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This paper addresses the path-tracking problem for flexible needle control systems using a stochastic linear parameter varying (LPV) and model predictive control (MPC) strategy. Flexible needles operating in dynamic environments with non-uniform tissue density often deviate from ideal assumptions, resulting in non-standard models. The bicycle kinematics model for flexible needle motion control is transformed into an LPV model, improving accuracy and enabling more efficient control. The proposed stochastic LPV MPC approach aims to mitigate uncertainties arising from modelling errors and dynamic environmental factors, ensuring accurate trajectory tracking for the flexible needle. The sample and removal method is utilized to reformulate the probabilistic-constrained optimization problem for implementation. The contributions of this work lie in the application of stochastic LPV MPC to address the trajectory tracking problem in the presence of uncertainties. The simulation results illustrate the superior robustness of the stochastic LPV MPC approach, as evidenced by significantly smaller tracking errors across various scenarios. [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 innovative formation control method using the leader–follower strategy for a marine vehicle system, addressing the computing power disparity between the leader Unmanned Surface Vehicle (USV) and the follower Autonomous Underwater Helicopters (AUHs). Accounting for model uncertainties and external disturbances affecting the leader USV, we develop a control law for trajectory tracking, which ensures precise parameterized path tracking. A finite-time distributed observer based on the consensus principle is designed for the follower AUHs. The observer estimates the leader's position within communication constraints, ensuring timely and accurate information sharing. To achieve effective tracking and formation of the follower AUHs relative to the leader USV, we propose a formation control method with low complexity and predefined performance constraints. This method introduces a novel prescribed performance function and a Nussbaum function to manage tracking errors and address unknown propeller control directions. Finally, we validate the system's stability using Lyapunov stability theory and demonstrate the method's effectiveness through detailed MATLAB simulations. The results show that our approach significantly improves tracking accuracy and formation stability under various conditions. [ABSTRACT FROM AUTHOR]

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping. [ABSTRACT FROM AUTHOR]

The robotic arm is a complicated system with multiple inputs and outputs, strong coupling, containing uncertainties and nonlinearities. This study proposes a new practical robust control method based on the dynamics model and tracking error, including a model- and error-based proportional-differential feedback term and an error-based robust term. Specifically, the dynamics of the system are modeled using the Lagrangian method. Uncertainties are presumed to be time-varying but limited. Based on the Lyapunov method, the proposed controller has theoretically demonstrated the controlled system with uniform boundedness (UB) and uniform ultimate boundedness (UUB). Furthermore, the radius of the ultimately bounded hypersphere is arbitrarily small based on selecting appropriate design parameters. Based on the two-degree-of-freedom (2-DOF) planar robotic arm experimental platform, the self-developed rapid controller prototype CSPACE-RT is intended to eliminate tedious programming or debugging, significantly simplifying the experimental process. Finally, numerical simulation and experiment results verified the excellent control performance of the suggested controller. [ABSTRACT FROM AUTHOR]

In this article, a robust model predictive control method is investigated for settling the trajectory tracking problem of a bionic ankle–foot aided by a tensegrity mechanism. In order to achieve adaptive movement of the ankle–foot mechanism, a three-degrees-of-freedom spatial ankle–foot mechanism is designed by tensegrity, which is a spatial grid structure composed of springs and struts. Dynamic analysis is the basis of control algorithm research, and the dynamic model of the mechanism can be established by a Lagrangian equation. Then, a controller is proposed for tracking the trajectory of the ankle–foot mechanism under external disturbances. Combining rolling optimization and feedback correction, the controller can be defined as an optimization problem, by solving which the ankle–foot mechanism can be controlled to track the desired trajectory quickly. Furthermore, stability analysis is an essential part of predictive controller design, which can help to understand the operational mechanism of the control strategy. Numerical results demonstrate that the proposed approach improves trajectory tracking accuracy and avoids mechanism movement problems caused by disturbances. [ABSTRACT FROM AUTHOR]

A parametric Adaptive Model Predictive Controller (AMPC) based on Particle Swarm Optimization-Back Propagation (PSO-BP) neural network has been developed in this paper, the primary focus is on improving the trajectory tracking performance of autonomous vehicles under varying road conditions. The PSO-BP neural network is employed for real-time adjustment of the controller's predictive horizon and sampling time. A vehicle dynamics model is established and an improved tracking control algorithm involving road curvature feedforward is proposed. In the design of AMPC, the real-time update of tire lateral stiffness is achieved through the adoption of the Recursive Least Squares (RLS) method, ensuring the precision of trajectory tracking for the vehicle under varying operating conditions. The simulation platform, which combines Carsim and Simulink, was employed for validating the proposed approach. The findings reveal that the proposed controller can promptly adjust the predictive horizon and sampling time according to the vehicle's state. Through the employed estimation strategy, real-time adjustments of tire lateral stiffness are achieved, allowing for dynamic alterations of vehicle speed and front wheel angle in response to road curvature. As a result, this approach significantly enhances control accuracy and lateral steering stability, especially in large curvature conditions. [ABSTRACT FROM AUTHOR]

Linear Quadratic Regulator (LQR) is one of the most prevalent methods used in the control of unmanned aerial vehicles. LQR controllers are commonly employed in the control of both linear and non-linear systems due to their advantages such as easy-to-apply and high-performance structure. However, there is one main difficulty that plays a significant role in the manner of determining the gain for the control signal: choosing appropriate weighting matrices. The selection of these matrices that directly affect the controller performance is generally performed by trial and error, which is laborious and time-consuming. Accordingly, various optimization algorithms have been utilized to determine the weighting matrices of the LQR controller. In this paper, the weighting matrices of the designed LQR controller were obtained using Standard Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Grey Wolf Optimization algorithms. The obtained weighting matrices of the LQR controller were tested on an unmanned aerial vehicle simulation, and the performance of optimization algorithms were presented comparatively. [ABSTRACT FROM AUTHOR]

This paper presents an innovative control strategy for robot arm manipulators, utilizing an adaptive sliding mode control with stochastic gradient descent (ASMCSGD). The ASMCSGD controller significant improvements in robustness, chattering elimination, and fast, precise trajectory tracking. Its performance is systematically compared with super twisting algorithm (STA) and conventional sliding mode control (SMC) controllers, all optimized using the grey wolf optimizer (GWO). Simulation results show that the ASMCSGD controller achieves root mean squared errors (RMSE) of 0.12758 for θ 1 and 0.13387 for θ 2 . In comparison, the STA controller yields RMSE values of 0.1953 for θ 1 and 0.1953 for θ 2 , while the SMC controller results in RMSE values of 0.24505 for θ 1 and 0.29112 for θ 2 . Additionally, the ASMCSGD simplifies implementation, eliminates unwanted oscillations, and achieves superior tracking performance. These findings underscore the ASMCSGD's effectiveness in enhancing trajectory tracking and reducing chattering, making it a promising approach for robust control in practical applications of robot arm manipulators. [ABSTRACT FROM AUTHOR]

This paper presents a new method for controlling a quadrotor unmanned aerial vehicle (UAV) with neural network adaptive adjustment combined with a super-twisting algorithm, which utilizes back-propagation algorithm in neural networks to design an adaptive method that can adjust the coefficients of the sliding mode surface as well as the control gain adjustment adaptive problem in the super-twisting to improve the stability and accuracy of the position and attitude control of the quadrotor UAV under uncertainty and external disturbances. Specifically, the adaptive neural network learns to dynamically adjust the sliding surface parameters and control gain, effectively inhibiting the sensitivity to parameter uncertainty and external disturbances, while the super-twisting sliding mode control ensures that the sliding trajectory converges in finite time and reduces the chattering phenomenon. In addition, the quadrotor UAV system is divided into a fully-actuated subsystem and an under-actuated subsystem, each of which contains two control inputs and the appropriate control algorithms are designed respectively, and the stability of the algorithm is demonstrated by means of a Lyapunov function in finite time. The proposed control method for quadrotor UAVs is validated through numerical simulations conducted in the Matlab/Simulink environment. [ABSTRACT FROM AUTHOR]

This paper solves a fixed-time trajectory tracking control problem for a marine surface vessel in the presence of model uncertainties, external disturbances, input saturation and prescribed performance constraints. Firstly, a fixed-time disturbance observer (FXDO) is designed, which not only realizes fixed-time stability, but also solves the design method problem in the existing disturbance observer. Secondly, a fixed-time nonsingular terminal sliding mode manifold (FXNTSMM) with simple structures is designed, whereby the designed FXNTSMM reduces the calculation burden of the system. Thirdly, a fixed-time trajectory tracking control scheme in the presence of model uncertainties, external disturbances, input saturation and prescribed performance constraints is proposed based on the FXDO, the FXNTSMM and a prescribed performance function, and the entire constrained closed-loop control system is with fixed-time stability, simplicity and nonsingularity. Finally, several numerical simulation results are presented to demonstrate the effectiveness of the proposed trajectory tracking control scheme. [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]

We propose continuous integral-type sliding mode tracking control (CIT-SMTC) for a class of under-actuated gantry/bridge cranes. Compared to the available sliding-mode-based approaches for crane systems, the notable improvements of the proposed CIT-SMTC include: (i) the assurance of the soft start through the trajectory tracking control mode; (ii) the enhancement of system performance in terms of convergence time and control accuracy; (iii) the continuity of the control action; and (iv) the complete stability analysis of the overall closed-loop system. In the proposed control structure, an integral-type sliding surface is first designed such that during the sliding phase, the stability of the closed-loop system is guaranteed and the control performance of crane systems is enhanced. Then, by employing the introduced integral manifold and the super-twisting-like algorithm, the CIT-SMTC is proposed such that the states are restricted to the sliding surface in finite time and the continuous control signal is imposed. Rigorous analysis is provided to prove the stability of the overall closed-loop system. Finally, experimental results are shown to verify the superiority of the proposed CIT-SMTC. [ABSTRACT FROM AUTHOR]

Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknownmassmatrix, the proposedmethod enables e [ABSTRACT FROM AUTHOR]

This article addresses the problem of speed and steering control of unmanned sea surface vehicles operating under unknown ocean environments affected by complex nonlinearities and uncertain hydrodynamic coefficients. An inertial delay control (IDC)-based sliding mode control (SMC) is proposed. The proposed controller is robust against the system's nonlinearities, parametric uncertainties, external disturbances like a strong wind, complex disturbances due to wind-generated and ocean currents, etc. The proposed controller uses IDC to estimate these effects mentioned above, which makes the proposed SMC independent of the bound of uncertainties and disturbances, and provides chatter-free control. The effectiveness of the proposed controller is confirmed by considering the highly nonlinear model of Cypership-II using various performance indices. [ABSTRACT FROM AUTHOR]

Accurate assessment and effective feedback are crucial for cultivating learners' abilities of collaborative problem‐solving and critical thinking in online inquiry‐based discussions. Based on quantitative content analysis (QCA), there has been a methodological evolvement from descriptive statistics to sequential mining and to network analysis for mining coded discourse data. Epistemic network analysis (ENA) has recently gained increasing recognition for modelling and visualizing the temporal characteristics of online discussions. However, due to methodological restraints, some valuable information regarding online discussion dynamics remains unexplained, including the directionality of connections between theoretical indicators and the trajectory of thinking development. Guided by the community of inquiry (CoI) model, this study extended generic ENA by incorporating directional connections and stanza‐based trajectory tracking. By examining the proposed extensions with discussion data of an online learning course, this study first verified that the extensions are comparable with QCA, indicating acceptable assessment validity. Then, the directional ENA revealed that two‐way connections between CoI indicators could vary over time and across groups, reflecting different discussion strategies. Furthermore, trajectory tracking effectively detected and visualized the fine‐grained progression of thinking. At the end, we summarize several research and practical implications of the ENA extensions for assessing the learning process.Practitioner notesWhat is already known about this topicAssessment and feedback are crucial for cultivating collaborative problem‐solving and critical thinking in online inquiry‐based discussions.Cognitive presence is an important construct describing the progression of thinking in online inquiry‐based discussions.Epistemic network analysis is gaining increasing recognition for modelling the temporal characteristics of online inquiries.What this paper addsDirectional connections between discourses can reflect different online discussion strategies of groups and individuals.A pair of connected discourses coded with the community of inquiry model can have different meanings depending on their temporal order.A trajectory tracking approach can uncover the fine‐grained progression of thinking in online inquiry‐based discussions.Implications for practice and/or policyBesides the occurrences of individual discourses, examining the meanings of directional co‐occurrences of discourses in online discussions is worthwhile.Groups and individuals can employ different discussion strategies and follow diverse paths to thought development.Developmental assessment is crucial for understanding how participants achieve specific outcomes and providing adaptive feedback. [ABSTRACT FROM AUTHOR]

Surface movement exploration techniques are an important approach to further the understanding of small celestial bodies. Hopping movement is employed to take advantage of the weak gravitational field of small celestial bodies, facilitating long-distance mobility of the rover. The rover exhibits a strong attitude-orbit coupling characteristic during the hopping movement. Different contact attitudes contribute to the challenge of determining the rebound trajectory, rendering it difficult for the rover to maintain continuous movement towards the target position along the nominal trajectory. In this paper, a dual-stage control method for continuous surface hopping movement is developed, wherein the control is applied separately during the flight stage and the collision stage. During the flight stage, applying attitude control enables the rover to establish contact with the surface in a specific attitude, ensuring accuracy in movement direction and creating favorable conditions for the control process in the collision stage. The flywheel is controlled in the collision stage to correct the state change and energy loss due to the collision, enabling the rover to attain the desired hopping state. Considering the energy consumption of the rover, the minimum hopping velocity sequence and angular velocity sequence are designed based on the hopping angle and heading angle. The intricacies and uncertainties inherent in the surface environment of small celestial bodies pose challenges for the rover during hopping and flying. In response to the issue, a finite-time adaptive sliding-mode control law is devised. The control law facilitates the rapid adjustment of the rover to the target attitude in flight, demonstrating strong robustness. In addition, by integrating the changes in contact point velocity with the impulse contact model, the accuracy of the rover's rebound state updates is enhanced. Finally, a set of simulations is performed, and simulation results indicate that the dual-stage control method can achieve a final landing position error of less than 0.2 m for a continuously hopping. The finite-time adaptive sliding-mode control law can stabilize the rover's attitude within 10 s. Conducting 500 Monte Carlo simulations of the continuously hopping rover in three small celestial body surface simulation environments shows that the position endpoint error is within 0.3 m. • An innovative control method for continuous hopping on the surface of small celestial bodies with low gravity is proposed. • The attitude-orbit coupling challenge is addressed through a staged control. • A sequence of hopping states with minimum velocities is devised. • A finite-time adaptive sliding-mode control law is designed to effectively address unknown disturbances. • A corrected sliding-mode control law is developed for precise hopping state updates. [ABSTRACT FROM AUTHOR]

This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime. • A global LPV model of the BSD is built by interpolation modeling. • A modal-based LPV pole placement control is proposed to achieve modal control. • The proposed modeling and control achieve precise tracking and excellent robustness. [ABSTRACT FROM AUTHOR]

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This paper deals with robotic systems trajectory tracking problems by designing a new event-triggered sliding mode control (ET-SMC) algorithm with signal quantization. More precisely, an event-triggered control strategy is introduced to the sliding mode control algorithm with robustness to reduce the controller update frequency, so as to reduce the network communication resources consumption and maintain the control accuracy. In addition, the dynamic quantization method is adopted between the controller and the actuator for more communication efficiency. Unlike periodic time-triggered control strategy, a novel event triggering condition which requires no state-dependent variables is discussed for less triggering threshold computations. Furthermore, the minimum interval of adjacent triggering instant based on the new triggering condition can be obtained to avoid the Zeno phenomenon. Finally, simulation results demonstrate the validity of the presented control algorithm and practical experiments with a PHANToM Omni robotic device are given to verify the advanced performances. As a result, the trajectory tracking error is limited within a small range and the control update frequency is evidently reduced. [ABSTRACT FROM AUTHOR]

This paper investigates the challenging problem of fixed time trajectory tracking for robotic manipulators under the presence of unavailable model perturbation, external disturbance and from different initial states. Firstly, a novel fixed-time extended state observer (FESO) is designed to estimate and compensate the lumped disturbance, which is analyzed and proved to be stable in the sense of fixed time bounded stability. Secondly, a new type of fixed-time prescribed performance control (FPPC) is constructed to guarantee the system convergences to stable state within a predefined time and enhance transient performance. Furthermore, a novel continuous fixed time nonsingular fast terminal sliding mode variable is established, which addresses singularity obstacle in terminal sliding mode. Together with FESO and FPPC, a new fixed-time adaptive nonsingular fast terminal sliding mode controller (FANFTSMC) is developed. Meanwhile, an adaptive terminal sliding mode reaching law adopted in FANFTSMC promotes the robustness and decreases the chattering phenomenon. Then, the Lyapunov approach is given to clarify the advantages of FANFTSMC in terms of predefined time stabilization and which is demonstrated on a 2-DOF robotic manipulators. Thirdly, theoretical analysis and experiments are presented to illustrate the formulated control strategy owns fine performance and stronger robustness. [ABSTRACT FROM AUTHOR]

This paper attempts to develop a unified model predictive control (MPC) method for integrated path planning and trajectory tracking of autonomous underwater vehicles (AUVs). To deal with the computational burden of online path planning, an event-triggered model predictive control (EMPC) method is introduced by using the environmental change as a triggering mechanism. A collision hazard function utilizing the changing rate of hazard as a triggering threshold is proposed to guarantee safety. We further give an illustration of how to calculate this threshold. Then, a Lyapunov-based model predictive control (LMPC) framework is developed for the AUV to solve the trajectory tracking problem. Leveraging a nonlinear integral sliding mode control strategy, we construct the contraction constraint within the formulated LMPC framework, thereby theoretically ensuring closed-loop stability. We derive the necessary and sufficient conditions for recursive feasibility, which are subsequently used to prove the closed-loop stability of the system. In the simulations, the proposed path planning and tracking control are verified separately and integrated and combined with static and dynamic obstacles. [ABSTRACT FROM AUTHOR]

This article introduces an innovative trajectory tracking control methodology for a marine vessel with disturbances. The vessel is driven to track a predetermined trajectory while preventing the constraint violation of the position error. A universal barrier Lyapunov function (BLF) is, for the first time, established to resolve the variable constraint. It should be emphasized that the devised barrier function can handle constraint types including time-varying, time-invariant, symmetric, and asymmetric forms, and it can be employed to devise control schemes for unconstrained systems. Consequently, in comparison to the current BLF-based techniques for vessels, it can be flexible for dealing with practical control issues with or without constraints. A simplified disturbance observer performs estimations of ocean disturbances. It is proven that all the error variables can be exponentially stabilized to a small neighborhood close to the equilibrium point, while violations of the constraints on the position error never occur. The feasibility of the theoretical discoveries is shown by the outcomes of the final simulation. [ABSTRACT FROM AUTHOR]

Most existing model predictive control (MPC) methods overlook the network resource limitations of autonomous underwater vehicles (AUVs), limiting their applicability in real systems. This article addresses this gap by introducing an adaptive transmission, interval-based, and self-triggered model predictive control for AUVs operating under ocean disturbances. This approach enhances system stability while reducing resource consumption by optimizing MPC update frequencies and communication resource usage. Firstly, the method evaluates the discrepancy between system states at sampling instants and their optimal predictions. This significantly reduces the conservatism in the state-tracking errors caused by ocean disturbances compared to traditional approaches. Secondly, a self-triggering mechanism was employed, limiting information exchange to specified triggering instants to conserve communication resources more effectively. Lastly, by designing a robust terminal region and optimizing parameters, the recursive feasibility of the optimization problem is ensured, thereby maintaining the stability of the closed-loop system. The simulation results illustrate the efficacy of the controller. [ABSTRACT FROM AUTHOR]

This paper investigates a global fixed-time control strategy for a search and rescue unmanned surface vehicle (SRUSV) targeting water rescue missions. Firstly, an improved time allocation trajectory generation (ITATG) method is proposed to generate a smooth and continuous desired trajectory, incorporating position, velocity, and acceleration information. Secondly, a fixed-time sideslip angle observer-based adaptive line-of-sight (FTSOALOS) guidance law is designed. This law integrates time-varying look-ahead distances with a fixed-time sideslip angle observer (FTSO) to ensure rapid convergence of positional errors within a fixed timeframe. Additionally, this paper employs a first-order fixed-time disturbance observer (FOFTDO) to accurately estimate external disturbances. To alleviate network pressure and reduce actuator failure rates, a fixed-time event-triggered sliding mode control (FTETSMC) mechanism is developed, ensuring the convergence of tracking errors within a fixed timeframe. Finally, using Lyapunov theory, this paper proves that the entire control system designed possesses consistent global fixed-time stability. Comparative simulation experiments validate the effectiveness and superiority of the FTSOALOS guidance law and the FTETSMC controller. [ABSTRACT FROM AUTHOR]

This study proposes the design of a robust controller based on a Sliding Mode Control (SMC) structure. The proposed controller, called Sliding Mode Control based on Closed-Form Continuous-Time Neural Networks with Gravity Compensation (SMC-CfC-G), includes the development of an inverse model of the UR5 industrial robot, which is widely used in various fields. It also includes the development of a gravity vector using neural networks, which outperforms the gravity vector obtained through traditional robot modeling. To develop a gravity compensator, a feedforward Multi-Layer Perceptron (MLP) neural network was implemented. The use of Closed-Form Continuous-Time (CfC) neural networks for the development of a robot's inverse model was introduced, allowing efficient modeling of the robot. The behavior of the proposed controller was verified under load and torque disturbances at the end effector, demonstrating its robustness against disturbances and variations in operating conditions. The adaptability and ability of the proposed controller to maintain superior performance in dynamic industrial environments are highlighted, outperforming the classic SMC, Proportional-Integral-Derivative (PID), and Neural controllers. Consequently, a high-precision controller with a maximum error rate of approximately 1.57 mm was obtained, making it useful for applications requiring high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a control method for the linear quadratic tracking (LQT) problem with zero steady-state error. This is achieved by augmenting the original system with an additional state representing the integrated error between the reference and actual outputs. One of the main contributions of this paper is the integration of a linear quadratic integral component into a general LQT framework. In this framework, the reference trajectories are generated using a linear exogenous system. During a simulative implementation for the specific real-world system of a car-in-the-loop (CiL) test bench, we assumed that the 'real' system was completely known. Therefore, for model-based control, we could have a perfect model identical to the 'real' system. It became clear that for CiL, stable solutions cannot be achieved with a controller designed with a perfect model of the 'real' system. On the contrary, we show that a model trained via Bayesian optimization (BO) can facilitate a much larger set of stable controllers. It exhibited an improved control performance for CiL. To the best of the authors' knowledge, this discovery is the first in the LQT-related literature, which is a further distinctive feature of this work. [ABSTRACT FROM AUTHOR]

The lower limb exoskeleton (LLE) plays an important role in production activities requiring assistance and load bearing. One of the challenges is to propose a control strategy that can meet the requirements of LLE trajectory tracking in different scenes. Therefore, this study proposes a control strategy (DBO–FPID) that combines the dung beetle optimizer (DBO) with feedforward proportional–integral–derivative controller (FPID) to improve the performance of LLE trajectory tracking in different scenes. The Lagrange method is used to establish the dynamic model of the LLE rod, and it is combined with the dynamic equations of the motor to obtain the LLE transfer function model. Based on the LLE model and target trajectory compensation, the feedforward controller is designed to achieve trajectory tracking in different scenes. To obtain the best performance of the controller, the DBO is utilized to perform offline parameter tuning of the feedforward controller and PID controller. The proposed control strategy is compared with the DBO tuning PID (DBO–PID), particle swarm optimizer (PSO) tuning FPID (PSO–FPID), and PSO tuning PID (PSO–PID) in simulation and joint module experiments. The results show that DBO–FPID has the best accuracy and robustness in trajectory tracking in different scenes, which has the smallest sum of absolute error (IAE), mean absolute error (MEAE), maximum absolute error (MAE), and root mean square error (RMSE). In addition, the MEAE of DBO–FPID is lower than 1.5 degrees in unloaded tests and lower than 3.6 degrees in the hip load tests, with only a few iterations, showing great practical potential. [ABSTRACT FROM AUTHOR]

The implementation of unmanned intelligent construction on the concrete surfaces of an airport effectively improves construction accuracy and reduces personnel investment. On the basis of three known common tracked vehicle dynamics models, reference trajectory planning and trajectory tracking controller algorithms need to be designed. In this paper, based on the driving characteristics of the tracked vehicle and the requirements of the stepping trajectory, a quartic polynomial trajectory planning algorithm was selected with the stability of the curve as a whole and the end point as the optimization goal, combining the tracked vehicle dynamics model, collision constraints, start–stop constraints and other boundary conditions. The objective function of trajectory planning was designed to effectively plan the reference trajectory of the tracked vehicle's step-by-step travel. In order to realize accurate trajectory tracking control, a nonlinear model predictive controller with transverse-longitudinal integrated control was designed. To improve the real-time performance of the controller, a linear model predictive controller with horizontal and longitudinal decoupling was designed. MATLAB 2023A and CoppeliaSim V4.5.1 were used to co-simulate the two controller models. The experimental results show that the advantages and disadvantages of the tracked vehicle dynamics model and controller design are verified. [ABSTRACT FROM AUTHOR]

This study examined a novel method to create a long and narrow calibrated capture volume for tracking objects. The methodology relies on the reflection of parallel distance-measuring lasers. Images of a board, blocking the lasers as it is moved through the field of interest, were assembled into a virtual calibration fixture. The method accommodates large calibration volumes and can be used with multiple cameras, providing a consistent absolute positional reference that is difficult to achieve with large mechanical calibration boards. This study considered a 17.4 m long tracking volume. A 0.9 m long rod was tracked throughout the calibrated volume where its average tracked length was within 0.2% of its measured length. The speed of balls traveling through the calibrated volume were within 0.1% of independent speed sensors. The average residual error of a ball's tracked trajectory and a polynomial fit was within 1.5 mm. The method shows promise as an efficient means of calibrating large calibration volumes with multiple camera pairs. [ABSTRACT FROM AUTHOR]

A hydrodynamic control model is established in attention to coupling relationships among cable, propellers and robot body of a tethered underwater robot system. In this model the governing equations of umbilical cable and the robot are firstly introduced, then supplementary conditions are coupled into the equations to forming the dynamic mathematical model; finally a hybrid control strategy based on feed-forward negative feedback method for the cable and PID rule for the propellers are integrated in the mathematical model for composing the whole hydrodynamic control model. Both the mathematical model and the control algorithms are proved to be effective and reliable through comparing simulation with the experimental data in existed references. Based on the numerical model constructed in this paper, trajectory tracking of a tethered underwater robot system in different motion combinations are numerically simulated through computational fluid dynamics method. In the numerical simulations, finite difference method is used to solving the kinematic parameters of the mathematical model, while finite volume method is applied on calculating the hydrodynamic forces under a hybrid control manipulations. The robot motion in vertical direction is determined primary by feed-forward negative feedback strategy of adjusting the cable length, while the horizontal movement of the robot is controlled mainly through PID algorithm; The hydrodynamic loading on the robot body are influenced by the flow fields around the robot. Article Highlights: Established the coupling equations with considering dynamic behaviors among propellers, umbilical cable and robot body. Discovered the conversion relation which unrelated to objective factors between the rotating speeds of propellers and the corresponding thrusts. Analyzed the changing rules of hydrodynamic loading on the underwater robot under a hybrid control strategy. [ABSTRACT FROM AUTHOR]

Unmanned logistics vehicle (ULV) realize the automation and intelligence of cargo transportation, which improves the efficiency, cost-effectiveness and safety of logistics and distribution, while the trajectory tracking control of ULV is the key technology to ensure their safe and efficient delivery of goods. In order to solve the trajectory tracking problem of ULV in the process of delivering goods, this paper proposes a model predictive control (MPC) method based on event-triggered and fuzzy adaptive optimization parameters. Firstly, the dynamics model of the ULV is established. Secondly, an event-triggered mechanism is introduced to establish ET-MPC, while a disturbance observer is designed considering the external disturbance and the controller calculation discarding the nonlinear term. Thirdly, the advantages of fuzzy control and MPC algorithms are integrated, and the four important parameters in the MPC controller are adaptively optimized by fuzzy control, and the improved MPC control strategy is designed. Finally, the CarSim-Matlab/Simulink co-simulation platform and the experimental vehicle platform are constructed to verify the effectiveness of the improved MPC trajectory tracking controller proposed in this paper. The results show that the improved MPC control strategy can reduce the computation time of the controller, and the total number of triggering times of the controller is reduced by 46.44% compared with the classical MPC, which reduces the computational complexity of the controller and improves the accuracy and smoothness of the trajectory tracking of the ULV. [ABSTRACT FROM AUTHOR]

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In this study, a novel control scheme based on spiking neural networks (SNNs) has been proposed to accomplish the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs). The update rules for the network parameters have been derived using the Lyapunov stability theorem. Three different trajectories have been utilized in the simulated and experimental studies to verify the efficacy of the proposed control scheme. The acquired results have been compared with the responses obtained for proportional–integral–derivative (PID) and traditional neural network controllers. Simulated and experimental studies demonstrate that the proposed SNN-based controller is capable of providing better tracking accuracy and robust system response in the presence of disturbing factors. [ABSTRACT FROM AUTHOR]

Path planning and trajectory tracking are very meaningful for the field of autonomous driving, but currently path planning still has problems such as non-optimal paths and insufficiently accurate paths. This paper addresses the issue of path planning by proposing a improved A-star algorithm and locally zooming on the map technique to achieve precise path planning. Compared with the conventional method, this method reduces the time by 23% and the path length by 21% in the scenarios shown in the paper, respectively, and provides a reference for related research. Moreover, trajectory tracking was achieved using the improved LQR control. Compared with the conventional method, the improved LQR control algorithm reduces the average error by 80% in the scenario shown in the paper. Firstly, the A-star algorithm is enhanced by incorporating an unknown path cost estimation function, thereby improving the effect of its path planning in complex environments. Additionally, the method of locally zooming on the map is incorporated, effectively enhancing the accuracy and safety of path planning. Building upon the path planning, further improvements are made to the LQR control algorithm, enabling autonomous deceleration in complex sections, which facilitates better trajectory tracking and enhances the motion control performance of the robot during practical operations. [ABSTRACT FROM AUTHOR]

In this article, an updated dynamic model of mobile manipulators is presented, which incorporates the effects of wheel slipping and skidding in the mobile base motion equations. This modified model is then used to design an improved control algorithm. The interaction between the robot's mobile platform and manipulator often leads to inaccuracies when using traditional control methods. To address this, a modified control strategy is proposed. Unlike previous research, which faced difficulties directly measuring wheel slippage and traction forces, this work implements a disturbance observer to estimate these unknown parameters. The control algorithm is then designed using the feedback from the observer estimates. First, kinematic control is used to guide the robot along the desired trajectory. Next, dynamic control augmented with the disturbance observer enables robust tracking. Specifically, a disturbance observer-based sliding mode controller is developed for dynamic control of the system. This is further optimized using the state-dependent Riccati equation method. Lyapunov analysis proves system stability and guarantees disturbance estimation errors converge to zero. Simulations on a model of a mobile manipulator confirm the effectiveness of the proposed method. When combining the disturbance observer and sliding mode control, the consumed torque of the wheels and arms is reduced on average by 0.42 Nm and 0.04 Nm, respectively. Defining a cost function and optimizing the torques with the optimal sliding mode control approach further decreases the required torque to 5.17 Nm compared to the basic SMC, while also reducing tracking error by 4.9 mm. Despite platform slippage, the controller performance keeps end effector errors small and within allowable bounds. Experimental validation on a Scout robot demonstrates the feasibility of implementing this method on physical systems. The robot is able to track desired trajectories with acceptable errors. The tracking error in experiments is approximately 54 mm, compared to 13 mm in simulation. [ABSTRACT FROM AUTHOR]

In the realm of autonomous robotics, navigating differential drive mobile robots through unknown environments poses significant challenges due to their complex nonholonomic constraints. This issue is particularly acute in applications requiring precise trajectory tracking and effective obstacle avoidance without prior knowledge of the surroundings. Traditional navigation systems often struggle with these demands, leading to inefficiencies and potential safety risks. To address this problem, our studies propose an algorithm that integrates machine learning and control concepts, especially through the synergistic software of a Q-learning set of rules and a (PID) controller. This technique leverages the adaptability of Q-learning pathfinding and the precision of PID control for actual-time trajectory adjustment, aiming to beautify the robotics' navigation skills. Our comprehensive technique includes growing a country-area version that integrates Q-values with the dynamics of differential power robots, employing Bellman's equation for iterative coverage refinement. This version enables the robotics' capacity to dynamically adapt its navigation techniques in reaction to instant environmental feedback, thereby optimizing efficiency and protection in actual time. The effects of our full-size simulations exhibit a marked improvement in trajectory-tracking accuracy and impediment-avoidance competencies. These findings underscore the capability of combining machine learning algorithms with traditional methods to increase autonomous navigation technology in robotic systems. Our effects, derived from full-size simulations, suggest that the integration of Q-learning with PID controller markedly improves trajectory tracking accuracy, reduces tour times to targets, and complements the robotics' ability to navigate round barriers. This incorporated method demonstrates a tremendous advantage over conventional navigation systems, providing a sturdy way to the challenges of autonomous robot navigation in unpredictable environments. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [ABSTRACT FROM AUTHOR]

This paper studies the trajectory tracking issue for an underactuated autonomous underwater vehicle (AUV) in the horizontal plane. The desired velocity–tracking error relationship (DVTER) is constructed according to the kinematics and kinetic equation, which means that the expected velocities are built so that the position tracking errors converge to 0. Moreover, the limitation of obtaining the expected velocity by directly differentiating the desired position values is avoided. Then, the nonsingular fast terminal sliding mode (TSM) controller is developed to ensure that the velocities converge to the designed expected values in finite time, and tracking speed is improved by comparing with the traditional nonsingular terminal sliding mode method. It turns out that the expected trajectory can be tracked by an underactuated AUV. Finally, the efficiency of the constructed control mechanism is confirmed by simulation results. [ABSTRACT FROM AUTHOR]

This article investigates a prescribed-time trajectory tracking control strategy for USVs considering marine environmental interferences and unmodeled dynamics. Firstly, a fixed-time extended state observer is introduced to quickly and accurately observe the compound perturbations including ocean disturbances and unmodeled dynamics. Subsequently, a prescribed-time prescribed performance function is utilized to obtain guaranteed transient performance within a predefined time. Finally, combining the fixed-time extended state observer, dynamic surface control technique, and prescribed-time prescribed performance control, a prescribed-time prescribed performance control strategy is developed to guarantee that the tracking errors converge to a predefined performance constraint boundary within a prescribed time. The effectiveness and superiority of the presented control strategy is verified by the simulation results. [ABSTRACT FROM AUTHOR]

In this work, we develop a trajectory tracking control method for unmanned surface vessels (USVs) based on real-time compensation for actual wave disturbances. Firstly, wave information from the actual sea surface is extracted through stereoscopic visual observations, and data preprocessing is performed using a task-driven point cloud downsampling network. We reconstruct the phase-resolved wave field in real time. Subsequently, the wave disturbances are modeled mechanically, and real-time wave disturbances are used as feedforward inputs. Furthermore, an adaptive backstepping sliding mode control law based on command filters is designed to avoid differential explosion and mitigate sliding mode chattering. An adaptive law is also designed to estimate and compensate for other external disturbances and inversion error bounds that cannot be computed in real time. Finally, the feasibility of the proposed control strategy is validated through stability analysis and numerical simulation experiments. [ABSTRACT FROM AUTHOR]

Extensive research has focused on enhancing the efficiency and stability of robotic arms. Sliding mode control (SMC) is commonly used in industrial robots due to its robustness and simplicity. However, SMC approaches have challenges such as chattering and slow convergence rates which can compromise tracking accuracy. To address these issues, this paper proposes a novel Super-Twisting Fast Non-singular Terminal Sliding Mode Control (ST-FNTSMC) strategy for a 3-DOF arm robot. The proposed approach significantly improves trajectory tracking accuracy, robustness, and convergence time and eliminates chattering. The proposed controller was tested in the presence of model mismatches and external disturbances. The super-twisting methodology avoided chattering effects and increased robustness against perturbations. Two Lyapunov functions ensure closed system stability and finite-time convergence. The designed ST-FNTSMC controller is implemented in real-time using a Smart Man Robot manipulator. Its performance is compared to other sliding mode controllers, such as conventional PID Sliding Mode Control (PID-SMC), Non-singular Terminal Sliding Mode Control (NTSMC), and Fast Non-singular Terminal Sliding Mode Control (FNTSMC). Experimental results demonstrate the superior performance of the proposed controller, highlighting its effectiveness in improving the efficiency and stability of industrial robots. [ABSTRACT FROM AUTHOR]

Technological advancements have positively impacted the automotive industry, leading to the development of autonomous cars, which aim to minimize human intervention during driving, and thus reduce the likelihood of human error and accidents. These cars are distinguished by their advanced driving systems and environmental benefits due to their integration of cutting-edge autonomous technology and electric powertrains. This combination of safety, efficiency, and sustainability positions autonomous vehicles as a transformational solution for modern transportation challenges. Optimizing vehicle speed is essential in the development of these vehicles, particularly in minimizing energy consumption. Thus, in this paper, a method to generate the maximum velocity profile of a vehicle on a real road, extracted using online mapping platforms while ensuring compliance with maximum legal speed limits, is proposed. A nonlinear model, closely aligned with real-world conditions, captures and describes vehicle dynamics. Further, a nonlinear model predictive control strategy is proposed for optimizing the vehicle's performance and safety in dynamic driving conditions, yielding satisfactory results that demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system's environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance. [ABSTRACT FROM AUTHOR]

Since the trajectory-tracking control performance of multi-joint robot manipulator may be degraded due to modeling errors and external disturbances, this paper designs a new adaptive robot manipulator trajectory tracking control method through improved genetic algorithm and radial basis function neural network sliding mode control (IGA-RBFNNSMC). Firstly, the genetic algorithm (GA) is improved by establishing superior populations centered on individuals with high fitness values and selecting individuals in the superior populations for crossover and variation. Secondly, the improved genetic algorithm (IGA) is used for the optimization of the center vector and width vector of the Gaussian basis function in radial basis function (RBF) neural network. Then, based on the dynamics model of the robot manipulator, the modeling errors are approximated by RBF neural network and eliminated by sliding mode control (SMC), and the Lyapunov theorem is used to prove the stability and convergence of the control system. Finally, a two-joint robot manipulator is taken as the research objective and the simulation results show that IGA can significantly reduce the solution time on the basis of guaranteed accuracy and IGA-RBFNNSMC can make the trajectory tracking control accurate and more efficient, which proves the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article presents the design of a control strategy for a lower limb gait training system catering to patients with Spinal Cord Injury (SCI) or stroke. The system operates by driving the hip and knee joints individually through pneumatic cylinders. The focus lies on the study and development of a control strategy for the pneumatic actuators within the gait training system, specifically targeting trajectory tracking control of pneumatic double-acting cylinders utilizing a PID Controller. The experiment setup comprises a pneumatic cylinder regulated by a proportional valve, incorporating feedback via position and pressure sensors. The experimental results show that the system exhibits good trajectory-tracking performance, particularly at low frequencies. [ABSTRACT FROM AUTHOR]

Abstract Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping.

This article is concerned with the trajectory tracking control problem for the nonlinear systems in the sense of the predefined settling time and accuracy. In contrast with the existing works, we focus on the cases where the system dynamics, its bounding functions, the unmatched disturbances, and the time‐varying parameters are totally unknown; the derivatives of the desired trajectory are not required to be available. They significantly challenge the identification and/or approximation‐based control solutions. To overcome this obstacle, a novel robust prescribed performance control approach via state feedback is put forward in this article. It not only ensures the natural satisfaction of the specific initial condition but also realizes a full‐time performance specification for trajectory tracking. Furthermore, for the case of unmeasured state variables, an output‐feedback control approach is further derived by adopting an input‐driven filter and conducting trivial changes on the design procedure. Moreover, both approaches exhibit significant simplicity, without the needs for parameter identification, function approximation, disturbance estimation, derivative calculation, or command filtering. Three simulation studies are conducted to clarify and verify the above theoretical findings. [ABSTRACT FROM AUTHOR]