Track Initiation Research Articles

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

Blockchain and smart contracts for supply chain transparency and vendor management

Blockchain technology and smart contracts are emerging as transformative tools for enhancing transparency and efficiency in supply chain management and vendor relations. Traditional supply chains often face challenges such as inefficiencies, lack of transparency, and susceptibility to fraud (Kouhizadeh & Sarkis, 2018). Blockchain, a decentralized ledger technology, coupled with smart contracts, which are self-executing contracts with the terms directly written into code, offers promising solutions to these challenges (Wang, Han, & Beynon-Davies, 2019). This paper explores the application of blockchain and smart contracts in these domains, examining their potential to provide immutable records, streamline processes, and mitigate risks (Casado-Vara et al., 2018). Through a comprehensive analysis of current literature and case studies, we identify key benefits including improved transparency, enhanced efficiency, and better risk management (Christidis & Devetsikiotis, 2016; Tian, 2016). Our findings suggest that blockchain and smart contracts can significantly improve supply chain transparency and vendor management, though their implementation requires careful consideration of technical, regulatory, and organizational factors (Saberi et al., 2019). Notable case studies, such as Walmart’s blockchain pilot for food safety and De Beers' diamond tracking initiative, illustrate the practical benefits and challenges of adopting these technologies in real-world scenarios (Casino, Dasaklis, & Patsakis, 2019; Kshetri, 2018). Despite the promising outlook, further research is needed to address scalability, interoperability, and regulatory compliance issues to fully realize the potential of blockchain and smart contracts in supply chain management (Hughes et al., 2019).

Track initialization and re-identification for 3D multi-view multi-object tracking

We propose a 3D multi-object tracking (MOT) solution using only 2D detections from monocular cameras, which automatically initiates/terminates tracks as well as resolves track appearance–reappearance and occlusions. Moreover, this approach does not require detector retraining when cameras are reconfigured but only the camera matrices of reconfigured cameras need to be updated. Our approach is based on a Bayesian multi-object formulation that integrates track initiation/termination, re-identification, occlusion handling, and data association into a single Bayes filtering recursion. However, the exact filter that utilizes all these functionalities is numerically intractable due to the exponentially growing number of terms in the (multi-object) filtering density, while existing approximations trade-off some of these functionalities for speed. To this end, we develop a more efficient approximation suitable for online MOT by incorporating object features and kinematics into the measurement model, which improves data association and subsequently reduces the number of terms. Specifically, we exploit the 2D detections and extracted features from multiple cameras to provide a better approximation of the multi-object filtering density to realize the track initiation/termination and re-identification functionalities. Further, incorporating a tractable geometric occlusion model based on 2D projections of 3D objects on the camera planes realizes the occlusion handling functionality of the filter. Evaluation of the proposed solution on challenging datasets demonstrates significant improvements and robustness when camera configurations change on-the-fly, compared to existing multi-view MOT solutions.

Joint track initiation of resolvable group target

This paper studies the problem of resolvable group target track initiation (RGTI) and proposes a novel method to obtain the discrete association vectors and continuous trajectory parameters of individual targets within the group, which benefits from the fitting representations of target tracks and the utilization of group target motion constraint. Specifically, every target track within the group is modeled as a weighted sum of a same set of time basis functions with different weights, and the aim is to solve multiple pairs of association vectors and trajectory parameters, i.e., unknown weights, according to the measurement equation and group target motion constraint. Next, a joint RGTI framework, which includes track extraction, track purification, trajectory parameter and association vector correction, is proposed to transform the original complex constraint set solving problem into multiple solvable sub-problems. And the convergence of RGTI is ensured through iterative optimization of track purification, trajectory parameter correction and association vector correction. Finally, simulation example demonstrates the superiority of the proposed method compared with existing multi-target track initiation methods.

RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating

Accurately classifying targets and clutter plots is crucial in radar data processing. It is beneficial for filtering out a large amount of clutters and improving the track initiation speed and tracking accuracy of real targets. However, in practical applications, this problem becomes difficult due to complex electromagnetic environments such as cloud and rain clutter, sea clutter, and strong ground clutter. This has led to poor performance of some commonly used radar plot classification algorithms. In order to solve this problem and further improve classification accuracy, the radar plot classification algorithm based on evidence adaptive updating (RPC-EAU) is proposed in this paper. Firstly, the multi-dimensional recognition features of radar plots used for classification are established. Secondly, the construction and combination of mass functions based on feature sample distribution are designed. Then, a confidence network classifier containing an uncertain class was designed, and an iterative update strategy for it was provided. Finally, several experiments based on synthetic and real radar plots were presented. The results show that RPC-EAU can effectively improve the radar plot classification performance, achieving a classification accuracy of about 0.96 and a clutter removal rate of 0.95. Compared with some traditional radar pattern recognition algorithms, it can improve by 1 to 10 percentage points. The target loss rate of RPC-EAU is also the lowest, only about 0.02, which is about one third to one half of the comparison algorithms. In addition, RPC-EAU avoids clustering all radar points in each update, greatly saving the computational time. The proposed algorithm has the characteristics of high classification accuracy, low target loss rate, and less computational time. Therefore, it is suitable for radar data processing with high timeliness requirements and multiple radar plots.

Unified framework for multi-sensor distributed fusion with memory configuration

This paper investigates the multi-sensor distributed fusion with memory configuration for multi-target automatic tracking (MTAT) in clutter. Aimed at fully addressing the problems of both track origin ambiguity and the strong-correlation among system track and its paired local tracks in the framework, this paper derives an Exact Unified Multisensory Tracks Fusion with Memory (EU-MTFwM) method that fuses the system track with enumerated combinatorial tracks to achieve fusing all sensors' local tracks at once. It deploys track-to-track probabilistic association (T2TPA) and target-existence based track automatic management (TAM) to mitigate track origin ambiguity caused by clutter disturbance and the presence of multiple targets. Additionally, it implements the exact distributed kinematic state estimation (exact DKSE) to remove strong correlation stemming from common historical measurements and process noise. However, as the number of sensors to be fused increases, the proposed EU-MTFwM suffers from the combinatorial explosion. An Implementable Unified Multisensory Tracks Fusion with Memory (IU-MTFwM) method is then derived to sequentially fuse the system track with origin-ambiguous local tracks from multiple sensors one-by-one, providing a unified but computationally efficient alternative to the EU-MTFwM. Furthermore, a novel system track initialization technology is proposed to trigger the multisensory tracks fusion recursion. Numerical results show the proposed two unified methods achieve comparable tracking performance to the centralized fusion benchmark but with significantly reduced communication consumption, and demonstrate intensive improvement over the enhanced state-of-the-art that incorporates our proposed system track initialization into its fusion recursion.

Scouts vs. usurpers: alternative foraging strategies facilitate coexistence between neotropical Cathartid vultures

Understanding how diverse assemblages of scavengers can coexist on shared ecological resources is a fundamental challenge in community ecology. However, current approaches typically focus on behaviour at carcass provisioning sites, missing how important differences in movement behaviour and foraging strategies can facilitate sympatric species coexistence. Such information is particularly important for vultures – obligate scavengers representing the most endangered avian foraging guild. Their loss from ecosystems can trigger trophic cascades, mesopredator release and disease outbreaks. We provide the first‐ever analyses of GPS location data from wild King Vultures Sarcoramphus papa and Greater Yellow‐headed Vultures Cathartes melambrotus, coupled with trait data (from both wild‐living and museum specimens) and visitation data from camera traps deployed at provisioned carcasses, to characterize vulture flight behaviour and strategies in the Peruvian Amazon. We found marked species differences in several key movement characteristics, including: King Vultures having home‐ranges five times larger, average flight heights four times greater and ground speeds 40% faster than those of Greater Yellow‐headed Vultures. Despite these differences, both species flew similar distances each day (on average), probably due to King Vultures taking 50% fewer flights and spending 40% less time in the air per day. Consistent with these patterns, King Vulture body mass was more than double that of the Greater Yellow‐headed Vulture, with a substantially larger hang wing index (a measure of long‐distance flight efficiency). At carcasses, Greater Yellow‐headed Vultures typically arrived first but were rapidly outnumbered by both King and Black Vultures Coragyps atratus. We find that the movement behaviour of obligate apex scavengers in the western Amazon is linked to their ability to coexist – Greater Yellow‐headed Vultures, a smaller stature ‘scouting’ species adapted to fly low, forage early and arrive first at carcasses, are ultimately displaced by larger‐bodied, wider ranging King Vultures at large ephemeral carrion resources. Expansion of future GPS tracking initiatives could facilitate the exploration of direct facultative interactions from animal movement data and give further insight into how diverse communities assemble and interact.

Safe tracking control based on a secure boundary protection method for nonlinear systems with unknown initial tracking condition

Aiming to the sudden change of the system actual output constraint, a novel safe tracking control strategy is investigated for a class of non-strict feedback nonlinear systems with unknown initial tracking condition in this paper. A secure boundary protection method (SBPM) based on prescribed performance and a new prescribed finite-time performance function (PFTPF) are proposed. The SBPM can automatically construct two adjustable secure boundaries, so that the system output meets both the control performance prescribed by the PFTPF and the secure boundary constraint. In order to solve the problem of the excessive control input jitter caused by the sudden change of output constraint, a bidirectional filtering smoothing mechanism (BFSM) is proposed. The proposed SBPM can effectively ensure the safe tracking control of the system with unknown initial tracking condition when the actual output constraint changes suddenly, and ensure that the system has the prescribed finite-time control performance given by the PFTPF. Differently from the existing works, the system output will be strictly constrained into the actual output constraint without the prediction for the system output value. Finally, the effectiveness of this proposed method is verified by simulations.

Thermal Image Tracking for Search and Rescue Missions with a Drone

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is utilized to detect people in thermal videos. Multiple-target tracking is performed via track initialization, maintenance, and termination. Position measurements in two consecutive frames initialize the track. Tracks are maintained using a Kalman filter. A bounding box gating rule is proposed for the measurement-to-track association. This proposed rule is combined with the statistically nearest neighbor association rule to assign measurements to tracks. The track-to-track association selects the fittest track for a track and fuses them. In the experiments, three videos of three hikers simulating being lost in the mountains were captured using a thermal imaging camera on a drone. Capturing was assumed under difficult conditions; the objects are close or occluded, and the drone flies arbitrarily in horizontal and vertical directions. Robust tracking results were obtained in terms of average total track life and average track purity, whereas the average mean track life was shortened in harsh searching environments.

Tunnel Prescribed Control of Nonlinear Systems With Unknown Control Directions.

This article solves the entry capture problem (ECP) such that for any initial tracking error, it can be regulated into the prescribed performance constraints within a user-given time. The challenge lies in how to remove the initial condition limitation and to handle the ECP for nonlinear systems under unknown control directions and asymmetric performance constraints. For better tracking performance, we propose a unified tunnel prescribed performance (TPP) providing strict and tight allowable set. With the aid of a scaling function, error self-tuning functions (ESFs) are then developed to make the control scheme suitable to any initial condition (including the initial constraint violation), where the initial values of ESFs always satisfy performance constraints. In lieu of the Nussbaum technique, an orientation function is introduced to deal with unknown control directions while such way is capable of reducing the control peaking problem. Using ESFs, together with TPP and an orientation function, the resulted tunnel prescribed control (TPC) leads to a solution for the underlying ECP, which also exhibits a low complexity level since no command filters or dynamic surface control is required. Finally, simulation results are provided to further demonstrate these theoretical findings.

Pharmacological Properties of Shionone: Potential Anti-Inflammatory Phytochemical against Different Diseases.

Shionone is a triterpenoid that is the primary constituent of an important ancient Chinese medicine named Radix Asteris. It has emerged as an attractive candidate against different important diseases, including interstitial cystitis, colitis, cancer, Parkinson's disease, and urinary tract infections, and was found to have a protective effect on multiple organs, including the colon, kidneys, lungs, brain, and bladder. The anti-inflammation activity of shionone may be considered an important property that imparts the positive health outcomes of shionone. Important molecular targets and markers such as TNF-α, STAT3, NLRP3, and NF-κB were also found to be targeted by shionone and were verified in different diseases. This suggests the possible potential of shionone against other diseases associated with these targets. Pharmacokinetic studies also support the therapeutic potential of shionone and provide the initial track that may be pursued for its development. Yet, the compilation of the pharmacological activities of shionone and its important genes and pathway targets are absent in the existing literature, which would direct its development as a therapeutic and/or supplement. Hence, the present review provides a compilation of information concerning pharmacological activities, highlights the existing holes, and proposes a specific direction for the expansion of shionone as a therapeutic against different diseases and conditions.

An Improved Range-Searching Initial Orbit-Determination Method and Correlation of Optical Observations for Space Debris

The Changchun Observatory of the National Astronomical Observatories, Chinese Academy of Sciences, and the Shanghai Astronomical Observatory are used to generate very short arc (VSA) angle observations of objects in low Earth orbit (LEO) and geostationary orbit (GEO) with their ground-based electrical–optical telescope arrays (EA), the Changchun EA and SAO FocusGEO, respectively. These observations are used in this paper. The range-searching (RS) algorithm for initial orbit determination (IOD) is improved through the multiple combinations of observations and the dynamic range-searching step length. Two different computation modes (the normal mode and the refining mode) of the IOD computation process are proposed. The geometrical method for the association is used. The IOD and association methods are extended to the real optical observations for both LEO and GEO objects. The results show that the IOD success rate of arcs from the LEO objects is about 91%, the error of the semimajor axis (SMA) of the initial orbital elements is less than 50 km, and the correlation accuracy rate is about 89%. The IOD success rate of arcs from the GEO objects is higher than 88%, and the correlation accuracy rate is greater than 87%. The recent COSMOS 1408 antisatellite test (ASAT) generated a large amount of debris. The algorithm of this paper and the observations of Changchun EA are used to initially identify new debris, possibly from the ASAT through initial orbit determination and track association. Finally, 64 suspected new pieces of debris can be found. The results show the effectiveness of the IOD and the correlation algorithm, as well as the potential application of the optical–electrical array in studying space events.

End-to-end validation of fiducial tracking accuracy in robotic radiosurgery using MRI-only simulation imaging.

Image-guided radiation-therapy (IGRT)-based robotic radiosurgery using magnetic resonance imaging (MRI)-only simulation could allow for improved target definition with highly conformal radiotherapy treatments. Fiducial marker (FM)-based alignment is used with robotic radiosurgery treatments of sites such as the prostate because it aids in accurate target localization. Synthetic CT (sCT) images are generated in the MRI-only workflow but FMs used for IGRT appear as signal voids in MRIs and do not appear in MR-generated sCTs, hindering the ability to use sCTs for fiducial-based IGRT. In this study we evaluate the fiducial tracking accuracy for a novel artificial fiducial insertion method in sCT images that allows for fiducial marker tracking in robotic radiosurgery, using MRI-only simulation imaging (MRI-only workflow). Artificial fiducial markers were inserted into sCT images at the site of the real marker implantation as visible in MRI. Two phantoms were used in this study. A custom anthropomorphic pelvis phantom was designed to validate the tracking accuracy for a variety of artificial fiducials in an MRI-only workflow. A head phantom containing a hidden target and orthogonal film pair inserts was used to perform end-to-end tests of artificial fiducial configurations inserted in sCT images. The setup and end-to-end targeting accuracy of the MRI-only workflow were compared to the computed tomography (CT)-based standard. Each phantom had six FMs implanted with a minimum spacing of 2cm. For each phantom a bulk-density sCT was generated, and artificial FMs were inserted at the implantation location. Several methods of FM insertion were tested including: (1) replacing HU with a fixed value (10000HU) (voxel-burned); (2) using a representative fiducial image derived from a linear combination of fiducial templates (composite-fiducial); (3) computationally simulating FM signal voids using a digital phantom containing FMs and inserting the corresponding signal void into sCT images (simulated-fiducial). All tests were performed on a CyberKnife system (Accuray, Sunnyvale, CA). Treatment plans and digital-reconstructed-radiographs were generated from the original CT and sCTs with embedded fiducials and used to align the phantom on the treatment couch. Differences in the initial phantom alignment (3D translations/rotations) and tracking parameters between CT-based plans and sCT-based plans were analyzed. End-to-end plans for both scenarios were generated and analyzed following our clinical protocol. For all plans, the fiducial tracking algorithm was able to identify the fiducial locations. The mean FM-extraction uncertainty for the composite and simulated FMs was below 48% for fiducials in both the anthropomorphic pelvis and end-to-end phantoms, which is below the 70% treatment uncertainty threshold. The total targeting error was within tolerance (<0.95mm) for end-to-end tests of sCT images with the composite and head-on simulated FMs (0.26, 0.44, and 0.35mm for the composite fiducial in sCT, head-on simulated fiducial in sCT, and fiducials in original CT, respectively. MRI-only simulation for robotic radiosurgery could potentially improve treatment accuracy and reduce planning margins. Our study has shown that using a composite-derived or simulated FM in conjunction with sCT images, MRI-only workflow can provide clinically acceptable setup accuracy in line with CT-based standards for FM-based robotic radiosurgery.

Event-triggered dynamic prescribed performance control for oxygen stoichiometry and stack temperature in PEM fuel cell systems with actuators faults

Oxygen stoichiometry (OS) and stack temperature (ST) significantly affect the output performance and durability of proton exchange membrane fuel cells. This paper investigates the prescribed performance tracking control strategy of OS and ST under external disturbances, modeling errors, actuators faults and limited communication resources. The total disturbances are compensated by a variable-bandwidth extended state observer to simplify the control design and enhance the system robustness. To tolerate excessive disturbances and relax the strict requirement on the initial conditions, a new event-triggered adaptive sliding mode controller that integrates modified dynamic performance constraints and nonsingular error transformation is developed to ensure the user-designed transient and steady-state performance constraints, while saving the communication costs and avoiding the chattering and jump in control signals. The numerical validation results under combined working scenarios indicate that the proposed controller always ensures the preset error constraints and possesses superior performance in tracking the optimal OS value 2 and the optimal ST value 353.15K than other three representative controllers, with the integral absolute errors of 0.732 for OS tracking and 12.94 for ST tracking; besides, it achieves satisfactory robustness to uncertain model parameters and relatively large initial tracking errors.

Unusual tracks: Statistical, controlling factors and model prediction

The progress of research and forecast techniques for tropical cyclone (TC) unusual tracks (UTs) in recent years is reviewed. A major research focus has been understanding which processes contribute to the evolution of the TC and steering flow over time, especially the reasons for the sharp changes in TC motion over a short period of time. When TCs are located in the vicinity of monsoon gyres, TC track forecast become more difficult to forecast due to the complex interaction between the TCs and the gyres. Moreover, the convection and latent heat can also feed back into the synoptic-scale features and in turn modify the steering flow. In this report, two cases with UTs are examined, along with an assessment of numerical model forecasts. Advances in numerical modelling and in particular the development of ensemble forecasting systems have proved beneficial in the prediction of such TCs. There are still great challenges in operational track forecasts and warnings, such as the initial TC track forecast, which is based on a poor pre-genesis analysis, TC track forecasts during interaction between two or more TCs and track predictions after landfall. Recently, artificial intelligence (AI) methods such as machine learning or deep learning have been widely applied in the field of TC forecasting. For TC track forecasting, a more effective method of center location is obtained by combining data from various sources and fully exploring the potential of AI, which provides more possibilities for improving TC prediction.

Event-Triggered Tracking Control for a Class of Nonlinear Systems: A Dynamic Gain Approach

This note presents a dynamic gain approach to the event-triggered tracking control for a class of strict-feedback nonlinear systems. A novel shifting function is designed to make the initial tracking error arbitrarily bounded, which significantly relaxes the requirement on the initial tracking error. To achieve the objective that the tracking error does not exceed the constraint after the pre-specified finite time, a barrier Lyapunov function and a new reduced-order observer are constructed by using the shifting function and the desired tracking signal. Based on the proposed dynamic gain approach, an event-triggered output feedback tracking controller is designed. It is proved that the developed control scheme guarantees that all the closed-loop signals are bounded and the tracking error constraint is strictly obeyed after a specified finite time. Compared with the celebrated backstepping method, the dynamic gain approach does not involve any virtual control variables, which avoids the complicated iterative procedure. The effectiveness of the proposed approach is verified by an example.

Prescribed Performance Fault-Tolerant Control of Strict-Feedback Systems via Error Shifting.

This article investigates the prescribed performance control (PPC) problem for a class of nonlinear strict-feedback systems with sensor/actuator faults. A shifting function is introduced to modify the output tracking error generated by the practically measured system state, based on which an improved PPC method is proposed to achieve the convergence of output tracking error to the prescribed region, and this convergence is shown to be independent of the initial tracking condition and insusceptible to sensor/actuator faults. The faults-induced uncertainties together with the nonlinear dynamics are compensated by involving a radial basis function neural network (RBFNN) to make the controller robust adaptive fault-tolerant without prior knowledge of fault coefficients. Via Lyapunov stability analysis, it is proven that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. The effectiveness and superiority of the method are demonstrated by two simulation examples.

High-Speed Videogrammetry with Mutually Guided Target Tracking under Occlusion for Masonry Building Structure Displacement on a Shaking Table

High-speed videogrammetric measurements are widely used on shaking tables. However, during progressive collapse experiments, the protective string used to ensure the safety of personnel and the shaking table, mandated by safety considerations, can partially occlude the structural model. To address the problem of inaccurate tracking of the ellipse targets in image sequences due to the partial occlusion, this paper proposes a novel mutually guided tracking method for the partial occlusion situations. Firstly, the strategy of loopback detection is proposed to eliminate the cumulative errors with the initial tracking model and to replace the initial results of the tracking with those from the loopback detection. Secondly, tiny offset compensation is used to solve the problem of deviations. The experimental results demonstrate that the proposed method can achieve single-point localization at the sub-millimeter level and interlayer localization at the millimeter level within partially occluded environments. It is important that the proposed method meets the requirements of experimental accuracy on shaking tables and ensures the safety of personnel and facilities.

Parallel-in-time multiple shooting for optimal control problems governed by the Navier–Stokes equations

In the past decades, multiple shooting methods have proven to be a promising direction to speed up the optimization process, especially in the context of ODE-based optimization. Very recently, Fang et al. (Journal of Computational Physics, vol. 452, 110926, 2022) proposed a multiple shooting algorithm for large-scale PDE-constrained optimization. The current paper continues this line of work and explores the potential of multiple shooting methods for optimal control problems governed by the three-dimensional Navier–Stokes equations. Similar to Fang et al., the augmented Lagrangian (AL) method is used to solve the resulting equality-constrained optimization problem, and we employ the classical limited-memory BFGS method for the unconstrained subproblems inside the AL loop. In the current work, we exploit the multiple shooting paradigm in full by processing the shooting windows parallel-in-time, allowing for significant parallel speed-ups compared to single shooting. The proposed method is validated on a velocity tracking case, using up to 100 windows. Our analysis shows that the multiple shooting algorithm allows for considerable algorithmic and parallel speed-ups. While algorithmic speed-up depends on the exact tracking case and initialization of the shooting windows, the multiple shooting algorithm always outperforms single shooting in terms of computational time (if the number of windows is sufficiently high) due to the parallel-in-time implementation. For a given amount of resources, we also show that the proposed parallel-in-time strategy can outperform spatial parallelization alone, especially when the spatial parallelization is saturated.

Adaptive predefined-time fault-tolerant attitude tracking control for rigid spacecraft with guaranteed performance

In this article, the attitude tracking problem for rigid spacecraft systems with the consideration of actuator fault, input saturation, inertia uncertainties. and external disturbances is investigated. By introducing a shifting function, the requirement for accurate values of the initial tracking errors has been removed. A novel adaptive prescribed performance function is developed to enable the relaxation and recovery of the performance bounds under the different conditions of the shifted tracking error. Then, an error transformation is employed to convert the problem of prescribed performance tracking into the stabilization problem of a new system. Subsequently, an adaptive controller is designed based on a nonsingular predefined-time sliding mode manifold and an auxiliary system. The proposed controller ensures a predefined-time convergence with prescribed static and transient performance for the tracking error. The stability analysis of the proposed attitude controller is rigorously established using Lyapunov methods. Finally, numerical simulations are conducted to exhibit the efficacy and superiority of the propounded scheme.