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As for a class of strict‐feedback nonlinear systems with simultaneous sensor and actuator faults, the article focuses on the problem of fixed‐time fault‐tolerant control. Both fixed‐time command filters and a compensation mechanism are utilized to cope with these issues of differential explosion and filter errors. Meanwhile, through the fusion of fixed‐time control, fuzzy logic systems, adaptive control and the backstepping technology, a n$$ n $$‐step design scheme is presented. Our proposed controller ensures the fixed‐time convergence of all the signals in the system even with sensor/actuator faults, and allows the designers to acquire the upper limit value of the fixed convergence time only by tuning the design parameters. And this scheme takes into account rapidity, adaptability and fault tolerance of the system simultaneously. Based on the numerical simulation analysis, it is shown that the better tracking performance can be achieved. So it is concluded that our strategy is effective. [ABSTRACT FROM AUTHOR]

This paper mainly concerns on the tracking control problem for event‐triggered networked control systems (NCSs) with diversified cyber‐attacks. An event‐triggered mechanism (ETM) is exploited to decrease the amount of redundant transmissions, thus saving communication resources. In addition, a novel tracking error model for NCSs is built with consideration of ETM and diversified cyber‐attacks, which involves deception attacks and Denial‐of‐Service (DoS) attacks. On the basis of Lyapunov stability theory and linear matrix inequalities (LMIs), the stability criterion of the tracking error system is constructed, and the controller is designed to ensure the tracking performance of the system. In the end, simulation examples are provided to testify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The major contribution of this study is the feedback design of a finite-time convergence sliding mode control to solve the trajectory-tracking problem in a class of mechanical systems. Some advantages are that the controller presents a continuous signal by integration of the high-frequency switching term. Another benefit is the design and implementation of an uncertainty and disturbance estimator (UDE) to robustify the closed-loop system. We use Lyapunov tools to develop the closed-loop stability analysis and to give an expression of the convergence time t = t through this, we can reduce the convergence time by tuning the gains of the controller. We illustrate the performance of the proposed control structure via numerical simulations conducted for a mass-spring-damper system and experiments developed in a pendular system. [ABSTRACT FROM AUTHOR]

Time‐varying group formation tracking control problems for multi‐agent systems are investigated based on distributed multi‐sensor multi‐target filtering with intermittent observations. First, in order to estimate the states of multiple targets under the phenomenon of intermittent observations accurately, a distributed multi‐sensor multi‐target filtering algorithm is proposed based on cubature Kalman filtering. Second, a time‐varying group formation tracking protocol is designed for multi‐agent systems by using the state estimations obtained from the filtering algorithm and the neighboring interaction. The protocol enables multi‐agent systems to form time‐varying subformations and track multiple targets in the same subgroups, respectively. Third, the boundedness of the error covariance matrices is proved under the condition that the observation probability is higher than the minimum threshold. Then the estimation errors of the filtering algorithm are proved to be stochastically bounded by introducing a stochastic process. Furthermore, the boundedness of the group formation tracking errors is proved. Finally, a numerical example is used to verify the performance of the proposed algorithm and protocol. [ABSTRACT FROM AUTHOR]

An innovative technique for developing fuzzy preview tracking control nonlinear systems using Takagi‐Sugeno (T‐S) fuzzy models is discussed. The design considers robustness against state time‐changing latency, input saturation, and reference tracking. To achieve this, the T‐S fuzzy system was combined with time‐changing latency and input saturation to create an augmented error system, transforming the original fuzzy preview tracking control issue into a stability issue for the augmented error systems. Next, a new fuzzy preview tracking controller was developed by considering the T‐S fuzzy systems' states or outputs, a distinct integrator, and a previewed reference signal to solve the tracking control issue. For the augmented error system's asymptotic stability, new adequate conditions were derived by employing the fuzzy Lyapunov function, small gain theory, and linear matrix inequality (LMI) method. Finally, the proposed method's effectiveness was demonstrated using two numerical examples. [ABSTRACT FROM AUTHOR]

This study investigates fault-tolerant consensus tracking for discrete-time multi-agent systems (MASs) subject to external eavesdropping threats and additive actuator faults. First, actuator faults are modeled by difference equations, and decentralized observers are constructed to estimate actuator faults as well as system states. To offset fault-induced effects, ensure secure communication, and alleviate communication congestion, neighboring encrypted state information based on the encryption–decryption strategy (EDS) and estimated fault are integrated into a distributed active fault-tolerant consensus tracking control (FCTC) protocol. Through the properties of compatible norms, criteria for the controller, observer, and dynamic encryption key in EDS are derived to achieve leader-following consensus (LFC) of MASs with bias and drift actuator faults. Simulation results confirm the validity of the encryption–decryption-based distributed FCTC strategy. • The design of consensus tracking control protocols combine privacy preservation and fault-tolerant control technologies when multi-agent systems suffer from cyber and physical security problems. • A sufficient condition guarantees the performance of leader-following consensus tracking and serves as a criterion for designing the controller, observer, and symmetric encryption key. • A new modified Riccati-based controller design method separates the graph properties from the feedback design problem. [ABSTRACT FROM AUTHOR]

In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional–Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems. [ABSTRACT FROM AUTHOR]

This paper studies the stability of stochastic nonlinear time‐delay systems with arbitrary switching and its application. A novel Krasovskii‐type stability theorem is introduced for stochastic nonlinear systems featuring arbitrary switching and time‐delay. Unlike previous results, this stability result removes the restriction that the infinitesimal generator of Lyapunov–Krasovskii functional (i.e., LV$$ \mathcal{L}V $$) must be negative definite and allows LV$$ \mathcal{L}V $$ to be indefinite. As an application, the tracking control is studied for a class of stochastic switched systems with inverse dynamics and time‐delay. The assumption condition imposed on the inverse dynamic subsystem is weaker compared with most of existing findings. Finally, we illustrate the feasibility and effectiveness of proposed control strategy by two simulation examples. [ABSTRACT FROM AUTHOR]

To maintain the vibrations of the gyroscope proof mass, an adaptive super-twisting sliding mode control (STSMC) of a micro gyroscope based on a two-loop recursive fuzzy neural network (TLRFNN) is designed. In order to estimate the unknown parameters of the nonlinear system online, an adaptive technology is adopted based on the parameter linearization of the nonlinear model. In response to the problem of system chattering caused by ordinary sliding mode control, the STSMC with the advantages of high order sliding mode and conventional sliding mode is introduced. Aiming at the problem of uncertainty in system parameters, a two-loop recursive fuzzy neural network approximation with the competence over the storage of a priori information is utilized. All adaptive laws are obtained under the Lyapunov stability framework which contributes to the stability of the system. The simulation research shows the system exhibits good tracking performance and maintains a small tracking error under the control of the suggested control system. The effect is measured by calculating the root mean square error (RMSE) parameter of the tracking error and the proposed control system achieves a tracking effect of and in the x and y directions, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper investigates the event‐triggered finite‐time trajectory tracking controller based on a type of chained‐form nonholonomic systems under unknown disturbances by utilizing relay switching technology. A key design idea is that the entire control design is grouped into two separated control stages to produce different finite‐time tracking controllers with correspondingly event‐triggered control rules. By using some nonlinear design methods, a relay switching–based event‐triggered finite‐time tracking control method is adopted to assure that the resulting closed‐loop tracking error system converges to an arbitrarily small neighborhood around zero within a finite time. All closed‐loop error system states keep bounded throughout the entire process. Simulation and experiment results demonstrate the rationality of the designed tracking control strategy. [ABSTRACT FROM AUTHOR]

This paper studies the formation tracking problem of the so-called open multiagent systems. In these systems, agents can freely join or leave at any time, i.e. the system sizes are changeable. To achieve formation tracking control in these systems, we propose a general theoretical analysis framework that consists of a formation adjustment strategy and a formation tracking protocol. Therein, the former is used to adjust the predefined task-oriented formation shape to the varying size of the system. This strategy is built on the existing scalable self-healing idea and then modified by a size estimation method. The latter is used to drive the present followers to form the adjusted formation and track a leader. To accommodate undirected and directed graphs, two distributed formation tracking protocols are designed respectively. Due to possible agent migration, the designed tracking protocols are endowed with self-adaptive ability, i.e. without depending on global topology information. Then, their convergence properties are analysed using the Lyapunov theory. Finally, two simulations are presented to verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

ABSTRACT This article pays attention to the obstacle avoidance trajectory tracking control problem for uncertain Euler–Lagrange (EL) systems subject to velocity constraints and input saturation. The existing obstacle avoidance results do not consider velocity constraints under input saturation, which means that an EL system may not be able to obtain sufficient control inputs to avoid a collision with an obstacle if it has a high speed when approaching the obstacle. Therefore, the velocity constraints in the obstacle avoidance tracking control are considered in this paper. A novel velocity constraint function that depends on the distance between the system and the obstacle is proposed. Integral‐multiplicative Lyapunov‐barrier functions (LBFs) are constructed and incorporated into the backstepping procedure to design an adaptive fuzzy obstacle avoidance tracking control scheme. Moreover, an auxiliary dynamic system is designed by constructing a bounded nonlinear vector related to an auxiliary variable to compensate for the effects of saturation. Through the Lyapunov method and boundedness analysis for the barrier function, it is shown that the protocol achieves obstacle avoidance for the EL system without violating the velocity constraints inside the obstacle detection region, while also guaranteeing the ultimate uniform boundedness of all the closed‐loop signals. Numerical simulations are presented to demonstrate the efficacy of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents an improved nonsingular adaptive super twisting sliding mode control for tracking of a quadrotor system in the presence of external disturbances and uncertainty. The initial step involves developing a dynamic model for the quadrotor that is free from singularities, achieved through the utilization of the Newton-Quaternion formalism. Then, the super twisting algorithm is used to develop a novel sliding mode control that mitigates chattering. Particle Swarm Optimization (PSO) is employed for the adjustment of the controller gains. Moreover, to maintain stable control of the quadcopter, even in scenarios where the upper limit of disturbances is unknown, an adaptive rule grounded in Lyapunov stability is applied. Simulation results demonstrate that the proposed controller reduces tracking errors to 0.1% for roll, 0.05% for pitch, and 2.2% for altitude, outperforming other state-of-the-art sliding mode controllers. Additionally, the proposed controller effectively rejects disturbances, maintaining minimal steady-state errors of 0.01° for roll, 0.02° for pitch, and 0.001° for yaw, significantly better than conventional controllers. These results highlight tracking and disturbance rejection capabilities of the proposed controller, making its real-time implementation for quadrotor Unmanned Aerial Vehicles (UAVs) feasible. [ABSTRACT FROM AUTHOR]

In this article, the problem of adaptive finite‐time tracking control for a class of nonlinear systems subject to backlash‐like input hysteresis and multiple objective constraints is investigated. For the purpose of realizing multiple objective constraints, a new time‐varying barrier function (TVBF) is introduced to ensure that all objective constraint functions are always within the defined range. Meanwhile, based on the combination of the command filter approach and adaptive backstepping control, an error compensation system (ECS) is supplied to reduce the impact of filter error on control performance. Additionally, the problem of "singularity" caused by hysteresis is avoided by linearizing the backlash‐like hysteresis model, and Nussbaum‐type function is also applied to reduce the influence of hysteresis on the stability of the system. Then, by combining multi‐dimensional Taylor network (MTN) technology and command filter backstepping approach, an adaptive finite‐time control strategy is designed. The proposed control strategy ensures that all the signals in the closed‐loop system realize finite‐time semi‐globally uniformly ultimately bounded (SGUUB), and the output signal of the system can track the reference signal greatly while adhering to multiple objective constraints. Finally, the effectiveness of the proposed control strategy is verified by a practical simulation example. [ABSTRACT FROM AUTHOR]

In this study, the problem of fixed‐time formation tracking for multi‐spacecraft systems without internal collisions was investigated. Aiming to ensure that the formation members can accurately realize and maintain the required configuration within the user‐given time, we designed a novel adaptive immersion and invariance (I&I)‐based control protocol. The novelty here lies in two aspects. First and foremost, this study was based on the adaptive I&I technique, combined with a new artificial potential function, to achieve the desired formation tracking without internal collision. Second, unlike the asymptotic convergence of the traditional I&I‐related works, to guarantee the fixed‐time stability, the proposed protocol introduced the prescribed performance control, which can also alleviate the possible system performance degradation caused by the non‐periodic control signal update of the event‐triggered mechanism. Further, the introduction of the event‐triggered mechanism can reduce the unnecessary information interaction. Lyapunov stability analysis shows that this proposed protocol can enable the defined implicit manifold to converge to the origin for the most initial conditions. Also, benefiting from the prescribed performance techniques, the convergence time can eliminate the dependence of the system on designed controller parameters or initial system conditions, relying only on the actual mission requirements. In addition, we adopted a linear extended state observer to deal with the parameter uncertainties and external disturbances, and used the I&I adaption to estimate the observer errors, thus further improving the system performance. Moreover, a new exponential‐type artificial potential function was designed to avoid close proximity between formation members and prevent internal collisions. Finally, numerical simulations were conducted to verify the theoretical results. [ABSTRACT FROM AUTHOR]

We consider the problem of tracking arbitrary free nutating trajectories of the Lagrange top using linear feedback. The equations of motion of the top are linearised about the desired trajectories. Due to the inherent symmetries, this yields a system of linear equations with periodically varying coefficients governing the error dynamics. It is shown that the higher-order dynamics of the Lagrange top leads to nuances in the implementation of the control. Two different control strategies are implemented in the present work. These are applicable for impulsive and short-duration disturbances, respectively. The control effort is in the form of a body-fixed torque input about the symmetry axis and a vertical space-fixed force input. In both the proposed control strategies, the iterative application of the control effort over multiple periods is shown to stabilise the error. These strategies are shown to work for significantly large errors. [ABSTRACT FROM AUTHOR]

This contribution examines the control problem for very large wind energy converters during shutdown operation and analyses the most important control approaches. The control methods make use of the built-in conventional control infrastructure, but control system reconfigurations are undertaken in order to meet the demands of the shutdown control operation. Hence, the torque controller as well as the collective pitch controller (CPC) are redesigned from their regulator functions to reference tracking control systems with constraints. In addition, the CPC is combined with a feedforward controller in order to gain responsiveness. Constraints in magnitude and rate are managed by a modified anti-windup mechanism. Simulations of a 20 MW reference wind turbine verify the performance of the approaches. [ABSTRACT FROM AUTHOR]

The tip–tilt mirror (TTM) is an important component of adaptive optics (AO) to achieve beam stabilization and pointing tracking. In many practical applications, the information of accurate TTM dynamics, complete system state, and noise characteristics is difficult to achieve due to the lack of sufficient sensors, which then restricts the implementation of high precision tracking control for TTM. To this end, this paper proposes a new method based on noisy-output feedback Q-learning. Without relying on neural networks or additional sensors, it infers the dynamics of the controlled system and reference jitter using only noisy measurements, thereby achieving optimal tracking control for the TTM system. We have established a modified Bellman equation based on estimation theory, directly linking noisy measurements to system performance. On this basis, a fast iterative learning of the control law is implemented through the adaptive transversal predictor and experience replay technique, making the algorithm more efficient. The proposed algorithm has been validated with an application to a TTM tracking control system, which is capable of quickly learning near-optimal control law under the interference of random noise. In terms of tracking performance, the method reduces the tracking error by up to 98.7% compared with the traditional integral control while maintaining a stable control process. Therefore, this approach may provide an intelligent solution for control issues in AO systems. [ABSTRACT FROM AUTHOR]

In this article, an adaptive switching controller is designed for the stabilization control and the tracking control of nonlinear systems suffering from full state constraints, unknown control directions, and system uncertainties. Featured with changing signs and unbounded increasing magnitudes, a switching control structure is proposed to deal with the unknown control direction problem. In order to explore and finally fix the correct control direction, an adaptive switching rule, a switching function, and a reference function are constructed. An integral Lyapunov function is adopted for the design of the control law, which ensures that full state constraints will not be violated. Using this method, the nonlinear system can be properly controlled without extra estimations or approximations. The boundedness of all the states and the stability of the system are analyzed and guaranteed. Finally, simulations are conducted to validate the proposed method, and the results illustrate its effectiveness. [ABSTRACT FROM AUTHOR]

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of language models in software engineering tasks, promoting their use in production environments and ensuring that deployed software is defect-free efficiently. [ABSTRACT FROM AUTHOR]

The overtaking process for autonomous vehicles must prioritize both efficiency and safety, with safe distance being a crucial parameter. To address this, we propose an automatic overtaking path planning method based on minimal safe distance, ensuring both maneuvering efficiency and safety. This method combines the steady movement and comfort of the constant velocity offset model with the smoothness of the sine function model, creating a mixed-function model that is effective for planning lateral motion. For precise longitudinal motion planning, the overtaking process is divided into five stages, with each stage's velocity and travel time calculated. To enhance the control system, the model predictive control (MPC) algorithm is applied, establishing a robust trajectory tracking control system for overtaking. Numerical simulation results demonstrate that the proposed overtaking path planning method can generate smooth and continuous paths. Under the MPC framework, the autonomous vehicle efficiently and safely performs automatic overtaking maneuvers, showcasing the method's potential to improve the performance and reliability of autonomous driving systems. [ABSTRACT FROM AUTHOR]

This article describes a control approach for obtaining predefined‐time robust tracking in multiplicative systems despite positive, bounded, and unknown multiplicative disturbances. The proposed approach is distinguished by imposing predefined‐time convergence, a topic previously studied in conventional calculus in the context of multiplicative systems. Multiplicative calculus is recognized as a beneficial tool that complements standard calculus by simplifying the modeling and comprehension of numerous processes. Simulations are carried out to illustrate that the given control strategy enforces convergence before a predefined time instant and, while inducing robustness against system uncertainties. The findings of this article pave the way for further research into predefined‐time synchronization of multiplicative oscillator systems, which would bring promising implications for data encryption and secure communication. [ABSTRACT FROM AUTHOR]

In this study, it is considered that the question of adaptive tracking control of nonlinear systems containing unknown time‐varying parameters. To optimize the control effect, a fixed‐time control strategy is adopted, in which the upper bound of the settling time is connected with the designed parameters, but not to the initial conditions. In the design, the unknown time‐varying parameters are treated as the multiplication of known and unknown vectors, and the adaptive laws are constructed to estimate the mean value of the components in the unknown ones, which availably solve the difficulty of unknown control gains. Besides, the application of command filtered technique, which combined with the compensation signals containing the adaptive parameters, effectively avoids the "complexity explosion" problem. Further, Lyapunov stability theory verifies that the tracking error can always be kept within the boundaries of the performance functions, and converges to a designated neighbourhood of the origin within a fixed time, beyond that all signals of the closed‐loop system are bounded. Finally, the simulation results prove the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Summary: This article delves into the adaptive heading tracking control of unmanned surface vehicle (USV) by incorporating an event‐triggered mechanism and signal quantization. The primary objective is to save communication resources while alleviating the burden of signal transmission. To address time‐varying external perturbations inherent in the control system, a disturbance observer is employed for precise estimation. Additionally, a linear model is introduced to delineate the procedure of quantization. By furnishing the controller with purpose‐designed quantized control input, the adaptive tracking control system can effectively track desired input without requiring any prior knowledge of the quantized parameters. The article substantiates its claims by demonstrating the system's stability in the absence of quantization considerations and the bounded nature of quantization errors through a series of presented lemmas. Further, the stability of the USV heading control system, integrated with an event‐triggered mechanism and signal quantization, is proofed in accordance with Lyapunov stability theory. Finally, the proposed strategy's efficacy and practical applicability are validated through experimental simulations. [ABSTRACT FROM AUTHOR]

This paper studies the approximate optimal event-triggered tracking control problem for a class of second-order nonlinear systems with prescribed performances. By employing a prescribed performances function, restrictions on tracking errors are removed for prescribed performances including convergence rate, maximum overshoot, and steady-state error. On this basis, a complex optimal tracking control problem can be transformed into an optimal stabilising control problem by constructing an augmented system. Subsequently, an event-triggered mechanism is introduced to reduce communication resources. Then, an adaptive critic learning algorithm is used to solve the Hamilton-Jacobi-Bellman equation, where the weights in the critic networks are tuned through the gradient descent approach and experience replay technology. Through the developed learning method, the persistence of excitation condition is released, and the data efficiency is improved. The Lyapunov stability theory is introduced to verify the stability of the close-loop system and the tracking errors are uniformly ultimately bounded. Finally, simulation results are presented to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article delves into the intricate challenge of implementing prescribed-time command filtered control in the context of uncertain nonlinear systems. Firstly, a prescribed-time function is defined to lay the groundwork for subsequent controller design. Subsequently, a novel prescribed-time command Filtered controller is proposed for high-order nonlinear systems featuring unknown parameters. This controller guarantees swift error convergence within a predefined time range, with the added capability of periodic error convergence to zero during subsequent controller operations. A pivotal innovation in this study lies in the controller's design, which remains unaffected by the system's initial conditions. This unique feature enables the prescribed time to be flexibly set within physical constraints, diverging markedly from conventional finite-time control theory. Theoretical analysis has conclusively shown that the controller achieves full-state tracking error convergence within the specified time frame. The efficacy of the research findings is substantiated through two simulation cases, underscoring a substantial contribution to the refinement and adaptability of nonlinear system control theory. • This article introduces a Prescribed Time Command Filtered Controller (PTCFC) for a specific class of uncertain high-order nonlinear systems with unknown parameters. • Through the design of a novel periodic finite-time function, the article achieves periodic asymptotic convergence of the control system tracking error. • The article proposes a novel compensation signal design scheme and combines it with a controller to achieve stability analysis of the control system and strict proof of periodic error convergence. [ABSTRACT FROM AUTHOR]

To meet the high-performance trajectory tracking requirements of underactuated autonomous underwater vehicle (AUV) under complex disturbances and parameter uncertainty, a three-dimensional trajectory tracking controller method with predefined dynamic performance and convergence time is proposed. Firstly, by extending the forward position channel of the underactuated AUV, and a multi-input multi-output trajectory tracking control-oriented model is developed. Subsequently, by combining dynamic process functions with predefined-time control theory, a dynamic performance-predefined trajectory tracking control system is established, which allows the transient quality of AUV trajectory tracking to be determined by dynamic process functions, and the actual convergence time of tracking errors to be predefined by a single control parameter. Finally, to avoid control singularities and the " differential explosion" phenomenon, the controller design incorporates the absolute value correction method and radial basis function neural network (RBFNN) fitting method. Numerical simulation results indicate that the proposed controller significantly improves the disturbance rejection and transient quality of underactuated AUV, achieving fact, smooth, and High-performance trajectory tracking. [ABSTRACT FROM AUTHOR]

Interior permanent magnet synchronous motors (IPMSMs) which are driven by matrix converters have many advantages, including one‐stage power conversion, high efficiency, bidirectional power transfer, near unity input power factor, and low input current harmonics. However, the virtual DC‐link voltages of matrix‐converters vary from 0.866Vdc${V}_{dc}$ to Vdc${V}_{dc}$ and the current commutations of matrix‐converters create serious three‐phase stator harmonic currents as well. Moreover, matrix‐converter‐based IPMSM drive systems, in general, have very large external loads, which require advanced control methods. To solve these problems, two adaptive controllers, including a model reference adaptive controller and a self‐tuning controller, are investigated in this article for matrix‐converter PMSM drive systems. From the experimental results, the proposed two adaptive speed controllers provide excellent dynamic responses, which are superior to PI speed controllers, including faster transient responses, better tracking abilities, and greater robustness. As a result, this proposed matrix‐converter‐based IPMSM drive system is very suitable in industrial applications. [ABSTRACT FROM AUTHOR]

An online estimated state feedback control for trajectory tracking in stochastic open quantum systems is proposed in this paper, which is based on the Lyapunov‐based control method. By inducing the error between the controlled state, and the target state as the error state, the trajectory tracking problem of the quantum system is transformed into the error state transition control problem. The quantum state online estimation method QST‐OADM is applied to estimate the state of the error state system online, and the tracking control laws are designed by using the quantum Lyapunov stability theorem for driving the stochastic open quantum system from an arbitrary initial state to an arbitrary trajectory. The numerical simulation experiments and results analyses are given. [ABSTRACT FROM AUTHOR]

This study investigates the performance of designing a Synergetic Control (SC) approach for angular position tracking control of driven-pendulum systems. SC is one of the popular nonlinear control techniques that contributed in a variety of control design applications. This research shows a unique application of the SC for angular position tracking control of driven-pendulum systems. Initially, the equations of motion of the system are developed. Subsequently, the control law of the SC is established. For the stability analysis of the closed loop control system, the Lyapunov Function (L.F) is used. To guarantee optimal performance, a Sparrow Search Optimization (SSO) based approach is presented in order to search for the optimum designing parameters of the controller. For performance comparison, the classical Sliding Mode Control (SMC) is introduced. The simulation's outcomes of the study have been confirmed that the proposed control algorithm is addressed the tracking problem of the angular position of the system successfully. Besides, when an external disturbance is inherited in the simulation, the SC exhibits a robustness performance. Moreover, the performance of the SC is slightly similar as SMC. However, the distinct difference in the performance is that the control signal of the SMC exhibits chattering problem, while this phenomenon is absent in the SC. All computer simulations are carried out using MATLAB software. [ABSTRACT FROM AUTHOR]

Recently, there has been widespread and vital adoption of flexible manipulators due to their increased prevalence. This is attributed to the growing demand for flexibility in various tasks like refueling operations, inspections, and maintenance activities. Nevertheless, these robots are under-actuated systems characterized by a nonlinear behavior and present dynamic coupling interactions that contribute to the complexity of the control process. The main control objective is to achieve an accurate tracking of the desired position while simultaneously reducing oscillations occurring in the link. Therefore, this paper proposes integrating the tuning and adaptive control by employing fuzzy logic methodology in conjunction with internal model control (IMC). The suggested controller takes advantage of intelligent techniques, simple structure, robustness, and easy tuning of the conventional IMC. Both triangular and trapezoidal Membership Functions (MFs) are applied in this study to create a pair of Fuzzy Logic Controllers (FLCs) based on the Mamdani method. These controllers are employed to dynamically adjust the parameters of the IMC, in contrast to the fixed parameters used in the conventional IMC approach. The effectiveness of the suggested Adaptive-based Fuzzy IMC (AFIMC) is showcased through simulation and practical experimentation, in scenarios both with and without disturbances. Results indicate that this technique outperforms conventional IMC in achieving control objectives and rejecting disturbances. [ABSTRACT FROM AUTHOR]

This study introduces a novel adaptive controller employing neural networks, particularly radial basis function (RBF) algorithms, to enhance the control performance of pneumatic artificial muscle (PAM)-based systems. The proposed controller seeks to address the nonlinearities and hysteresis inherent in PAM-based systems by integrating neural approximation. Experimental testing and comparisons with conventional controllers are conducted using an antagonistic configuration of PAMs. The results illustrate the precision and reliability of the proposed controller, suggesting potential for future advancements in trajectory tracking control of PAM-based systems. [ABSTRACT FROM AUTHOR]

A novel controller using fixed‐time sliding mode (FTSM) and fixed‐time disturbance observer (FTDO) is proposed to achieve trajectory tracking control of robotic manipulators. First, the mathematical model is established for robots with dynamic model uncertainties and external disturbances. An FTSM control strategy is presented where the integral terms of position errors are introduced into the existing sliding mode surface (SMS) to reduce the steady‐state error of the system. The exponential form in the integral terms can provide an appropriate control force for tracking systems when the position errors are far from the sliding surface for a long time. Then, an FTDO is designed to obtain a precise estimation of lumped disturbances which can be used to weaken the impact of disturbances on the control accuracy. Finally, the fixed‐time convergence properties of the tracking control system are demonstrated using Lyapunov stability theory. Simulation research is carried out to illustrate that the proposed control strategy has strong robustness and fast‐tracking speed. [ABSTRACT FROM AUTHOR]

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times ( p < 0.001 ). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use ( p = 0.04 ). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements. [ABSTRACT FROM AUTHOR]

In this paper, fast finite-time time-varying formation tracking control for second-order multi-agent systems with external disturbance and region constraints is investigated. The ubiquitous control input response delay is considered. A predictor-based state transformation is employed to obtain the ideal equivalent systems without control input response delay. Considering the difficulty of measuring external disturbance, a fast finite-time observer with the sliding mode technique is proposed to estimate the disturbance, which positively compensates the system. By adopting the sliding mode control strategy, a novel fast finite-time formation tracking controller is proposed. Sufficient conditions for the formation tracking control with region constraints are obtained. Numerical simulations are employed to validate the feasibility and effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Tracking control of the output probability density function presents significant challenges, particularly when dealing with unknown system models and multiplicative noise disturbances. To address these challenges, this paper introduces a novel tracking control algorithm based on reinforce-ment Q-learning. Initially, a B-spline model is employed to represent the original system, thereby transforming the control problem into a state weight tracking issue within the B-spline stochastic system model. Moreover, to tackle the challenge of unknown stochastic system dynamics and the presence of multiplicative noise, a model-free reinforcement Q-learning algorithm is employed to solve the control problem. Finally, the proposed algorithm's effectiveness is validated through comprehensive simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, an improved CKF (Cubature Kalman Filter) target tracking method is adopted to solve the tracking and pointing problem in the field of the Active Denial System. The math model of the system is built and the precision requirement is analyzed. The improved CKF method is input as the feedforward compensation for system control to improve the system tracking performance. In the process of the iterative CKF algorithm, nonlinear means are used. The method makes full use of measurement information and estimates the target velocity acceleration model parameters through the neural network, which is used as the input of the CKF to modify the process parameters of CKF and improve the state estimation accuracy. At the same time, the limited lower bound method is used to ensure that the gain reaches the lower bound bottom line of the precision demand, so that it does not tend to zero with time, so as to avoid affecting its rapid response ability during maneuvering and so that the prediction error is also controlled within the range of the precision demand. The simulation and experimental results show the superiority of the method and make the system fully meet the design requirements. [ABSTRACT FROM AUTHOR]

This article considers the adaptive tracking control problem of a class of strict feedback nonlinear systems with unknown time‐varying parameters. Note that the recent adaptive method, that is, congelation of variables, is built upon the assumption that the known basis functions are zeros as zero states. To remove this assumption, a novel monotonically increasing function is designed to transform the output tracking error and the backstepping tracking errors into increasing forms, which is integrated into the congelation of variables to design the σ$$ \sigma $$‐modification adaptive tracking control law. It is noted that the proposed form of monotonically increasing function is general, which includes positive proportional functions, exponential functions, and logarithmic functions and so on. To yield the boundedness of increasing tracking errors, a barrier Lyapunov function is designed. The asymptotic convergence of the resulting adaptive tracking system is analyzed in detail. Simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This research addresses the tracking problem of least squares adaptive control for a class of nonlinear system with mismatched uncertainties. Different from most of existing solutions, modified predictive model is integrated into the proposed least squares adaptive control architecture. The significant role of modified predictive model in the adaptive control architecture is to achieve smooth transient by filtering out the high‐frequency oscillations, which cannot be canceled out by use of the hypothetical parameterized uncertainty models. Meanwhile, in order to guarantee tracking performance, a generalized restricted potential function (GRPF) is designed to constrain the weighted Euclidean norm of the predictive error of the modified predictive model to be less than a predefined scalar worst‐case bound. Finally, comparative simulations via the generic transport model (GTM) are conducted to examine the effectiveness of the proposed method. The results show that the transient performance and tracking performance of the controlled system can be improved simultaneously by the proposed method. [ABSTRACT FROM AUTHOR]

Summary: The problem of adaptive fuzzy fixed‐time tracking control based on a category of nonlinear systems with unmodeled dynamics and dynamic disturbances is investigated in this article. By introducing the novel fixed‐time dynamic signal, the unmodeled dynamics can be disposed of. On the basis of the fuzzy logic systems (FLSs) and adaptive backstepping technology, the disposing difficulty of the unknown nonlinear portions is reduced. Then, all signals in the closed‐loop system are ensured to be bounded under the fixed‐time Lyapunov stability theory. Simultaneously, the tracking error converges to a small neighborhood of the origin. Eventually, simulation consequences reveal the validity of the presented control method. [ABSTRACT FROM AUTHOR]

In this study, the problem of non-overshooting tracking control (NOTC) for a class of strict-feedback systems with prescribed finite-time (FT) is studied. In order to obtain a simple closed-loop system (CLS), we first propose a criterion lemma for prescribed FT stable control. A prescribed FT controller is designed by using backstepping and the proposed criterion lemma. According to the CLS, many conditions of NOTC are analyzed. Compared with the related results, the NOTC conditions obtained in this paper are relatively extensive, which reduces conservatism. The algorithm makes the system overshoot zero, so this result increases the accuracy. The final simulation results show that this algorithm is effective. • In the related research results of no overshoot control, the closed-loop system obtained by the existing prescribed finite-time control algorithm is very complicated. This leads to the failure to calculate the expression of tracking error through the closed-loop system, and then the control condition with non-overshooting cannot be obtained. Then, a new criterion lemma of prescribed finite-time control is proposed to design the control in this paper. Obviously, our closed-loop system is relatively simple. • In the related research results of non-overshooting control, the non-overshooting tracking control conditions obtained in this paper are relatively extensive, which reduces the conservatism of the existing algorithms. • Compared with mean-nonovershooting tracking control, the algorithm in this paper can make the overshoot of tracking error zero. Therefore, this result increases accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a composite finite-time prescribed performance tracking control scheme is presented for an unmanned helicopter (UH) system subject to performance constraints, model uncertainties and external perturbations. A new finite-time neural network disturbance observer (FTNNDO) with adaptive laws is designed to deal with the external disturbances and model uncertainties, which not only accelerate the convergence rate in finite time but also eliminate the complicated differential calculation in the traditional backstepping scheme. Using the continuous adaptive law, the neural network (NN) approximate errors can be effectively estimated and compensated online without the chattering and gain overestimation caused by traditional methods, thus further enhancing the robustness of the system. To constrain the tracking performance of the transient process and steady-state accuracy, a novel prescribed performance function is designed to preset the tracking errors within prescribed boundaries. Based on the FTNNDO and barrier Lyapunov function (BLF), an improved finite-time tracking controller is designed to achieve fast convergence with prescribed performance. By using Lyapunov synthesis, it is strictly proven that the finite-time convergence of the closed-loop control system can be achieved and tracking errors are always within the prescribed performance bounds. In the end, simulation results for the UH tracking control system are given to demonstrate the effectiveness of developed control scheme. [ABSTRACT FROM AUTHOR]

Coordinating the movements of a robotic fleet using consensus-based techniques is an important problem in achieving the desired goal of a specific task. Although most available techniques developed for consensus- based control ignore the collision of robots in the transient phase, they are either computationally expensive or cannot be applied in environments with dynamic obstacles. Therefore, we propose a new distributed collision-free formation tracking control scheme for multiquadcopter systems by exploiting the properties of the barrier Lyapunov function (BLF). Accordingly, the problem is formulated in a backstepping setting, and a distributed control law that guarantees collision-free formation tracking of the quads is derived. In other words, the problems of both tracking and interagent collision avoidance with a predefined accuracy are formulated using the proposed BLF for position subsystems, and the controllers are designed through augmentation of a quadratic Lyapunov function. Owing to the underactuated nature of the quadcopter system, virtual control inputs are considered for the translational (x and y axes) subsystems that are then used to generate the desired values for the roll and pitch angles for the attitude control subsystem. This provides a hierarchical controller structure for each quadcopter. The attitude controller is designed for each quadcopter locally by taking into account a predetermined error limit by another BLF. Finally, simulation results from the MATLAB-Simulink environment are provided to show the accuracy of the proposed method. A numerical comparison with an optimization-based technique is also provided to prove the superiority of the proposed method in terms of the computational cost, steady-state error, and response time. [ABSTRACT FROM AUTHOR]

A weld is the main connection form of special equipment, and a weld is also the most vulnerable part of special equipment. Therefore, an effective detection of a weld is of great significance to improve the safety of special equipment. The traditional inspection method is not only time-consuming and labor-intensive, but also expensive. The welding seam tracking and inspection robot can greatly improve the inspection efficiency and save on inspection costs. Therefore, this paper proposes a welding seam tracking and inspection robot based on YOLOv8s-seg. Firstly, the MobileNetV3 lightweight backbone network is used to replace the backbone part of YOLOv8s-seg to reduce the model parameters. Secondly, we reconstruct C2f and prune the number of output channels of the new building module C2fGhost. Finally, in order to make up for the precision loss caused by the lightweight model, we add an EMA attention mechanism after each detection layer in the neck part of the model. The experimental results show that the accuracy of weld recognition reaches 97.8%, and the model size is only 4.88 MB. The improved model is embedded in Jetson nano, a robot control system for seam tracking and detection, and TensorRT is used to accelerate the reasoning of the model. The total reasoning time from image segmentation to path fitting is only 54 ms, which meets the real-time requirements of the robot for seam tracking and detection, and realizes the path planning of the robot for inspecting the seam efficiently and accurately. [ABSTRACT FROM AUTHOR]

To improve the anti-disturbance capability of controller in automatic parking system and optimize the execution effect of control law, the closed-loop layered controller of front wheel angle was designed. The non-smooth control law based on the fal function was designed with driving distance as non-time reference to output the target front wheel angle. The front wheel angle observer was designed based on the Ackerman steering model, and the fuzzy sliding mode controller was used to implement closed-loop lateral control. The Carsim/Simulink co-simulation was built to verify the feasibility, tracking effect and robustness of the designed controller by simulating typical parking scenarios. A real vehicle test platform was used for testing. The results show that the closed-loop layered controller of front wheel angle can quickly and accurately track the target path, improve the lateral control accuracy of the parking system and ensure the tracking control effect under unknown steering nonlinear disturbances. [ABSTRACT FROM AUTHOR]

A prevention and control tracking system based on three-dimensional (3D) face recognition was designed to improve the target tracking accuracy of the prevention and control tracking system. The ARM control chip of TMS320DM6446 was selected as the control chip of the ARM control module. The CMOS image acquisition sensor of the image acquisition module collected face images. The collected images were transmitted to the 3D face recognition module. The 3D face recognition module used the Gabor wavelet algorithm to extract the 3D face contour features of the face image. Moreover, the LDA algorithm was used to recognize faces based on 3D face contour features. The 3D face recognition results were compared with the faces in the face library to determine whether prevention and control tracking were necessary. When prevention and control tracking was needed, the GPS tracking and positioning module embedded in the mobile device terminal of the target object was started. The GPS tracking and positioning module was used to prevent and control the tracking of the target. The results of prevention and control tracking were displayed to the system users using a VGA display. The experimental results indicated that the designed system could accurately recognize faces and achieve prevention and control tracking of the target based on the face recognition results. [ABSTRACT FROM AUTHOR]

Summary: A novel integral sliding mode control (ISMC) strategy combined with an adaptive super twisting observer (ASTO) for an uncertain robotic manipulator tracking control system is presented in this article. The comprehensive uncertainties including both parameter perturbations and external disturbances are considered during the controller design. Firstly, a new nominal control law with prescribed time convergent property based on time varying scaling function is presented for the system without uncertainties. Then this nominal control law constitutes the prescribed time convergent sliding surface for ISMC. As the reaching phase is eliminated in ISMC, leading to the prescribed time stability of the whole control system without uncertainties. Secondly, take the system uncertainties (both the matched and unmatched uncertainties) into consideration, two ASTOs are designed for handling them. So, the lumped uncertainties of the robotic manipulator control system can be well estimated and compensated in finite time with the help of backstepping method. Besides, the finite time convergent adaptive switching gains of the ASTO make the system stable without knowing the bounds of the uncertainties exactly and suppress the chattering phenomenon of control input. Finally, the proposed control algorithm is validated by simulation and experiment on a robotic manipulator. Also, from a quantitative analysis, we testify the proposed control scheme outperforms the compared one in all of the discussed cases of simulation part. [ABSTRACT FROM AUTHOR]