Optimal Tracking Performance Research Articles

Optimal tracking performance of communication‐constrained systems under energy constraints

AbstractThis paper investigates the optimal tracking performance of systems by considering packet dropouts, additive white Gaussian noise(AWGN), and coding under energy constraints. The optimal tracking performance of communication‐constrained systems is obtained by spectral decomposition and partial factorization. The results show that the optimal tracking performance of the system is related to intrinsic properties such as non‐minimum phase zeros and unstable poles. What's more, encoding, data loss rate, and AWGN are also able to affect the performance of the system. Finally, the correctness of the results is verified by specific examples.

Camera-Sensing-Assisted Fast mmWave Beam Tracking for Connected Automated Vehicles

The millimeter-wave (mmWave) technology can support the wide-band communication requirements for the environment raw perception information sharing to guarantee the safety of connected automated vehicles (CAVs). But, how to achieve the fast and robust mmWave beam tracking is a challenging problem in a high mobility CAVs scenario. Therefore, this article proposes a camera-sensing-assisted joint offline and online beam tracking (CS-JBT) algorithm in the mmWave frequency band. First, the mobility model is designed to predict the next moment of CAV and reduce the beam searching space overhead. In the offline learning phase of the proposed CS-JBT algorithm, the optimal beam pair under the current mobility state is obtained to guarantee the timeliness of the beam tracking process. Furthermore, to solve the mobility state deviation problem caused by the camera sensing error, the online learning phase is designed to achieve the optimal beam tracking performance efficiently for the practical scenario. Simulation and hardware testbed results verify that the proposed CS-JBT algorithm can minimize the beam tracking latency from 500 to 20 ms in contrast to the existing VBC-PF algorithm, while achieving a stable throughput over 2.5 Gb/s in the 28-GHz mmWave frequency band.

Data-Driven Feedforward Parameter Tuning Optimization Method Under Actuator Constraints

Feedforward control includes iterative force injection-based feedforward control and model-based feedforward control. Model-based feedforward control enables high performance for nonrepetitive motions in industrial motion systems. In particular, model-based feedforward control with a parameterized input shaping filter and a feedforward controller can effectively implement trajectory tracking tasks without relying on mathematical models of systems. However, the pre-existing model-based feedforward control does not consider the saturation boundary of the system actuator, which may lead to saturation of the feedforward force for actual systems with actuator constraints. Therefore, this article aims to develop a feedforward control method that obtains high tracking performance for nonrepetitive trajectory under actuator constraints. In particular, a parameterized input shaping and feedforward control method based on basis functions is pursued, which is combined with a data-driven iterative optimization approach to cope with nonrepetitive trajectory tracking tasks and actuator constraints. Simulations and experiments on a motion system confirm the optimal trajectory tracking performance under actuator constraints and robustness to nonrepetitive trajectory.

Tracking and Regulation Performance Limitations of Networked Control Systems Over Erasure Channel With Input Quantization

Limitations in communication networks, such as delays, packet dropouts, and insufficient bandwidth, create great challenges in the output tracking control and regulation performance of networked control systems. In this article, we propose some novel performance indexes for achieving the optimal tracking and regulation performance. By considering the influences of noise, packet dropouts, and logarithmic quantization on the channel input, the performance limitations of the networked systems are investigated by applying a two-degree-of-freedom (TDOF) control technique. The results obtained prove that the tracking and regulation performance is dependent not only on the position of the plant’s nonminimum phase zeros and unstable poles, but also on the directions. Furthermore, it is demonstrated that noise and quantization error have a negative influence on the control performance, and the design of the TDOF controller can eliminate the effect of packet dropouts on control performance. To validate the innovation results, several simulations are conducted.

Optimization-based adaptive neural sliding mode control for nonlinear systems with fast and accurate response under state and input constraints

The high-performance control requires the system to be stable, fast and accurate simultaneously. However, various systems (e.g., motors, industrial robots) generally face technical challenges such as nonlinearities, uncertainties, external disturbances and physical constraints, which make it difficult to reach the hardware potential of the systems to track the desired trajectories when satisfying the high-performance control requirements. Therefore, take a two-order nonlinear system for example, an optimization-based adaptive neural sliding mode control based on a two-loop control structure is proposed in this paper, where the outer and inner loops are designed separately to achieve different control requirements. Namely, the outer loop is designed as a model predictive control (MPC)-based optimization problem, which can optimize the desired trajectories to meet the state and input constraints, and maximize the converging speed of transient response as fast as possible, and the inner loop is designed with a recurrent neural network (RNN)-based adaptive neural sliding mode controller, which can guarantee the tracking of the replanned desired trajectories from outer loop as accurate as possible. The stability of the system is guaranteed by Lyapunov theorem, the optimal tracking performance is achieved under nonlinearities, uncertainties, external disturbances and physical constraints, and comparative simulation with a motor system is carried out to verify the effectiveness and superiority of the proposed approach.

Mental Workload in Neuropsychology: An Example With the NASA-TLX in Adults With HIV.

A preliminary set of analyses are presented, where workload was examined in 32 adults infected with the human immunodeficiency virus (HIV). Like the current COVID-19 pandemic (caused by the SARS-CoV-2 virus), HIV can produce a wide variety of symptoms, including various levels of cognitive dysfunction. In fact, a recent meta-analysis estimates that of the 39 million adults infected globally with HIV, 42.6% exhibit some form of HIV-associated neurocognitive disorder. A common cognitive symptom in HIV is decline in attention and executive functioning. Though typically examined by clinicians with less precise traditional paper-and-pencil neuropsychological tests, we examined this aspect of cognitive functioning using a more psychometrically sophisticated task as we had HIV-positive adults perform a computerized tracking task in single, dual, and tri-task conditions via the Multi-Attribute Task (MAT) Battery. Also assessed was mental workload, with the NASA-Task Load Index (NASA-TLX), rarely used in neuropsychology but a standard tool in human factors and neuroergonomics research. As expected, tracking performance declined with task condition difficulty (p < 0.001). Although no direct statistical comparisons were made, MAT performance here appeared worse than the MAT performance of various other groups reported in the research literature and in our laboratory. Ratings of workload also tended to increase as a function of task condition difficulty (p < 0.001). Plotting MAT tracking performance against the Mental Demand subscale scores, large individual differences in this aspect of workload were evident in both optimal and sub-optimal tracking performance. To examine likely variables with a potential impact on Mental Demand, a variety of variables (nadir CD4 count, viral load, depression symptoms, diagnosis of AIDS, presence of opportunistic infection, general cognitive status, etc.) were examined in relation to the Mental Demand scale, with age showing a significant association (r = 0.41, p = 0.022) and a diagnosis of AIDS showing trend associations (ps ≥ 0.066). Findings suggesting a deficit in metacognition or insight are also discussed. It is argued that assessment of workload (and its various aspects or components) can provide valuable additional information in neuropsychology.

Optimal Tracking Performance Analysis of MIMO Control Systems Under Multiple Constraints

This article is concerned with the optimal tracking performance on linear time-invariant (LTI) discrete-time multi-input–multi-output (MIMO) systems based on quantization, channel noise, and encoding–decoding, as well as bandwidth constraints. Meanwhile, power constraint, feedback and feedforward loops constraints of the systems are also taken into account. The tracking performance expression is described by inner–outer factorization, which is obtained by all stabilizing two-degree-of-freedom compensators. The results will illustrate that tracking performance limitation is decided by nonminimum phase zeros and unstable poles of the given plant. Moreover, time delay, encoding–decoding, channel noise, quantization, and bandwidth constraints also impact the performance. The accuracy of the method is verified by simulation examples.

Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

Hexameric rings of the scaffolding protein CheW enhance response sensitivity and cooperativity in Escherichia coli chemoreceptor arrays.

The Escherichia coli chemoreceptor array is a supramolecular assembly that enables cells to respond to extracellular cues dynamically and with great precision and sensitivity. In the array, transmembrane receptors organized as trimers of dimers are connected at their cytoplasmic tips by hexameric rings of alternating subunits of the kinase CheA and the scaffolding protein CheW (CheA-CheW rings). Interactions of CheW molecules with the members of receptor trimers not directly bound to CheA-CheW rings may lead to the formation of hexameric CheW rings in the chemoreceptor array. Here, we detected such CheW rings with a cellular cysteine-directed cross-linking assay and explored the requirements for their formation and their participation in array assembly. We found that CheW ring formation varied with cellular CheW abundance, depended on the presence of receptors capable of a trimer-of-dimers arrangement, and did not require CheA. Cross-linking studies of a CheA~CheW fusion protein incapable of forming homomeric CheW oligomers demonstrated that CheW rings were not essential for the assembly of CheA-containing arrays. Förster resonance energy transfer (FRET)-based kinase assays of arrays containing variable amounts of CheW rings revealed that CheW rings enhanced the cooperativity and the sensitivity of the responses to attractants. We propose that six-membered CheW rings provide the additional interconnectivity required for optimal signaling and gradient tracking performance by chemosensory arrays.

A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

Considering the performance requirements in actual applications, a look-up table based fractional order composite control scheme for the permanent magnet synchronous motor speed servo system is proposed. Firstly, an extended state observer based compensation scheme was adopted to suppress the motor parametric uncertainties and convert the speed servo plant into a double-integrator model. Then, a fractional order proportional-derivative (PDμ) controller was adopted as the speed controller to provide the optimal step response performance for the servo system. A universal look-up table was established to estimate the derivative order of the PDμ controller, according to the optimal samples collected by an improved differential evolution algorithm. With the look-up table, the optimal PDμ controller can be tuned analytically. Simulation and experimental results show that the servo system using the composite control scheme can achieve optimal tracking performance and has robustness to the motor parametric uncertainties and disturbance torques.

Proportional Tracking Control of Positive Linear Systems

This letter considers the reference tracking of positive linear systems via a proportional controller, which remains an open problem. Through using a virtual reference method in state-space models, optimal tracking performance is guaranteed for single-input multi-output (SIMO) systems via a bisection method. The proportional tracking for multi-input multi-output (MIMO) systems is reformulated as a bi-convex programming problem and a convergence-guaranteed algorithm is developed through using iterative linear matrix inequalities. Numerical simulations are provided to verify the proposed results.

Design of Generalized Sinusoidal Frequency Modulated Pulse Train Waveform to Improve Tracking Performance of High Duty Cycle Sonar Systems

High duty cycle sonar (HDCS) systems have a high potential for improving tracking performance compared to conventional pulsed active sonar systems, but their implementation has been challenging to achieve. This is because conventional studies had focused primarily on solving direct blast interference problems caused by continuous transmission and reception of pulse train waveforms. In this paper, we propose a design scheme for a generalized sinusoidal frequency modulated (GSFM) pulse train waveform to improve HDCS tracking performance. The proposed optimization scheme utilizes the trade-off relationship between detection performance in a reverberation environment and measurement uncertainty according to the parameter ρ of the GSFM waveform. The detection probability and measurement noise covariance matrix of the Kalman filter are calculated using the HCDS tracking framework, which reflects the designed GSFM pulse train waveform in the tracking process. The simulation using the HDCS tracking framework demonstrated that optimal tracking performance was obtained when the parameter ρ was 1.07.

Image‐based monitoring of bolt loosening through deep‐learning‐based integrated detection and tracking

AbstractStructural bolts are critical components used in different structural elements, such as beam‐column connections and friction damping devices. The clamping force in structural bolts is highly influenced by the bolt rotation. Much of the existing vision‐based research about bolt rotation estimation relies on traditional computer vision algorithms such as Hough transform to assess static images of bolts. This requires careful image preprocessing, and it may not perform well in the situation of complicated bolt assemblies, or in the presence of surrounding objects and background noise, thus hindering their real‐world applications. In this study, an integrated real‐time detect‐track method, namely, RTDT‐bolt, is proposed to monitor the bolt rotation angle. First, a real‐time convolutional‐neural‐networks‐based object detector, named YOLOv3‐tiny, is established and trained to localize structural bolts. Then, the target‐free object tracking algorithm based on optical flow is implemented to continuously monitor and quantify the rotation of structural bolts. In order to enhance the tracking performance against background noise and potential illumination changes during tracking, the YOLOv3‐tiny is integrated with the optical flow tracking algorithm to re‐detect the bolts when the tracking gets lost. Extensive parameter studies were conducted to identify optimal tracking performance and examine the potential limitations. The results indicate that the RTDT‐bolt method can greatly enhance the tracking performance of bolt rotation, which can achieve over 90% accuracy using the recommended range for the parameters.

Bi‐objective control design for vehicle‐mounted mobile antenna servo systems

This paper studies a high-performance control design for the servo system of a vehicle-mounted mobile satellite communication antenna. Considering the different requirements for tracking and disturbance attenuation in the servo system, a novel controller structure consisting of a central controller and a disturbance filter is proposed. Based on this controller structure, a bi-objective control design scheme is proposed: One is an optimal tracking design for the central controller, and the other is a disturbance attenuation design for the disturbance filter. It turns out that the resulting control system achieves an optimal tracking performance when the external disturbance is at a low level; meanwhile, the system achieves a robust disturbance attenuation performance when the disturbance is significant. Moreover, considering the trade-offs between performance and the robust stability of the closed-loop system, the weighting parameter selection in control design criteria is studied for a practical application. Furthermore, validation experiments and field tests are carried out to evaluate the effectiveness of this design, and their results show that the well-designed bi-objective controller significantly improves the dynamic pointing accuracy of the antenna in tracking a geostationary satellite when its carrier is driving off-road.

Adjustable Gain Enhanced Fuzzy Logic Controller for Optimal Wheel Slip Ratio Tracking in Hard Braking Control System

This paper has presented hard braking control system based on Adjustable Gain Enhanced Fuzzy Logic Controller (AGE-FLC) for optimal wheel slip ratio tracking performance. The purpose of the study was to improve slip ratio tracking and eliminate cycling while achieving very much shortened distance during emergency braking. The model of a braking vehicle at speed of 30 m.s^-1 subject to wheel locking was developed and implemented in MATLAB/Simulink environment. Simulation was conducted without a controller to study the slip ratio performance of the system on different road surfaces. The simulation results showed that stopping distance was 135.2 m in 5 seconds. A Fuzzy Logic Controller (FLC) whose control signal was enhanced by adding an adjustable gain mechanism to its output was designed. Simulation results showed that the AGE-FLC controller offered optimal tracking of desired wheel slip ratio of 0.1 as fast as possible on all road surface scenarios, while improving the stopping distance by 70.4% on dry road surface, 63.3% on wet road surface, 57.5% on cobblestone road surface and 48.8% on snow road surface in 2.651seconds.

Distributed Multi-Agent Target Tracking: A Nash-Combined Adaptive Differential Evolution Method for UAV Systems

Multi-agent cooperative target tracking plays a significant role in distributed artificial intelligence for unmanned aerial vehicle (UAV) systems. Most existing tracking solutions using online path planning focus on general target trajectories. Such trajectories have strong regularity and are smooth with minimal fluctuations. However, in real UAV applications a target will change its trajectory frequently to prevent from being tracked, and thus its trajectory will fluctuate greatly and randomly. To this end, in this paper we consider a realistic scenario where the UAV target trajectory is changeable and random. For real-time target tracking, we formulate the tracking task as a distributed model predictive control (DMPC) problem, the objective of which is to optimize tracking performance under various constraints. To do so, we innovatively combine the adaptive differential evolution (ADE) algorithm with Nash optimization, and then propose a Nash-combined ADE method. Specifically, we use ADE to adaptively adjust the predicted trajectory of each UAV agent and Nash optimization to efficiently solve DMPC formulation for global optimization, which can improve the tracking accuracy and simultaneously reduce the computational complexity. The simulation results show that the proposed method performs well in terms of tracking accuracy, UAV collision avoidance and tracking stability for realistic target trajectories.

Detection and Tracking of Aircraft from Small Unmanned Aerial Systems

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small unmanned aerial system (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with Federal Aviation Administration well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of linear Kalman filtering and extended Kalman filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS versus fixed-wing sUAS and multirotor sUAS versus general aviation (GA) plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.

A Two-Degree-of-Freedom Controller Design Satisfying Separation Principle With Fractional-Order PD and Generalized ESO

This article proposes a two-degree-of-freedom fractional-order proportional derivative (FOPD) controller with a general extended state observer (GESO) to achieve both optimal set-speed tracking and disturbance rejection performance for a typical permanent magnet synchronous motor servo system. The GESO simplifies the control plant, estimates and actively rejects total disturbances. The FOPD controller achieves fast speed tracking with almost no overshoot. Furthermore, it is verified that the control system with the proposed FOPD-GESO controller design meets the separation principle through mathematical derivation and simulation illustration. It shows that the proposed controller breaks the inherent tradeoff on tracking performance and disturbance robustness of the typical traditional proportional–integral–derivative (PID) control. Meanwhile, a systematic scheme for designing the FOPD-GESO controller to satisfy both frequency-domain and time-domain specifications is proposed in this article. Simulation illustration and experimental validation are performed to demonstrate that the proposed FOPD-GESO controller is superior to the typical integer-order PID controller, integer-order active disturbance rejection control with a typical extended state observer, and fractional-order PID controller in terms of speed tracking, antiload disturbance, and robustness to internal uncertainties.

Two Phase Linear Quadratic Gaussian Controller Design for Launch Vehicle Autopilot

The achievable payload of a launch vehicle is sensitive to the mass of the terminal stage which injects the payload into orbit, as well as to tracking errors introduced by the autopilot. The system is usually characterized by lightly damped slosh modes with uncertain parameters, whose stabilization is critical, while maintaining the overall performance of the system. Passive slosh stabilization involves introduction of mechanical baffles into the fluid tanks, which impacts the achievable payload mass. Optimal tracking performance along with active slosh stabilization is hence an ideal combination of autopilot design objectives. While optimal control methods are attractive for such systems, the lack of physical insight in the design procedure often limits the usefulness of these methods for launch vehicle autopilot design. A major challenge in stabilizing lightly damped slosh is the necessity to cater to very large mode magnitudes and phase swings exhibited by the system over the range of parameter perturbations. The significant contribution of this paper is the development of a two phase Linear Quadratic Gaussian approach, utilizing the physical insight into the system performance for tuning the weights in the performance index, to satisfy the time domain as well as frequency domain objectives. A robust design which exhibits superior time domain as well as frequency domain performance as compared to the benchmark classical controller is obtained, with near perfect phase stabilization of the lightly damped slosh dynamics.

Air–Ground Surveillance Sensor Network based on edge computing for target tracking

With the development of Internet of Things (IoT), Unmanned Aerial Vehicle (UAV) target tracking recently has received lots of attention in research community. The two major research topics in UAV target tracking include loss of a tracked target and high tracking latency, especially in the case where tracking is conducted in an area of many sight-blocking obstacles that put restrictions on a UAV’s flight altitude and block its line of sight. Most recent work on these topics focuses on improving tracking accuracy through adjusting tracking algorithms based on deep learning. But the complexity of these algorithms requires a computation capacity that a regular UAV cannot afford, and therefore tracking failure probability is still non-negligible. To address this challenge, we propose a new target tracking system, referred to as an Air–Ground Surveillance Sensor Network (AGSSN). We build AGSSN by jointly optimizing the network establishment and data transmission, and we design an algorithm ARIT to achieve an optimal tracking performance. We further carry out a series of simulations by deploying our AGSSN on a university’s campus map, and our simulation results show that our proposed AGSSN system can achieve higher reliability and significantly better performance than regular tracking systems in an area with many visually blocking obstacles.