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This research presents an algorithm for modeling and controlling a trajectory tracking problem for a 6-dof (degree of freedom) parallel platform using inverse differential modeling aided by neural networks. The algorithm addresses the issues of model uncertainty and adaptation to changes and external disturbances within the surrounding environment. By employing the algorithm of Particle Swarm Optimization (PSO), the initial weight values of the neural network are determined, along with the optimal network structure required for estimating the differential model. This is done to achieve the best network performance with minimal training time. Subsequently, the network is trained using a comprehensive training dataset that covers the entire workspace of the platform, without having to know the geometric configuration or mathematical model of the robotic manipulator. The trained network is then utilized within the control loop to drive the parallel platform in a reference trajectory tracking problem. The acquired data during platform operation is used to retrain the neural network, enabling it to adapt to changes occurring within the system. The simulation results demonstrated the effectiveness of the proposed algorithm in the problem of reference path tracking by enhancing performance and adapting to changes occurring in the overall platform. The proposed model was able to reduce tracking error by an average of 92.2%, assuming differences in the length of one of the platform’s arms, as well as an average reduction of 27.3% assuming the presence of mechanical backlash within the platform actuators. Furthermore, the proposed algorithm exhibited robustness against noise and external disturbances.

This paper implements and compares the performance of three controllers based on Sliding Mode Control (SMC) applied to the motion control of mobile robots. The controllers include a standard SMC controller and two variations, namely Dynamic Sliding Mode Control (D-SMC) and Dual Sliding Mode Control (DM-SMC). The three controllers differ from conventional SMC approaches since they are designed based on a generic reduced-order empirical model that can represent any system that exhibits behavior akin to a First-Order Plus Delay Time (FOPDT) system. In this study, the comparison of controllers focuses on the Trajectory Tracking Problem (TTP) of a Nonholonomic Mobile Robot (NMR). Performance indices are employed to quantify the controllers' results across three trajectory types. Simulation results evidence that if a smooth curvature trajectory is tracked, the best performance (in terms of minimizing trajectory tracking error and controller effort) can be obtained by using a DM-SMC. On the other hand, if there are discontinuities in the curvature, the results suggest that a better alternative is the D-SMC since it compensates for abrupt changes as in the case of tracking a square trajectory. Overall, the findings from this work demonstrated that the use of simplified empirical models to design SMC-based motion controllers can be successfully applied to solve the TTP of NMR.

This paper presents a Lyapunov based controller for a quadrotor Unmanned Aerial Vehicle (UAV) to address the desired trajectory tracking problem. The controller is designed based on the dynamical model of the quadrotor UAV. The quadrotor UAV system can be divided into two subsystems e.g. translational components as the positional subsystem and rotational components as the attitude subsystem. The positional subsystem is an under-actuated system, and the attitude subsystem is a fully actuated system. The desired roll and pitch angles are calculated by considering the nonholonomic constraints of a quadrotor UAV. The desired yaw is considered as constant as well as variable considering different applications. The proposed controller is applied to hover at a desired setpoint, movement through multiple desired waypoints at different planes, and to track desired trajectories on different planes and with different shapes. The results depict that the quadrotor UAV has successfully tracked the desired waypoints and trajectories and hover at a desired setpoint. The comparative results are provided on multiple aspects such as the control input values, improvement of the tracking performance. These shows the effectiveness of the proposed controller over existing controllers.

In this study, we applied deep learning to improve the control of a KUKA LBR4 7 Degrees of Freedom (DOF) robotic arm. We developed a dynamic model using a comprehensive dataset of joint angles and actuator torques obtained from pick-and-place operations. This model was incorporated into a Model Predictive Control (MPC) framework, enabling precise trajectory tracking without the need for traditional analytical dynamic models. By integrating specific constraints within the MPC, we ensured adherence to operational and safety standards. Experimental results demonstrate that deep learning models significantly enhance robotic control, achieving precise trajectory tracking. This approach not only surpasses traditional control methods in terms of accuracy and efficiency but also opens new avenues for research in robotics, showcasing the potential of deep learning models in predictive control techniques.

Most of the previous research about unmanned surface vehicle (USV) control system just focuses on reaching the target but not considers berthing. Moreover, computation cost is rarely concerned. For automatic target arriving and berthing of USV in presence of multi-shape convex obstacles and disturbances, this paper designs a composite control system including trajectory optimization and trajectory tracking. In order to generate a smooth and optimal trajectory, two objectives including minimum running time and energy consumption are proposed, and their priority requirement is presented. Fuzzy satisfactory optimization is introduced to reformulate the objective functions, and the membership degree difference is used to model priority between two objectives. The obstacle avoidance constraint is developed based on the improved P-criterion. The comprehensive optimization model is established to obtain the maximum membership degree. Further, the priority optimization model is proposed to expand the feasible domain by relaxing the comprehensive optimization result. Two models are discretized by Gaussian pseudospectral method to find the optimal solution. To achieve good trajectory tracking under disturbances, two first-order active disturbance rejection control (ADRC) controllers are designed to follow the desired longitudinal velocity and course angular velocity. All the velocities of USV can converge. The simulation proves the effectiveness of the proposed co-designed system by comparing with other methods.

This paper deals with the nonlinear robust PWM control of a DC/AC differential boost inverter based on dynamic feedback linearization, active disturbance rejection, and sliding mode control techniques. The inverter topology consists of two synchronous boost converters linked through a load resistance. The model of each boost converter is put in Brunovski’s canonical form through dynamic feedback linearization, defining an auxiliary input depending on the control variable in an affine mode and a nonlinear disturbance containing endogenous and exogenous variables. Each disturbance is estimated by an extended state observer and compensated through a control law designed for tracking the desired trajectory, according to the active disturbance rejection control technique. Finally, a sliding mode component is designed and added to the previous control law to assure the robustness of the closed loop system against uncertainties due to electric parameters, supply voltage and load resistance deviations, and disturbance estimation errors. Experimental results validate the proposed control methodology.

Chemotherapy, a vital cancer treatment, operates by delivering drugs to target and eliminate cancer cells in the patient body. Mathematical models like the log-kill, Norton-Simon, and $E_{\max }$ hypotheses describe the growth/shrinking of the cancerous tumor due to the interaction and administration of the drugs with the tumor. This paper proposes a robust control approach based on artificial time-delayed theory to track the desired rate of change in tumor volume under model uncertainties and disturbances. The proposed algorithm relaxes the assumption on a priori knowledge of disturbance bound and its derivative. Unlike traditional methods, the control structure is simple, and the total disturbance is estimated by analyzing the previous input and output of the feedback state and control variables. Thus, robustness is ensured without relying on high-frequency switching or high gain. The stability analysis of the proposed scheme is investigated based on the Lyapunov theory. Moreover, extensive simulation results with comparative analysis affirm the efficacy of the proposed approach.

A predefined-time $H_{\mathrm {\infty }}$ trajectory tracking control scheme based on self-adjusting prescribed performance functions and dynamic relative threshold event-triggered mechanism is proposed for a five-degree-of-freedom underactuated autonomous underwater vehicle (AUV) with unknown external disturbances and actuators saturated. First, a new self-adjusting prescribed performance function is designed to ensure that the tracking errors do not exceed the prescribed bounds and to solve the singularity problem of general prescribed performance control (PPC) due to actuator saturation. Second, a predefined-time $H_{\mathrm {\infty }}$ controller combined with a predefined-time dynamic surface control (DSC) method is proposed to guarantee that system signals converge to the neighborhood of zero in a predefined time without adjusting the control parameters and to improve system robustness to uncertain disturbances. Third, an improved dynamic relative threshold event-triggered method is designed to save the communication resources of an underactuated AUV system. Then, the Lyapunov stability proof indicates that all signals in the closed-loop system are predefined-time bounded, and the trajectory tracking errors can converge to the neighborhood of zero within the set desired time. Finally, the simulations not only demonstrate the performance of the proposed controller-controlled system but also verify the effectiveness of the proposed scheme.

In order to improve the tracking accuracy and response speed of the articulated robot in the working process under non-strict repetitive conditions, a three-joint articulated robot model is designed, and the kinematics and dynamics analysis are carried out to verify the reasonable structure of the model. In view of the non-repetitive and nonlinear characteristics of the articulated robot system, it is proposed that a high-order internal model iterative learning control algorithm can be applied to the control of the articulated robot system. A reasonable learning gain and a higher internal model order are designed to strictly prove its convergence in theory. The simulation contrast experiment and the trajectory tracking experiment after adding the disturbance are designed. The results show that the high-order internal model iterative learning algorithm converges faster and has good control effect.

This article presents an extensive comparison of collision avoidance systems with local path planning based on a mathematical model of an autonomous underwater vehicle (AUV) controlled by the trajectory tracking control system. In the study, six methods such as improved artificial potential field (APF), D* algorithm, dynamic course adjustment (DCA), genetic algorithm (GA), particle swarm optimization (PSO) and rapidly-exploring random tree (RRT) were tested, the efficiency of which was verified on 10 test maps with various types of obstacles’ configurations and shapes with taking into account real-time decision-making ability. Both obstacle detection system and collision avoidance systems were extensively fine-tuned to achieve a compromise between collision avoidance efficiency and minimization of computation time. Based on the prepared simulation framework and environment, the trajectory completion percentage, average trajectory deviation, trajectory energy consumption, computation time and percentage of collision trials were assessed. The best results in terms of AUV safety were achieved by the D* and DCA methods. Slightly worse results were obtained by GA and PSO. Considering real-time decision-making ability, the best results were obtained by DCA and APF methods. Based on a detailed analysis of the results, it can be concluded that the DCA method can be used in quick-react emergency systems, and D*, GA and PSO methods are reasonable to use in medium-range local collision avoidance systems.

In order to realize waypoint-based automatic berthing alongside or astern, a generation algorithm of dynamic virtual ships is proposed to guide the underactuated surface vehicle (USV). In this paper, a nonlinear model predictive control (NMPC) problem is set up with multiple shooting method, which lifts the system and improves convergency. Measuring error and disturbances are reduced by moving horizon estimation (MHE) where the weights of sensors are auto-tuned according to their variation. Simulations of berthing alongside and astern of ship BAY CLASS illustrates the position error is smaller than 0.6 m and course error is smaller than 0.5°. Compared to adaptive sliding mode control, proposed method has advantage on state smoothness and the control performance meets the requirements of berthing accuracy.

It is undeniable that the development of robot manipulators has gained significant attention due to its numerous applications in various sectors. In order to address and resolve several issues for robotic manipulators, this paper provides a review of recent control approaches that have been conducted. In the beginning, a succinct comprehensive introduction of the pertinent terminology, configurations, components, advantages/disadvantages, and applications is given. A state-of-the-art review of the various control strategies for robotic manipulator systems is then presented, along with potential solutions when the systems are faced with various obstacles and impediments. An analytical study is discussed showing percentages of the papers discussed in this study in terms of the different control strategies, link types, models, applications, and degrees of freedom of robotic manipulators. For academic, research, medical, and industrial uses, some off-the-shelf developments in robotic manipulators are also presented.

To address the challenges posed by nonlinear tire characteristics in trajectory tracking control of active four-wheel steering (4WS) vehicle or fire-fighting robot, a novel control method is proposed. This method incorporates time-varying corrected cornering stiffness to enhance adaptability. Initially, the impact of friction coefficient and vertical load on tire lateral force nonlinearity is analyzed. Key input features are identified, and a cornering stiffness estimation model is developed using a Back Propagation (BP) neural network. Subsequently, the trajectory tracking state equation is established based on the 2-degree-of-freedom (2-DOF) dynamic model and trajectory tracking error model. An active front-wheel steering (AFS) trajectory tracking sliding mode controller (SMC) is designed, augmented by a Radial Basis Function (RBF) neural network, forming an RBF-SMC AFS controller capable of real-time correction of cornering stiffness. Additionally, an active rear-wheel steering (ARS) controller is designed based on fuzzy control theory, considering the influence of center of gravity lateral deviation on vehicle stability. Finally, Simulation tests under varied speed conditions on icy and dry asphalt surfaces, using the Matlab/Simulink-Carsim co-simulation platform, demonstrate that the proposed method dynamically adjusts front and rear axle cornering stiffness, enhancing trajectory tracking accuracy and maneuvering stability, particularly in high-speed or low-friction road conditions.

A linear time-varying model predictive control (LTV-MPC) method based on $SE(3)$ is proposed, to enhance the control precision and reduce energy consumption of the spacecraft. First, left-invariant principle of Lie group, Lie algebra $SO(3)$ , and other differential geometry theories are applied to extend LTV-MPC to $SE(3)$ . Then, considering the obstacle avoidance problem of the spacecraft in space, a suitable optimization function is selected to ensure smooth tracking of the desired working trajectory. By controlling the incremental output with the fastest convergence rate, the controller enables effective trajectory tracking. Finally, the effectiveness and applicability of the controller are verified by simulation experiments. These simulations validate the controller’s ability to achieve constraint satisfaction, optimize transient processes, and enhance control accuracy. The results provide compelling evidence of the controller’s potential for real-world spacecraft control applications.

In response to the challenge of insufficient trajectory tracking accuracy and low solution efficiency of Mecanum wheel AGV (Automated Guided Vehicle) under complex and constrained working conditions, this paper proposes an efficient Model Predictive Control (MPC) method to achieve superior tracking performance and robustness. Initially, a linear error model of the mobile platform is established based on pose error, serving as the predictive model for the MPC controller. A target function is designed to transform the trajectory tracking control problem into an optimal control problem. To handle inequality constraints, penalty terms are introduced into the objective function, and the resulting constrained problem is subsequently solved to approximate the optimal solution for the original inequalities. To alleviate the computational burden associated with real-time optimization problem-solving, an efficient MPC algorithm. has been developed. To ensure closed-loop stability under the MPC control method, stability constraints are imposed on the new optimization problem. Simulation results demonstrate that, in comparison to traditional MPC methods, the proposed approach reduces the average solution calculation time by 5.1% and the maximum single calculation time by 13.7%, all while maintaining trajectory tracking accuracy. These results validate the algorithm’s feasibility, effectively addressing the challenges associated with solving MPC problems.

Multi-unmanned aerial vehicle (UAV) system often encounters sudden static or dynamic obstacle threats during flight. How to realize obstacle avoidance is one of the key problems that must be solved for multi-UAV system. Therefore, this paper proposes a distributed model prediction control algorithm with mode switching. Two control modes (the tracking mode and the collision avoidance mode) and their switching strategies are designed based on risk of collision. Among them, the tracking mode takes tracking trajectory as the goal, and the corresponding algorithm can ensure the asymptotic stability of the system, the collision avoidance mode takes collision avoidance as the primary goal, and collision avoidance constraints are introduced by triggering to achieve collision avoidance between the UAV and the obstacles. Compared with the traditional single-mode predictive control algorithm, the proposed method can make the UAVs avoid the pre-unknown large-scale obstacles more effectively, and has the advantages of low conservativeness and short computation time. Finally, the effectiveness of the method is verified using the simulation of six quadrotors.

A proportional plus derivative (PD) adaptive tracking control strategy based on neural sliding mode is proposed for the interference of unknown factors in the mathematical model of four-wheel omnidirectional robots. Firstly, the dynamics model of the robot is analyzed. Secondly, the backstepping trajectory tracking controller is used to obtain the virtual velocity, and the PD controller is designed based on the error between the virtual input and the actual input. Then, the neural network is used to approximate the uncertainty in the dynamics model, and the stability of the system is proved by the Lyapunov theorem. Simulation and experimental results show that the control strategy is effective.

For the trajectory tracking control problem of the omnidirectional mobile platform of Mecanum wheels, a strategy of double closed-loop control with model predictive control (MPC) and differential forward proportional plus integral plus derivative (PID) is proposed. The attitude control loop and velocity control loop are designed based on the kinematics characteristics of Mecanum wheels. The linear error model of the Mecanum wheels chassis is established by the attitude control loop, the quadratic objective function is designed, and the problem of path following is transformed into predictive control for the nonlinear model. In the velocity control loop, the differential forward PID controller is used to avoid the high-frequency disturbance to the system caused by frequent step changes of the input quantity, accelerate the convergence of the angular velocity of the Mecanum wheels, and enhance the stability of the system. Simulation experiments demonstrate that the controller designed in this study has better convergence speed and tracking accuracy than the commonly used trajectory algorithm, and it can provide good robustness to control the mobile platform of Mecanum wheels.

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.

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.

Soaring drones can use updrafts to reduce flight energy consumption like soaring birds. With control surfaces that are similar to those of soaring birds, the soaring drone achieves roll control through asymmetric sweepback of the wing on one side. This will result in asymmetry of the drone. The moment of inertia and the inertial product will change with the sweepback of the wing, causing nonlinearity and coupling in its dynamics, which is difficult to solve through traditional research methods. In addition, unlike general control objectives, the objective of this study was to enable the soaring drone to follow the soaring strategy. The soaring strategy determines the horizontal direction of the drone based on the vertical wind situation without the need for active control of the vertical movement of the drone. In essence, it is a horizontal trajectory tracking task. Therefore, based on the layout and aerodynamic data of the soaring drone, reinforcement learning was adopted in this study to construct a six-degree-of-freedom dynamic model and a control flight training simulation environment for the soaring drone with asymmetric deformation control surfaces. We compared the impact of key factors such as different state spaces and reward functions on the training results. The turning control agent was obtained, and trajectory-tracking simulations were conducted.

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.

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.

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.

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.

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.

This article studies a nonlinear model predictive control (NMPC) scheme for the trajectory tracking efficiency of a quadcopter UAV. A cost function is first proposed that incorporates weighted increments of control forces in each direction, followed by a weighted summation. Furthermore, a contraction constraint for the cost function is introduced based on the numerical convergence of the system for the sampling period of the UAV control force. Then, an NMPC scheme based on improved continuous/generalized minimum residuals (C/GMRES) is proposed to obtain acceptable control performance and reduce computational complexity. The proposed control scheme achieves efficient and smooth tracking control of the UAV while guaranteeing the closed-loop stability of the system. Finally, simulation results are presented to illustrate the effectiveness and superior performance of the proposed NMPC control scheme.

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.

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.

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.

This article researches the problem of trajectory tracking for unmanned marine vehicles (UMVs) under disturbances and denial‐of‐services (DoS) attacks in the wireless channel. An equivalent data‐driven model of UMVs with ocean disturbances is established by using partial form dynamic linearization algorithm. The disturbances and the input of UMVs have different pseudo partitioned Jacobean matrix, and the disturbances are estimated by using extended state observer, which improves the immunity of UMVs to disturbances in the environment. It is the first time that the DoS attacks are considered under the data‐driven model for UMVs, and a novel data‐driven adaptive trajectory tracking control framework is constructed. The article proposes an attack predictive compensation mechanism to mitigate their effects of DoS attacks, which follows the Bernoulli distribution. Based on it, the data‐driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data‐driven control scheme and the predictive compensation mechanism is validated through the simulations. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a controller for joint position tracking of torque‐driven robot manipulators affected by torque disturbances. In particular, the proposed approach allows concluding global exponential stability (GES) of the state‐space origin of the closed‐loop system when constant disturbances affect the robot dynamics. Besides, when time‐varying disturbances are presented, the trajectories of the closed‐loop system are proven to be bounded as result of the input‐to‐state stability. The main contribution is the design of the control law and a nonlinear observer based on an alternative energy shaping approach. The nonlinear observer is designed to compute and compensate for unknown disturbances, whether they are constant or time‐varying. Furthermore, a detailed stability analysis of the closed‐loop system based on Lyapunov theory and input‐to‐state stability is presented. As far as the authors know, this is the first energy‐shaping controller for trajectory tracking control of robot manipulators affected by constant disturbances that achieves global exponential stability. Real‐time experiments on a manipulator arm of two degrees‐of‐freedom illustrate the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

Ship docking plays an important role in the shipbuilding industry as a key part of maintaining and repairing ship structures. However, at present, the surface grinding process during ship docking relies on traditional manual work, which has problems such as low efficiency, long working hours and high danger. For this reason, a new cable-driven grinding mechanism was proposed, which used four cable to drive the grinding device to achieve three degrees of freedom of movement. Firstly, the dynamics model of the system was established by Lagrangian method. Then a Fuzzy-PID( proportional integral derivative) control strategy with cable tension optimization term was proposed on the basis of the dynamics model, which could achieve accurate trajectory tracking and ensured the cable was in tension. Finally, the effectiveness of the proposed control strategy was verified by numerical simulation. The results show that the control accuracy of the proposed control strategy is improved by 25% compared with the PID control commonly used in cable-driven parallel robots, which has high control accuracy and stability. The cable-driven grinding mechanism and its control strategy proposed in this paper can provide some theoretical support for applications such as surface treatment and precision manufacturing of large structural parts. [ABSTRACT FROM AUTHOR]

To address the nonlinear stabilization problem and improve the tracking control feature of ball on plate system (BPS), this paper puts forward a novel Takagi Sugeno (TS) fuzzy control augmented with the current cycle feedback iterative learning control (CCF-ILC) scheme. According to Bode's sensitivity integral, the performance of linear controllers is always a trade-off between reference tracking and robustness. Hence, to deal with the so-called 'waterbed' effect, this work exploits the capability of TS fuzzy to handle the nonlinear dynamics and synthesizes a learning control scheme based on current iteration error to capitalize the information rich error signal for enhancing the robustness and trajectory tracking features. The global asymptotic stability of the proposed TS fuzzy augmented ILC scheme is proved using the Lyapunov function and linear matrix inequalities (LMIs). Moreover, the monotonic convergence of ILC is presented based on the singular value condition. For identifying the rolling mass from the video stream, a background subtraction algorithm based on thresholding technique is implemented. Finally, the robustness and tracking features of the proposed scheme are evaluated on a two degrees of freedom (DoF) laboratory scale BPS system through hardware in loop (HIL) testing for three realistic test cases. The tracking performance quantified using the root mean square error (RMSE) and power spectral density plot corroborates that the proposed scheme can offer better setpoint tracking and robustness feature compared to state-of-the-art fuzzy and ILC control techniques implemented on BPS. [ABSTRACT FROM AUTHOR]

This paper investigates the high-precision sliding mode based tracking control of robot manipulators with uncertain dynamics and external disturbances. Different from most currently used fast nonsingular terminal sliding mode surfaces (FNTSMs) which use linear sliding mode (LSM) to avoid singularity, a new recursive terminal sliding mode surface (RTSM) is firstly constructed with a recursive structure to avoid the singularity problem in this paper. The RTSM can improve convergence precision and speed near the equilibrium point compared with FNTSMs. Then a sliding mode based controller has been designed to stabilize the closed-loop system. To cope with model uncertainties, frictions and external disturbances, a novel adaptive sliding mode disturbance observer (ASMDO) has been constructed, which can estimate lumped uncertainties and feed them to the controller to achieve disturbance-rejection control. Compared with traditional disturbance observer with asymptotic stability, the observation error of ASMDO can be driven into a sufficiently small set centered on zero within a predefined fixed time, which means fast observation speed and high observation accuracy. The upper bounds of uncertainties and their derivatives are not needed in observer design. Abundant simulations and experiments also verified the effectiveness and superior properties of the proposed scheme. [ABSTRACT FROM AUTHOR]

Considering the precise model information of the aerospace model is not always available under the effect of external disturbance and model uncertainty, a new data‐driven point‐to‐point iterative learning control (PTPILC) method is proposed for aerospace vehicles to track the predetermined trajectory. Firstly, the system of the aerospace vehicle is converted into a discrete form without any omission, which reflects the dynamics of the original system by input and output data. Then, the objective function of the tracking error is proposed in the quadratic form to facilitate the application of the conjugate gradient algorithm, which develops an improved data‐driven PTPILC method. And the convergence analysis of the proposed method is presented. Furthermore, the control parameters are optimized to make the proposed method more robust against different parameter deviations. Finally, the effectiveness of the proposed method is illustrated by different simulations. [ABSTRACT FROM AUTHOR]

A fixed‐time fault‐tolerant control scheme based on sliding mode observer is proposed, which makes the system more stable and stronger anti‐jamming ability. In our design, its distinct characteristic is to combine the passive fault‐tolerant control and non‐singular fast terminal sliding mode (NFTSM). And it ensures that the system states can converge to a very small neighborhood near the equilibrium point in a fixed time no matter in normal condition or fault condition. In addition, a novel sliding‐mode observer and a fast variable power reaching law are added to make the system converge faster and chattering smaller. Finally, the computer simulation results of a two‐joint manipulator demonstrate the feasibility of the proposed strategy. [ABSTRACT FROM AUTHOR]

As for the problem of trajectory tracking of a multi-joint serial manipulator, a novel fixed-time control scheme is proposed based on non-singular fast terminal sliding mode control. By employing a fast terminal sliding mode surface, we solve the singularity problem existed in traditional terminal sliding mode surface. In the meantime, in order to improve the rapidity of the system, the fixed-time control is incorporated with the fast terminal sliding mode surface control. Theoretical analysis proves that the proposed control scheme guarantees that better tracking performance is obtained, and its convergence time upper limit is not affected by the initial states. In addition, a reaching law with the exponential approach characteristic is added to the control law, which effectively reduces the chattering phenomenon in the controller design. Finally, the effectiveness and feasibility of the designed controller are verified through a numerical simulation. [ABSTRACT FROM AUTHOR]

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system's capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme. [ABSTRACT FROM AUTHOR]

Achieving the high-precision control of cable-driven parallel robots (CDPRs) is complex because of their structural properties. In this paper, a quintessential redundant CDPR is designed as the research subject, and a continuous switching sliding mode controller based on workspace vision is implemented to enhance the accuracy and stability of trajectory tracking. In addition, a virtual prototype of the CDPR with uncertainties is created in the simulation analysis software ADAMS, and co-simulation is performed with the control system designed in Simulink to validate the effectiveness of the proposed control strategy. Furthermore, a CDPR platform is established for trajectory tracking experiments using the visual-based position feedback method. The trajectory tracking performance with the three control schemes is then evaluated. The experimental results show that the continuous switching sliding mode control algorithm can significantly decrease trajectory tracking errors and exhibit superior trajectory tracking performance compared to the other control strategies. [ABSTRACT FROM AUTHOR]

Robotic manipulators are nowadays widely used in various underwater scenarios, but their motion control remains a challenging task due to hydrodynamic effects. This article proposes a novel adaptive fuzzy sliding mode control (AFSMC) strategy for precise and robust control of underwater manipulators. To simulate the movement of the robotic manipulators in the underwater environment, the Unified Robot Description Format (URDF) file of a custom-designed electric underwater manipulator is imported into the Simscape Multibody and the hydrodynamic disturbance is modeled according to Morison's equation. Furthermore, the proposed control strategy takes advantage of the universal approximation capability of fuzzy systems to avoid chattering and observe disturbances by adjusting the control gains of classical sliding mode control (CSMC). And adaptive laws are designed to update the parameters of the fuzzy systems. The strong friction caused by the seal is also compensated by actual test data. In the simulation experiments, a special environment of water flow and variable loads is considered. The results demonstrate that the AFSMC strategy can achieve high precision and strong robustness against disturbances for trajectory tracking. More importantly, the chattering caused by CSMC can be eliminated and the hydrodynamic disturbance can be estimated with high precision through the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive super-twisting global integral terminal sliding mode control algorithm for the trajectory tracking of autonomous robotic manipulators with uncertain parameters, unknown disturbances, and actuator faults. Firstly, a novel global integral terminal sliding mode surface is designed to ensure that the tracking errors of autonomous robotic manipulators converge to zero in finite time and the global robustness of the system is also enhanced. Then a new adaptive method is devised to deal with the adverse effect of nonlinear uncertainty. To suppress the chattering phenomenon, the adaptive super-twisting algorithm is used in this paper, which can ensure that the control torque is a continuous input signal. Based on the adaptive mechanism, the adaptive super-twisting global integral terminal sliding mode controller is developed to provide superior control performance. The stability analysis of the system is demonstrated by using the Lyapunov method. Ultimately, the effectiveness of the control scheme is confirmed by a simulation study. • A novel adaptive finite-time global integral terminal sliding mode control scheme is designed. • A novel global integral terminal sliding mode surface is proposed to ensure faster convergence and better global robustness. • A novel adaptive law is designed to adjust the estimation parameters according to the change of system state to avoid overestimation. • The actuator fault is considered to make the proposed control logic more suitable for practical applications. [ABSTRACT FROM AUTHOR]

This paper studies the three‐dimensional (3‐D) dynamic trajectory tracking control of an autonomous underwater vehicle (AUV). As AUV is a typical nonlinear system, each degree of freedom is strongly coupled, so the traditional control method based on the nominal model of AUV cannot guarantee the accuracy of the control system. To solve this problem, we first propose a prediction model based on a radial basis function neural network (RBF‐NN). The nonlinearity of AUV is learned and modeled offline by RBF‐NN based on previous data. This model can reflect the time sequence state and control variables of AUV. Secondly, to avoid the overfitting problem in network training based on the traditional gradient descent method, a new adaptive chaotic sparrow search algorithm (ACSSA) is proposed to optimize the network parameters, to improve the full approximation ability of RBF‐NN to nonlinear systems. To eliminate the steady‐state error caused by external interference during AUV trajectory tracking, a nonlinear optimizer is designed by updating the deviation of the NN model output layer. In each sampling period, the predictive control law is calculated online according to the deviation between the predicted value and the actual value. In addition, the stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the 3‐D dynamic trajectory tracking the performance of AUV under different external disturbances is verified by MATLAB/Simulink, and the results show that the proposed controller is more efficient and robust than the standard model predictive controller (MPC) controller and the standard NN model predictive controller (NNPC). [ABSTRACT FROM AUTHOR]

Underactuated Unmanned Surface Vessels (USVs) are widely used in civil and military fields due to their small size and high flexibility, and trajectory tracking control is a critical research area for underactuated USVs. This paper proposes a trajectory tracking control strategy using the Biologically Inspired Neural Network (BINN) for USVs to improve tracking speed and accuracy. A virtual control law is designed to obtain the required virtual velocity for trajectory tracking control, in which the velocity error is calibrated to ensure that the position error converges to zero. To observe and compensate for unknown and complex environmental disturbances such as wind, waves, and currents, a nonlinear extended state observer (NESO) is designed. Then, a controller based on Non-singular Terminal Sliding Mode (NTSM) is designed to resolve the problems of singular value and controller chattering and to improve the controller response speed. A BINN is introduced to simplify the process of differentiation, reduce the input values of the initial state, and solve the problem of thruster input saturation. Finally, the Lyapunov stability theory is utilized to analyze the stability of the proposed algorithm. The simulation results show that the proposed algorithm has a higher trajectory tracking accuracy and speed than traditional methods. [ABSTRACT FROM AUTHOR]

This work addresses the motion control problem for a 4-degree-of-freedom unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. A disturbance observer-based control scheme is proposed, which is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the developed EAOB has the ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC is implemented by feeding the estimated disturbances back into the MPC's prediction model, which forms an effective adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and robot operating system environment. The results show that the proposed method can effectively reject unpredictable disturbances and improve the UUV's control performance. [ABSTRACT FROM AUTHOR]