondly, neural network architecture, reward functions, and soft actor-critic (SAC)-based speed command perception algorithm are designed to train the policy network. The output of the policy network is denormalized and directly used as speed control command, which realizes the UAV dynamic target tracking. Finally, the feasibility of the proposed end-to-end control method is demonstrated by numerical simulation. The results show that the proposed DRL-based framework is feasible to simplify the traditional modularization paradigm. The UAV can track the dynamic target with rapidly changing of speed and direction.