Tracking Performance Research Articles

Sex differences in elite track and field performances and inferences about steroid doping.

Females likely experience larger performance benefits from androgenic-anabolic steroids than males. We set out to determine if there were temporal differences in select athletics (track and field) records between females and males. Exploratory aims included: (1) evaluating the improvements in female and male world records over time, and (2) investigating the influence of doping programs on male and female world records before and after 1990, when sports governing bodies began to implement random out-of-competition and systematic in-competition drug testing. We collected the top 500 performances of all time for both sexes from an online database (worldathletics.org) in four running events (100, 200, 400 and 800m) and two throwing events (discus throw and shot-put). Data were stratified into quintiles based on world record ranking (1st to 100th, 101st to 200th, etc.). The temporal distribution of the top 100 female performers was significantly earlier than the top 100 male performers (year: 2000±1 vs. 2005±1, respectively; P<0.0001). Within the event, the top performances occurred significantly earlier for females in the 800m (year: 1995±15 vs. 2003±12; P=0.0007) and shot-put (year: 1992±14 vs. 2003±17; P=0.0004). Among females, world records rapidly improved through the 1980s, but following 1990, the world records ceased to improve. Geographically, there was a greater representation of countries with state-sponsored doping programs, specifically among female performances. We postulate these sex differences in the temporal distribution of top performances are likely associated with enhanced effectiveness of exogenous androgens (steroid doping) among female athletes with lower endogenous androgen hormones compared to males.

Longitudinal and Lateral Cooperative Control of Preview Intelligent Vehicles with Stabilization

Aiming at the highly nonlinear and coupled longitudinal and transverse motion control problems of intelligent vehicle path tracking control, a new method of preview MPC path tracking control is proposed by combining visual preview model and model predictive control theory. The method first locally linearizes the nonlinear prediction model and transforms the MPC optimization solution problem into a quadratic programming problem. The longitudinal vehicle speed and the preview distance are then treated as known in each control time domain, an exponential model is applied to characterize the longitudinal vehicle speed, and a transverse angular velocity-center of mass lateral deflection (r-β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r-\\beta $$\\end{document}) phase plane plot is applied to characterize the vehicle stability. Finally, proportional integral differential stability controller is designed to improve vehicle stability. Simulation and test results show that: Compared with the MPC tracking control method, the preview MPC control method proposed in this paper can optimize the vehicle tracking performance on different adhesion coefficient road surfaces and improve the tracking accuracy, and this enhancement effect is more obvious at high speeds.

Modified preview repetitive control with equivalent-input-disturbance based on two-dimensional model

This paper deals with the problem of two-dimensional (2D) system-based modified preview repetitive control (MPRC) with equivalent-input-disturbance (EID) for continuous-time systems. First, an EID-based MPRC law is used to improve the tracking performance and disturbance attenuation. Next, by using a new equality constraint, a continuous-discrete 2D system is established and the design problem of the EID-based MPRC law is transformed into the stability problem of the 2D system. Then, we employ the 2D system theory together with the linear matrix inequality (LMI) approach, a sufficient condition is derived to ensure the stability of the closed-loop system. By solving an LMI, the gains of the controller and state observer can be obtained. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the developed controller design technique.

Recursive dynamic state estimation for power systems with an incomplete nonlinear DAE model

AbstractPower systems are highly complex, large‐scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates.

A Quasilinear Quadratic Tracking Method for Systems With Saturating Actuators

ABSTRACTLinear quadratic tracker (LQT) is usually employed to solve unconstrained tracking problems but falls short when dealing with systems exhibiting actuator saturation. This paper presents a novel quasilinear quadratic tracking method specifically designed to address this scenario. Firstly, the stochastic linearization (SL) approach is utilized to approximate the saturation nonlinearity with equivalent gains and biases using statistical properties of its input, which are thus incorporated into the system model so as to eliminate the nonlinearity. Then, different time scales are applied in the tracking controller and states in order to improve tracking accuracy. In addition, to reduce computational complexity, two algorithms are provided for approximating the equivalent gains and biases, catering to both scalar and vector control signals. Finally, the proposed algorithms are evaluated through numerical examples, demonstrating their effectiveness and superior tracking performances.

Rest the brain to learn new gait patterns after stroke

BackgroundThe ability to relearn a lost skill is critical to motor recovery after a stroke. Previous studies indicate that stroke typically affects the processes underlying motor control and execution but not the learning of those skills. However, these studies could be confounded by the presence of significant motor impairments. Furthermore, prior research involving the upper extremity indicates that stroke survivors have an advantage in offline motor learning when compared with controls. However, this has not been examined using motor acuity tasks (i.e., tasks focusing on the quality of executed actions) that have direct functional relevance to rehabilitation.ObjectiveInvestigate how stroke affects leg motor skill learning during walking in stroke survivors.MethodsTwenty-five participants (10 stroke; 15 controls) were recruited for this prospective, case-control study. Participants learned a novel foot-trajectory tracking task on two consecutive days while walking on a treadmill. The task necessitated greater hip and knee flexion during the swing phase of the gait. Online learning was measured by comparing tracking error at the beginning and end of each practice session, offline (rest-driven) learning was measured by comparing the end of the first practice session to the beginning of the second, and retention was measured by comparing the beginning of the first practice session to the beginning of the second. Online learning, offline learning, and retention were compared between the stroke survivors and uninjured controls.ResultsStroke survivors improved their tracking performance on the first day (p = 0.033); however, the amount of learning in stroke survivors was lower in comparison with the control group on both days (p ≤ 0.05). Interestingly, stroke survivors showed higher offline learning gains when compared with uninjured controls (p = 0.011).ConclusionsEven stroke survivors with no perceivable motor impairments have difficulty acquiring new motor skills related to walking, which may be related to the underlying neural damage caused at the time of stroke. Furthermore, stroke survivors may require longer training with adequate rest to acquire new motor skills.

Integrated automatic parking path planning and trajectory tracking optimization method

Current automatic parking systems often separate path planning and trajectory tracking, increasing complexity and failing to meet the parking needs of autonomous cars. Consequently, this study presents an approach based on Multi-stage Nonlinear Model Predictive Control (MSNMPC) for integrated autonomous parking path planning and trajectory tracking optimization. The method represents the evolution of uncertainty parameters over time through a scenario tree, and the MSNMPC controller with a predictive horizon P defines P + 1 phases, each with specific cost and constraint functions that depend only on the vehicle state and control inputs of that phase to satisfy the constraints of all phases of the parking process. In addition, the method integrates path planning and tracking control into one optimization problem, which is solved online to achieve integrated parking control. Simulation confirms that, in comparison to MPC with RRT* hierarchical control, the integrated parking scheme of MSNMPC has less lateral error and offers superior flexibility and tracking performance. To verify the generality of the scheme, it was validated in the diagonal parking scenario and the vertical parking scenario, respectively. The results show that, compared to the control experiments, the parking elapsed time has been reduced by 10.76% and 9.02%, respectively, enhancing parking efficiency. In addition, the parking error has decreased by 30.77% and 38.46%, respectively, thus improving parking accuracy. Moreover, the minimum safe distance in the control scheme for addressing uncertainty factors is 0.7 m greater than that of the control experiments, meeting the driving requirements for driverless vehicles in parking scenarios.

Approximation-based adaptive two-bit-triggered bipartite tracking control for nonlinear networked MASs subject to periodic disturbances

Purpose This paper aims to investigate the problem of adaptive bipartite tracking control in nonlinear networked multi-agent systems (MASs) under the influence of periodic disturbances. It considers both cooperative and competitive relationships among agents within the MASs. Design/methodology/approach In response to the inherent limitations of practical systems regarding transmission resources, this paper introduces a novel approach. It addresses both control signal transmission and triggering conditions, presenting a two-bit-triggered control method aimed at conserving system transmission resources. Additionally, a command filter is incorporated to address the problem of complexity explosion. Furthermore, to model the uncertain nonlinear dynamics affected by time-varying periodic disturbances, this paper combines Fourier series expansion and radial basis function neural networks. Finally, the effectiveness of the proposed methodology is demonstrated through simulation results. Findings Based on neural networks and command filter control method, an adaptive two-bit-triggered bipartite control strategy for nonlinear networked MASs is proposed. Originality/value The proposed control strategy effectively addresses the challenges of limited transmission resources, nonlinear dynamics and periodic disturbances in networked MASs. It guarantees the boundedness of all signals within the closed-loop system while also ensuring effective bipartite tracking performance.

CCE-OMBOC: A Simple and Efficient Constant-Envelope Technology for Multicarrier Navigation Modulation by Clipping

Multicarrier navigation modulation is a trend within next-generation global navigation satellite systems (GNSS) aiming to enhance navigation performance, but it forces amplifiers to work in nonsaturation zones, resulting in low power efficiency. This paper presents constant-envelope multiplexing (CEM) based on clipping to overcome the low transmission efficiency of orthogonal multi-binary offset carriers (OMBOCs). The clip constant-envelope OMBOC (CCE-OMBOC) features a hard limit for the original OMBOC signal, and its cross-correlation function (CCF) has a fixed ratio with the CCF of the original OMBOC. Thus, the clipping process has no adverse effect on navigation performance. Additionally, the expression of transmission and multiplexing efficiency is presented according to OMBOC’s amplitude distribution. A low sampling rate is suggested for the CCE-OMBOC, which reduces the cost of signal generation. For OMBOC, the CCE-OMBOC provides multiplexing efficiency comparable to that of constant-envelope multiplexing via intermodulation construction (CEMIC). CCE-OMBOC has a straightforward generation process; in contrast, the complexity of CEMIC rises significantly with increasing subcarriers. Moreover, the CCE-OMBOC is a multicarrier CEM modulation tool that has good tracking performance and excellent compatibility. The greater the number of subcarriers, the more navigation services and the higher the navigation data rate. The CCE-OMBOC can be used in next-generation GNSS and integrated communication and navigation systems.

Pericardial adhesion and performance of hemodynamically significant constrictive pericarditis using cine magnetic resonance feature tracking.

To investigate the diagnostic performance of cine magnetic resonance feature tracking (FT) for pericardial adhesions and hemodynamically significant constrictive pericarditis (CP). This retrospective study included patients who underwent cardiac magnetic resonance (CMR) imaging between 2011 and 2021. Twenty-four patients (median age, 73 years; seven females) suspected of having CP and undergoing CMR were selected (CP group). Age-matched 24 participants with heart failure (HF) and 24 healthy volunteers were also analyzed. The FT method assessed septal bounce, the presence of a dip or plateau pattern, and pericardial adhesions. The diagnostic performance, including the area under the receiver operating characteristic curve (AUC) for hemodynamically significant CP, was evaluated against catheterization results, and the adhesion detection in the combined CP and HF control was based on cine tagging or surgical records. Septal bounce was detected in 54% (13/24) of the patients by FT and 38% (9/24) of the patients by visual evaluation in the CP group but not in the HF or normal group (p < 0.001). To detect hemodynamically significant CP, the sensitivity and specificity of each finding were as follows: septal bounce, 85% (11/13) and 82% (9/11); adhesion, 100% (13/13) and 9% (1/11); dip, 77% (10/13) and 36% (4/11); and plateau, 69% (9/13) and 46% (5/11), respectively. The sensitivity and specificity of pericardial adhesion detection using FT and visual evaluation were 96% (23/24) and 100% (24/24), respectively, and the AUCs were 0.998 and 0.999, respectively. CMR-FT demonstrates high sensitivity and specificity for diagnosing hemodynamically significant CP and pericardial adhesions.

Safety-Critical Fixed-Time Formation Control of Quadrotor UAVs with Disturbance Based on Robust Control Barrier Functions

This paper focuses on the safety-critical fixed-time formation control of quadrotor UAVs with disturbance and obstacle collision risk. The control scheme is organized in a distributed manner, with the leader’s position and velocity being estimated simultaneously by a fixed-time distributed observer. Meanwhile, a disturbance observer that combines fixed-time control theory and sliding mode control is designed to estimate the external disturbance. Based on these techniques, we design a nominal control law to drive UAVs to track the desired formation in a fixed time. Regarding obstacle avoidance, we first construct safety constraints using control barrier functions (CBFs). Then, obstacle avoidance can be achieved by solving an optimization problem with these safety constraints, thus minimally affecting tracking performance. The main contributions of this process are twofold. First, an exponential CBF is provided to deal with the UAV model with a high relative degree. Moreover, a robust exponential CBF is designed for UAVs with disturbance, which provides robust safety constraints to ensure obstacle avoidance despite disturbance. Finally, simulation results show the validity of the proposed method.

A Positioning and Tracking Performance–Enhanced Composite Control Algorithm for the Macro–Micro Precision Stage

In the macro–micro composite motion process, the macro stage achieves rapid motion in a large workspace, while the micro stage realizes precise positioning within a small displacement. The large-stroke and high-speed motion of the macro stage is influenced by nonlinear friction, overshooting, and vibrations, making it challenging to achieve rapid stabilization within the travel range of the micro stage, thereby impacting equipment operation efficiency. This paper proposes a composite controller structure designed to solve the fast point-to-point positioning of the macro stage driven by a linear motor and enhance the trajectory tracking performance of the stage. The proposed composite control algorithm (CCA) includes velocity feed-forward, gain-scheduled proportional integral differential (PID) control, and a plug-in repetitive control method. By employing a tracking differentiator, velocity feed-forward, and a gain-scheduled PID controller, the control algorithm can realize rapid stabilization and positioning for the macro stage. Through velocity feed-forward, gain-scheduled PID control, and a plug-in repetitive controller, the control algorithm can reduce the trajectory tracking error of the stage. Simulations and experimental studies of point response and sinusoidal trajectory tracking are carried out on a macro–micro stage to verify the effectiveness of the composite controller for the linear motor. The experimental results demonstrate that the proposed composite controller effectively reduces the macro stage’s settling time and overshoot, and improves the accuracy of sinusoidal trajectory tracking, which lays a foundation for submicron positioning accuracy in high-speed macro–micro motion stages.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

First test beam of the DMAPS-based proton tracker at the pMBRT facility at the Curie Institute

Objective.Proton radiotherapy's efficacy relies on an accurate relative stopping power (RSP) map of the patient to optimise the treatment plan and minimize uncertainties. Currently, a conversion of a Hounsfield Units map obtained by a common x-ray computed tomography (CT) is used to compute the RSP. This conversion is one of the main limiting factors for proton radiotherapy. To bypass this conversion a direct RSP map could be obtained by performing a proton CT (pCT). The focal point of this article is to present a proof of concept of the potential of fast pixel technologies for proton tracking at clinical facilities.Approach.A two-layer tracker based on the TJ-Monopix1, a depleted monolithic active pixel sensor (DMAPS) chip initially designed for the ATLAS, was tested at the proton minibeam radiotherapy beamline at the Curie Institute. The chips were subjected to 100 MeV protons passing through the single slit collimator (0.4×20mm2) with fluxes up to1.3×107p s-1 cm-2. The performance of the proton tracker was evaluated with GEANT4 simulations.Main results.The beam profile and dispersion in air were characterized by an opening of 0.031 mm cm-1, and aσx=0.172mm at the position of the slit. The results of the proton tracking show how the TJ-Monopix1 chip can effectively track protons in a clinical environment, achieving a tracking purity close to 70%, and a position resolution below 0.5 mm; confirming the chip's ability to handle high proton fluxes with a competitive performance.Significance.This work suggests that DMAPS technologies can be a cost-effective alternative for proton imaging. Additionally, the study identifies areas where further optimization of chip design is required to fully leverage these technologies for clinical ion imaging applications.

Augmented reality guidance improves accuracy of orthopedic drilling procedures

In several orthopedic procedures, the accurate use of surgical power tools is critical to avoid damage to surrounding tissues. As such, various guidance techniques and safety measures were developed. Augmented reality (AR) guidance shows promise but requires validation. We evaluated a new approach using an inside-out infrared tracking solution for the HoloLens to compensate for its limited tracking performance. Eighteen participants with varying levels of experience (student, trainee, expert) each drilled twelve trajectories (six perpendicular, six oblique) in equidimensional wooden logs. Three different techniques were evaluated: freehand drilling; proprioception-guided drilling towards the contralateral index finger; and AR-guided drilling using a tracked drill and a virtual overlay of the log with predefined guidance vectors. The angular errors between planned and performed trajectories were compared using a mixed-design ANOVA. The results demonstrated that guidance technique (p < 0.001) and drilling direction (p < 0.001) significantly affected drilling accuracy, while experience (p = 0.75) did not. AR outperformed both other techniques, particularly for oblique trajectories (p < 0.001). For perpendicular trajectories, it only outperformed proprioception guidance (p = 0.04). Target plots revealed an important scatter perpendicular to the longitudinal axis of the log during freehand and proprioception-guided drilling, especially for oblique trajectories. This inaccuracy disappeared during AR-guided drilling. As such, we were able to conclude that AR guidance using inside-out infrared tracking reduced angular uncertainty during directional drilling, resulting in improved drilling accuracy. This improvement was particularly noticeable for complex trajectories and angles. The benefits of AR guidance were observed across all experience levels, highlighting its potential for orthopedic applications. We believe this study opens the way for the methodical evaluation of AR guidance in specific orthopedic use cases.

Improved FPTPPF-based predefined-time tracking control of a UVMS with actuator faults

In this paper, a new predefined-time control scheme is proposed for the trajectory tracking problem of an underwater vehicle-mechanic system (UVMS) with external disturbances, model uncertainties and actuator faults. First, a predefined-time controller is proposed to achieve convergence in a predefined time so that the convergence time does not depend on the initial value of the system. Second, considering the possible grasping and transportation tasks for the UVMS, an improved flexible predefined-time prescribed performance function (FPTPPF) with self-adjustment capability is proposed to avoid the vulnerabilities of the existing prescribed performance functions. A control framework is constructed for the integral barrier Lyapunov function and FPTPPF, which can achieve good tracking performance. Third, a predefined-time extended state observer (ESO) is constructed to address the problems caused by external disturbances, model uncertainties and actuator faults. For strong sudden disturbances, the H∞ control strategy is designed via the backstepping method, which effectively improves the robustness. Finally, the predefined-time stability of the system is proven via Lyapunov stability theory, where the tracking errors can converge to a small region of the null domain in a predefined time. The performance and superiority of the proposed predefined-time control method are verified via simulation comparisons.

Integral predictive sliding mode control for high-speed trains: A dynamic linearization and input constraint-based data-driven scheme

A control scheme with high reliability and excellent tracking performance is essential for the automatic operation of high-speed trains (HSTs). In this study, a novel discrete-time data-driven predictive sliding mode control (DDPSMC) scheme is proposed for multi-power unit HSTs. Initially, a nonlinear integral terminal sliding mode surface was designed to replace the traditional linear sliding mode function, thereby achieving a rapid system error convergence and alleviating chattering. Then, receding horizon optimization was integrated into predictive control, which allowed the predicted sliding mode state to follow the expected trajectory of a predefined continuous convergence law. This scheme enabled the system to obtain higher output error accuracy and explicitly handle input constraints. Moreover, to enhance robustness, a parameter update law and disturbance delay estimation algorithm were introduced to calculate the control gain and total uncertainty, respectively. Finally, a comparative test of the proposed control scheme was conducted using a CRH380A HST simulation experimental platform in a laboratory setting. Simulation results demonstrate that the velocity error range of each power unit of the HST under the proposed control scheme is within [−0.176 km/h, 0.152 km/h], while the control force and acceleration are within [−55.7 kN, 44.8 kN] and [−0.564 m/s2, 0.496 m/s2], respectively, with stable variation, and other performance indicators are also better than other comparison methods. These results satisfy the safety, stability, and punctuality requirements of the train.

Adaptive neuro-fuzzy inference system based active force control with iterative learning for trajectory tracking of a biped robot

ABSTRACT We investigate the integration of the active force control (AFC) scheme and the adaptive neuro-fuzzy inference system (ANFIS) as an intelligent controller algorithm to address trajectory-tracking problems in robotic systems, The AFC-ANFIS model exploits iterative learning (IL) to improve the tracking performance based on its trained model. The ANFIS parameters are tuned using both particle swarm optimisation (PSO) and beetle antennae search (BAS) algorithms. The simulation results of two different robots, i.e. a five-link biped robot and a PUMA 560 robot arm, indicate that the proposed AFC-ANFIS controller performs well for trajectory tracking and disturbances rejection. The AFC-ANFIS performance is evaluated and compared with those from other controllers using the average tracking error (ATE) metric. The comparative results reveal that AFC-ANFIS offers a viable approach with a rapid training process to undertaking trajectory tracking and disturbance rejection tasks.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Distributed Neural Adaptive Impedance Control for Cooperative Manipulation With Unknown Objects.

Existing cooperative manipulation methods for multiple manipulator systems usually assume that the grasp matrix and the desired trajectory of each manipulator are known in advance. In this work, distributed neural adaptive impedance control (AIC) strategies integrating fully distributed observers are proposed to remove both limitations. Specifically, two fully distributed finite-time observers are designed to estimate the actual and ideal states of the reference point without using global information. The estimates of the grasp matrix and the desired trajectory of each end-effector (EE) are then obtained by kinematic constraints and the estimates of the reference point's states. At the controller development, a distributed adaptive impedance model is established to achieve an adaptive trade-off between tracking performance and compliance. Then, distributed neural network (NN)-based tracking control strategies are developed to asymptotically realize the desired adaptive impedance dynamics in the presence of uncertainties. Additionally, a virtual energy tank (EK) is employed to interact with the impedance system to correct the adaptive impedance laws for system passivity. A simulation for four mobile manipulators tightly cooperative transport an unknown object is carried out to demonstrate the established results.