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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]

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.

Objective: To solve the problems of low control accuracy and poor motion stability in the trajectory tracking control method of parallel food sorting robots. Methods: On the basis of analyzing the structure of a four degree of freedom parallel robot, a combination of sliding mode control algorithm, fuzzy control algorithm, and improved bat algorithm was proposed for trajectory tracking control of a parallel food sorting robot. Optimizing the bandwidth of fuzzy algorithms by improving the bat algorithm, and adaptively adjusting the fuzzy gain and sliding surface slope of the sliding membrane control algorithm through the optimized fuzzy control algorithm, the tracking error of the controller was reduced, improve anti-interference ability, and verify the superiority of the proposed trajectory tracking control method. Results: The proposed trajectory tracking control method had an actual sorting accuracy of 99.90% and an average sorting time of 0.509 seconds. Conclusion: Compared with conventional methods, the proposed trajectory tracking control method has higher joint trajectory tracking accuracy, stronger anti-interference ability, and smoother output torque.

ObjectiveThe technology of multi-machine convoy driving has emerged as a focal point in the field of agricultural mechanization. By organizing multiple agricultural machinery units into convoys, unified control and collaborative operations can be achieved. This not only enhances operational efficiency and reduces costs, but also minimizes human labor input, thereby maximizing the operational potential of agricultural machinery. In order to solve the problem of communication delay in cooperative control of multi-vehicle formation and its compensation strategy, the trajectory control method of multi-vehicle formation was proposed based on model predictive control (MPC) delay compensator.MethodsThe multi-vehicle formation cooperative control strategy was designed, which introduced the four-vehicle formation cooperative scenario in three lanes, and then introduced the design of the multi-vehicle formation cooperative control architecture, which was respectively enough to establish the kinematics and dynamics model and equations of the agricultural machine model, and laied down a sturdy foundation for solving the formation following problem later. The testing and optimization of automatic driving algorithms based on real vehicles need to invest too much time and economic costs, and were subject to the difficulties of laws and regulations, scene reproduction and safety, etc. Simulation platform testing could effectively solve the above question. For the agricultural automatic driving multi-machine formation scenarios, the joint simulation platform Carsim and Simulink were used to simulate and validate the formation driving control of agricultural machines. Based on the single-machine dynamics model of the agricultural machine, a delay compensation controller based on MPC was designed. Feedback correction first detected the actual output of the object and then corrected the model-based predicted output with the actual output and performed a new optimization. Based on the above model, the nonlinear system of kinematics and dynamics was linearized and discretized in order to ensure the real-time solution. The objective function was designed so that the agricultural machine tracks on the desired trajectory as much as possible. And because the operation range of the actuator was limited, the control increment and control volume were designed with corresponding constraints. Finally, the control increment constraints were solved based on the front wheel angle constraints, front wheel angle increments, and control volume constraints of the agricultural machine.Results and DiscussionsCarsim and MATLAB/Simulink could be effectively compatible, enabling joint simulation of software with external solvers. When the delay step size d=5 was applied with delay compensation, the MPC response was faster and smoother; the speed error curve responded faster and gradually stabilized to zero error without oscillations. Vehicle 1 effectively changed lanes in a short time and maintains the same lane as the lead vehicle. In the case of a longer delay step size d =10, controllers without delay compensation showed more significant performance degradation. Even under higher delay conditions, MPC with delay compensation applied could still quickly respond with speed error and longitudinal acceleration gradually stabilizing to zero error, avoiding oscillations. The trajectory of Vehicle 1 indicated that the effectiveness of the delay compensation mechanism decreased under extreme delay conditions. The simulation results validated the effectiveness of the proposed formation control algorithm, ensuring that multiple vehicles could successfully change lanes to form queues while maintaining specific distances and speeds. Furthermore, the communication delay compensation control algorithm enables vehicles with added delay to effectively complete formation tasks, achieving stable longitudinal and lateral control. This confirmed the feasibility of the model predictive controller with delay compensation proposed.ConclusionsAt present, most of the multi-machine formation coordination is based on simulation platform for verification, which has the advantages of safety, economy, speed and other aspects, however, there's still a certain gap between the idealized model in the simulation platform and the real machine experiment. Therefore, multi-machine formation operation of agricultural equipment still needs to be tested on real machines under sound laws and regulations.

The utilization of the Delta parallel robot in high-speed and high-precision applications has been extensive, with motion stability being a critical performance measure. To address the inherent inaccuracies of the model and minimize the impact of external disturbances on motion stability, we propose an adaptive backstepping fractional-order non-singular terminal sliding mode control (ABF-NTSMC). Initially, by employing a backstepping algorithm, we select the virtual control for subsystems as the state variable function in joint space while incorporating a calculus operator to enhance fractional-order sliding mode control (SMC). Subsequently, we describe factors such as model uncertainty and external disturbance using a lumped uncertainty function and estimate its upper bound through an adaptive control law. Ultimately, we demonstrate system stability for our proposed control approach and provide an analysis of finite convergence time. The effectiveness of this presented scheme is demonstrated through simulation and experimental research. [ABSTRACT FROM AUTHOR]

This article discusses the problem of controlling the trajectory of a flapping-wing aircraft in the face of external disturbances. As the applications for flapping-wing aircraft have diversified, the external disturbances to which the system is exposed have become more complex. Existing control methods have difficulty with effectively counteracting these disturbances. Therefore, this paper suggests a control method that combines linear active disturbance rejection with model predictive control to solve the tracking problem under disturbances, improve the system's disturbance rejection capability, and ensure the accuracy of trajectory tracking. First, a linear active disturbance controller (LADRC) is developed for the position system to monitor and compensate for internal uncertainties and environmental disturbances in a timely manner. Secondly, the attitude control system is equipped with a model predictive controller (MPC) to effectively determine the optimal control variables and achieve stable attitude tracking. The method is evaluated through simulation studies to assess its performance in tracking a reference trajectory in the presence of disturbances. The findings demonstrate that the approach can accurately track the reference trajectory even when the system is subject to sinusoidal disturbances. This indicates that the method exhibits robustness and practicality. [ABSTRACT FROM AUTHOR]

In flying animals, wing morphology is typically assumed to influence flight behaviours. Whether seasonal polymorphism in butterfly morphology is linked to adaptive flight behaviour remains unresolved. Here, we compare the flight behaviours and wing morphologies of the spring and summer forms of two closely related butterfly species, Pieris napi and P. rapae. We first quantify three‐dimensional flight behaviour by reconstructing individual flight trajectories using stereoscopic high‐speed videography in an experimental outdoor cage. We then measure wing size and shape, which are characteristics assumed to influence flight behaviours in butterflies. We show that seasonal, but not interspecific, differences in flight behaviour might be associated with divergent forewing shapes. During spring, Pieris individuals are small and have elongated forewings, and generally fly at low speed and acceleration, while having a high flight curvature. On the contrary, summer individuals are larger and exhibit rounded forewings. They fly at high speed and acceleration, while having high turning acceleration and advance ratio. Our study provides one of the first quantitative pieces of evidence of different flight behaviours between seasonal forms of two Pieris butterfly species. We discuss the possibility that this co‐divergence in flight behaviour and morphology is an adaptation to distinct seasonal environments. Properly identifying the mechanisms underpinning such divergence, nonetheless, requires further investigations to disentangle the interacting effects of microhabitats, predator community, parasitoid pressure and behavioural differences between sexes. [ABSTRACT FROM AUTHOR]

Autonomous sailing is seen as one of the possible solutions to cope with the decrease in qualified personnel and to minimise the risk to humans and ships in challenging conditions. Autonomy is not limited to sailing a vessel safely through the seas, but it also includes docking the vessel. A feeder vessel distributes cargo and spends a relative large percentage of its time on (un)docking. Automating this part of the operation is expected to help save on resources. The objective of this work is to automatically approach a dock with an underactuated vessel. It comprises of both the design of a time-dependent trajectory, and a controller that can track this trajectory. The solution is tailored for our 71 m long feeder vessel designed for the eu-h2020 Moses project. The focus is on approaching the dock from cruising speed until the speed of the vessel is near-zero. The result of the study is a high-fidelity time simulation that shows the behaviour of the vessel in combination with the control system when it approaches a dock. The simulations show that the ship can approach the dock with coupled azimuthing thrusters to a speed when the bow thrusters become effective. The vessel then becomes fully actuated and can safely dock with its dynamical positioning system. [ABSTRACT FROM AUTHOR]

This research aims to design controllers based on the hedge-algebras (HA) theory to control differential robots that track reference trajectories. First, the HA-based controller (denoted as HA controller) is synthesized by selecting a suitable qualitative control rule base for the investigated model as a rule-based optimization problem. Then, the optimal HA-based controller (denoted as oHA controller) is established based on the problem of simultaneously optimizing the rule base, the reference interval of variables, and the fuzzy parameters of the variables. Optimization problems aim to minimize the distance between the robot and the reference trajectory. The optimization problems in this study use the Balancing composite motion optimization (BCMO) algorithm. A controller based on fuzzy set theory (denoted as FC controller) with the same parameters as the HA controller is also included for comparison. The simulation results show that the HA and oHA controllers demonstrate many advantages over the FC controller regarding reference trajectory tracking ability, calculation time, and control robustness. The main contribution of this work consists of (i) The development of a novel HA, oHA approaches to control a mobile robot to follow reference trajectories accurately; (ii) Providing optimal global-based BCMO in terms of minimal tracking error with computational efficiency; (iii) The investigation of one control rule base for HA and oHA controllers, which is effective for many different reference orbits; (iv) The development of a robust controller that adapts to the robot's geometric parameters changes; (v) The proposed controllers have superior performance results compared to controllers based on fuzzy set theory in terms of position error between the robot and the reference trajectory, control action calculation time, and robust ability to change robot parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a novel neural network-based control algorithm with fixed-time performance constraints for manipulator systems in workspaces. The algorithm efficiently controls the manipulator's trajectory tracking by tuning a preset performance function, thereby optimizing both speed and accuracy within a fixed timeframe. Initially, a tangent-type error transformation, applied through homogeneous embryonic transformation, ensures rapid convergence of tracking errors to a specific region. Subsequently, integrating a predetermined control strategy into the fixed-time stability framework ensures the system's state reaches a defined boundary within a finite period. Lastly, neural networks are employed to approximate dynamic parameters and adjust the controller, achieving optimal parameter approximation and significantly enhancing trajectory tracking robustness. Simulation analyses and comparisons confirm the controller's effectiveness and superiority in enhancing both the transient and steady-state performance of the control system. [ABSTRACT FROM AUTHOR]

Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of the navigation process is to determine the most efficient route for the hovercraft and regulate its movement along that route without any oscillation. The primary goal of this work is to determine the most efficient routes for a hovercraft operating in a global environment. The proposed approach, called Artificial Bee Colony Self Perception Particle Swarm Optimization (ABC-SPPSO), is utilized to accomplish this objective. The main benefit of utilizing the ABC-SPPSO algorithm is its ability to design the most efficient route while preventing collisions with stationary obstructions. In addition, a suggested controller that consists of a Feedforward Numerical Inverse Dynamic Controller (FNIDC) and a Feedback Neural Network Radial Basis Function (FNNRBF) control technique with the Grey Wolf Optimization (GWO) algorithm will be utilized to guide the hovercraft along predetermined paths. This suggested controller is designed to regulate the nonlinear dynamics of the hovercraft system in order to efficiently and rapidly generate the forces exerted by the starboard and portboard fans. These forces are utilized to control the orientation and position of the hovercraft. Moreover, the utilization of the suggested controller effectively reduces the differences between the desired and the actual positions in both the X-axis and the Y-axis. Additionally, the controller nearly eliminates any deviation in orientation and ensures a stable response without any oscillation. Specifically, the controller ensures that the hovercraft will promptly and accurately adhere to its intended trajectories. Ultimately, we verify the accuracy of the numerical simulation outcomes of the suggested control approach by contrasting them with those of other controllers, specifically in relation to the highest level of error improvement in the X-position and the Y-position. In particular, when comparing the proposed controller to the Improved Quasi-Velocities (IQV) controller, the results show that the suggested controller decreases the error rate of tracking on the X-position by 22% and enhances the tracking error rate on the Y-position by 14.8%. Furthermore, the suggested controller was evaluated against the terminal sliding mode controller (TSMC), and the results of the comparison indicate that the proposed controller enhances the error rate of tracking on the X-position by 50.7% and on the Y-position by 64.5%. Additionally, the suggested controller was evaluated against the nonlinear cascade controller, and the comparative analysis demonstrates that the suggested controller enhances the X-position and the Y-position tracking error rates by 25.9% and by 33%, respectively. Finally, from a comparative study with the neural network-based adaptive dynamic inversion controller, the proposed controller enhances the error rate of tracking on the X-position by 51% and on the Y-position by 42.9%. [ABSTRACT FROM AUTHOR]

In recent years, applications of inverse model-based control techniques have experienced significant growth in popularity and have been widely used in engineering applications, mainly in nonlinear control system design problems. In this study, a novel fuzzy internal model control (IMC) structure is presented for single-input-single-output (SISO) nonlinear systems. The proposed structure uses the forward and inverse dynamic Takagi–Sugeno (D-TS) fuzzy models of the nonlinear system within the IMC framework for the first time in literature. The proposed fuzzy IMC is obtained in a two-step procedure. A SISO nonlinear system is first approximated using a D-TS fuzzy system, of which the rule consequents are linearized subsystems derived from the nonlinear system. A novel approach is used to achieve the exact inversion of the SISO D-TS fuzzy model, which is then utilized as a control element within the IMC framework. In this way, the control design problem is simplified to the inversion problem of the SISO D-TS fuzzy system. The provided simulation examples illustrate the efficacy of the proposed control method. It is observed that SISO nonlinear systems effectively track the desired output trajectories and exhibit significant disturbance rejection performance by using the proposed control approach. Additionally, the results are compared with those of the proportional-integral-derivative control (PID) method, and it is shown that the proposed method exhibits better performance than the classical PID controller. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A modified far-field model (FFM) to simplify the calculation of the electromagnetic force is proposed and common interference forces of the dynamic model of Electromagnetic Formation Spacecraft (EMFS) are analyzed. • A novel proportional passive controller is designed as a nominal controller. For comparison, a saturation proportional-derivative (PD) controller is proposed. The proportional passive controller includes a damping term, which has the potential to avoid energy accumulation. The proposed proportional passive control method demonstrates good robustness and enables faster system stabilization. • Based on the observer determination of AFED, a fixed-time FTC method was developed. Although it can effectively handle unexpected failures of actuators, the design process is relatively complex [26]. The nature of actuator failures and efficiency factor information are determined before controller design, so unexpected actuator faults cannot be effectively handled [27]. Different from the FTC mentioned earlier, this paper proposes an ismc-based FTC scheme that can effectively suppress AFED without requiring a fault observer and the reconstruction of the nominal controller. This scheme combines an adaptive estimation method to estimate AFED in real-time, without requiring any information about the disturbance or actuator faults. Fault-tolerant control (FTC) is an essential consideration for the reason that the proper functioning of electromagnetic formation spacecraft (EMFS) actuators are challenged by the harsh space environment. An Integral-Type Sliding Mode Control (ISMC) scheme is proposed for actuator faults and external disturbances (AFED) caused by coil failure and external complex environment of EMFS system. Before the system state reaches the sliding mode surface, we use super-twisting algorithm to accelerate the reaching of the sliding mode surface and reduce the chattering. After reaching the sliding surface, a novel nominal proportional passive controller is proposed that integrates a damping term, thereby enhance system robustness and expediting system stabilization. Moreover, the FTC method proposed incorporates adaptive techniques to handle unknown AFED in real-time while ensuring system stability. This paper proposes a new adaptive FTC method that enhances the actuator's capability without requiring controller reconfiguration. Simulation results illustrate the effectiveness of this method for suppressing AFED in harsh space environment. [ABSTRACT FROM AUTHOR]

Wheeled mobile robots are prone to wheel slippage on soft and complex terrains, which affects trajectory tracking accuracy. To improve the trajectory tracking accuracy of robots under such conditions, an adaptive slip compensation controller (ASCC) based on fault‑tolerant control (FTC) is proposed. Firstly, a kinematic model of a four‑wheeled mobile robot (4 WMR) is established by considering wheel slippage rates, and the corresponding trajectory tracking error model is constructed. Then, based on the FTC method, a slippage compensation term is designed. The stability of the ASCC is proven using Lyapunov stability theory, achieving accurate compensation for the velocity error caused by wheel slippage. Finally, experiments under two working conditions are conducted on the 4 WMR. The results show that the designed controller enables the mobile robot to track the desired path under unknown wheel slippage rates, and it exhibits stronger robustness and higher tracking accuracy compared to the conventional controllers. [ABSTRACT FROM AUTHOR]

A tracking control algorithm for autonomous vehicles using state expansion is proposed. This algorithm, based on a three degrees of freedom (3-DOF) vehicle lateral dynamics model, extends the state variables of the traditional model predictive control (MPC) algorithm to an input-output dual feedback form based on the state expansion method. A dual feedback model predictive control (DF-MPC) algorithm was constructed. Based on this, taking the current speed and the curvature of the reference trajectory as the system input, and using the fuzzy control algorithm to dynamically adjust the prediction range of the DF-MPC trajectory tracking controller in real time, a variable predictive horizon double feedback model predictive control (VDF-MPC) trajectory tracking control method for autonomous vehicle was established. Through MATLAB/Simulink-CarSim joint simulation, the reliability of the established VDF-MPC trajectory tracking control method was verified. [ABSTRACT FROM AUTHOR]

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input–output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties. [ABSTRACT FROM AUTHOR]

This article presents a hierarchical control framework for autonomous vehicle trajectory planning and tracking, addressing the challenge of accurately following high-speed, at-limit maneuvers. The proposed time-optimal trajectory planning and tracking (TOTPT) framework utilizes a hierarchical control structure, with an offline trajectory optimization (TRO) module and an online nonlinear model predictive control (NMPC) module. The TRO layer generates minimum-lap-time trajectories using a direct collocation method, which optimizes the vehicle's path, velocity, and control inputs to achieve the fastest possible lap time, while respecting the vehicle dynamics and track constraints. The NMPC layer is responsible for precisely tracking the reference trajectories generated by the TRO in real time. The NMPC also incorporates a preview algorithm that utilizes the predicted future travel distance to estimate the optimal reference speed and curvature for the next time step, thereby improving the overall tracking performance. Simulation results on the Catalunya circuit demonstrated the framework's capability to accurately follow the time-optimal raceline at an average speed of 116 km/h, with a maximum lateral error of 0.32 m. The NMPC module uses an acados solver with a real-time iteration (RTI) scheme, to achieve a millisecond-level computation time, making it possible to implement it in real time in autonomous vehicles. [ABSTRACT FROM AUTHOR]

The paper presents a novel control strategy for autonomous vehicles, which can effectively handle the nonlinear effects and uncertainties of the controlled system. This solution is based on a new formulation of the ultra-local model-based control approach. This method can deal with the unknown dynamics and other uncertainties of the vehicle without any further measurement or complex modelling process. The control methods require a reduced-complexity nominal linear model of the system, and the unknown part is continuously estimated by the proposed ultra-local model. The proposed control-oriented vehicle modelling method leads to an increased performance level of vehicle motion. The effectiveness of the proposed method through a high-fidelity vehicle simulator (CarMaker) is illustrated. Moreover, the operation of the control through its implementation on a test vehicle of ThyssenKrupp is also demonstrated. [ABSTRACT FROM AUTHOR]

This paper presents a compliant variable admittance adaptive fixed-time sliding mode control (SMC) algorithm for trajectory tracking of robotic manipulators. Specifically, a compliant variable admittance algorithm and an adaptive fixed-time SMC algorithm are combined to construct a double-loop control structure. In the outer loop, the variable admittance algorithm is developed to adjust admittance parameters during a collision to minimize the collision time, which gives the robot compliance property and reduce the rigid collision influence. Then, by employing the Lyapunov theory and the fixed-time stability theory, a new nonsingular sliding mode manifold is proposed and an adaptive fixed-time SMC algorithm is presented in the inner loop. More precisely, this approach enables rapid convergence, enhanced steady-state tracking precision, and a settling time that is independent of system initial states. As a result, the effectiveness and improved performance of the proposed algorithm are demonstrated through extensive simulations and experimental results. [ABSTRACT FROM AUTHOR]

Dual humanoid robot collaborative control systems possess better flexibility and adaptability in complex environments due to their similar structures to humans. This paper adopts a distributed model predictive controller based on the leader–follower approach to address the collaborative transportation control issue of dual humanoid robots. In the dual-robot collaborative control system, network latency issues may arise due to unstable network conditions, affecting the consistency of dual-robot collaboration. To solve this issue, a communication protocol was constructed through socket communication for dual-robot collaborative consistency, thereby resolving the problem of consistency in dual humanoid robot collaboration. Additionally, due to the complex structure of humanoid robots, there are deficiencies in position tracking accuracy during movement. To address the poor accuracy in position tracking, this paper proposes a distributed model predictive control that considers historical cumulative error, thus enhancing the position tracking accuracy of dual-robot collaborative control. [ABSTRACT FROM AUTHOR]

The snake-like robot has superior flexibility in complex and extreme environments due to its long and narrow robotic arm and redundant degrees of freedom. Based on the structure of the underactuated snake-arm robot, two trajectory tracking algorithms based on equivalent rod length and equivalent virtual joint are proposed to realize the tracking of the target curve by the snake-arm robot and meet the motion control requirements of the snake-arm robot. Finally, a snake-like robot prototype with an arm length of 1 075 mm and seven joint units is designed and the trajectory tracking experiment is carried out. The results show that the snake-like robot can easily pass through a narrow pipe with a diameter of more than 90 mm. The smooth and vibration-free motion process verifies the correctness of the trajectory control algorithm and the rationality of the structural design. [ABSTRACT FROM AUTHOR]

As a bridge between planners and actuators, trajectory tracking is a critical and essential part of robot navigation, autonomous driving and some other fields. While traditional trajectory tracking methods generally have disadvantages such as poor tracking accuracy, high modeling requirements, and heavy computational load. This paper proposes an end-to-end trajectory tracking method based on reinforcement learning, and an information encoding network and a reinforcement learning policy network are constructed. A multi-task dense reward function for trajectory tracking is designed. For efficient encoding of local trajectory information, a self-attentive mechanism is developed. A virtual simulation environment is constructed for model training by modeling the trajectory tracking task. Comparing with model predictive control and pure pursuit in the tracking experiments of several reference trajectories, the obtained results show that the proposed method has significant advantages in terms of lateral tracking. [ABSTRACT FROM AUTHOR]

The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability. [ABSTRACT FROM AUTHOR]

In order to enhance the trajectory tracking accuracy of distributed-driven intelligent vehicles, this paper formulates the tasks of torque output control for longitudinal dynamics and steering angle output control for lateral dynamics as Markov decision processes. To dissect the requirements of action output continuity for longitudinal and lateral control, this paper adopts the deep deterministic policy gradient algorithm (DDPG) for longitudinal velocity control and the deep Q-network algorithm (DQN) for lateral motion control. Multi-agent reinforcement learning methods are applied to the task of trajectory tracking in distributed-driven vehicle autonomous driving. By contrasting with two classical trajectory tracking control methods, the proposed approach in this paper is validated to exhibit superior trajectory tracking performance, ensuring that both longitudinal velocity deviation and lateral position deviation of the vehicle remain at lower levels. Compared with classical control methods, the maximum lateral position deviation is improved by up to 90.5% and the maximum longitudinal velocity deviation is improved by up to 97%. Furthermore, it demonstrates excellent generalization and high computational efficiency, and the running time can be reduced by up to 93.7%. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses a trajectory tracking control problem for marine surface vessels (MSVs) in the presence of mismatched conditions and input saturation. Firstly, an MSV model under mismatched conditions is established to solve the problem of unreasonable modeling methods in the existing literature. Secondly, fixed‐time disturbance observers (FXDOs) are designed, where the designed FXDOs not only avoid selecting complicated parameters but also has simple structures, and the disturbance estimation errors can converge to the origin within a setting time. Thirdly, based on the FXDOs and an anti–saturation auxiliary dynamic system, a trajectory tracking control scheme in the presence of mismatched conditions and input saturation is constructed by virtue of fixed‐time integral sliding mode control (SMC), and the actual tracking error signals are uniformly fixed‐time bounded rather than finite‐time one. Fourthly, in order to decrease theoretical calculation values of the original setting time in fixed‐time control, the new setting time under three different parameter conditions is derived, and thus reducing the conservatism. Finally, the simulation results are provided to show the effectiveness and superiority of the designed tracking control scheme. [ABSTRACT FROM AUTHOR]

A bioinspired approach to immune systems with a novel set of fuzzy rules is proposed in this paper to provide adaptability to sliding mode control. As multi-robotic systems have attracted the attention of researchers due to their variety of applications, as well as relevant control issues in practical demands, trajectory tracking and its extension to formation control are addressed. Despite its robustness, some disadvantages of first-order sliding mode control must be addressed, such as chattering and the need for a priori knowledge of the bounds of uncertainties and disturbances. Considering that an artificial immune system is a bioinspired approach provided with adaptive regulation mechanisms, this conception motivates the study and investigation of an adaptive strategy inspired by immunity. The leader–follower framework is used, and the nonholonomic differential-drive wheeled mobile robot model is considered. A fuzzy system based on the effect of the immunological reaction is designed, and an adaptation law is proposed to adjust the gain online. A fuzzy boundary layer method is also employed in replacement for the discontinuous control signal. Lyapunov's theorem proves the stability. The simulation and experimental results show the effectiveness of the proposed control strategy, which is also compared with another control scheme of recent research. [ABSTRACT FROM AUTHOR]

Unmanned underwater vehicle-dual-manipulator systems (UVDMSs) have attracted much research due to their humanoid operation capabilities, which have the advantage of cooperative manipulations and transporting underwater objects. Meanwhile, collision avoidance of UVDMSs is more challenging than that of unmanned underwater vehicle-dual manipulator systems (UVMSs). In this work, a model predictive control (MPC) approach is proposed for collision avoidance in objects transporting tasks of UVDMSs. The minimum distances of mutual manipulators and frame obstacles are handled as velocity constraints in the optimization of the UVDMS's object tracking control. The command velocity generated by the model predictive kinematic controller is tracked by a dynamic inversion control scheme while model uncertainties are compensated by a neural network. Moreover, the tracking errors of the proposed dynamic controller are proved to be convergent by the Lyapunov method. At last, a three-dimensional (3D) UVDMS simulation platform is developed to verify the effectiveness of the proposed control strategy in the tasks of collision avoidance and object transport. [ABSTRACT FROM AUTHOR]

Trajectory tracking control of input-constrained systems is an essential problem in many control applications, including robotics. In this paper, we propose a constrained tracking controller for input affine nonlinear systems with convex input constraints based on tracking control Lyapunov functions (TCLFs). To deal with general convex input constraints, we first solve a convex optimization problem that minimizes the time derivative of TCLFs subject to convex input constraints; we refer to its optimal solution as minimizing input. Then, the proposed trajectory tracking is constructed by using the minimizing input and an appropriate scaling function. We prove that the proposed controller locally achieves trajectory tracking and satisfies the given convex input constraints. Finally, we demonstrate the effectiveness of the proposed controller by numerical simulations of a wheeled mobile robot. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the accuracy and stability of unmanned vehicle trajectory tracking, a vehicle motion control method based on weighted variable time domain model predictive control (MPC) was proposed. The grey relational analysis method was used to determine the optimal time domain under different speed conditions, and the Fourier approximation method was used to fit the time domain parameters. Taking the controller calculation time as the weight coefficient, a weighted variable time domain MPC semi-empirical model with time domain parameters varying with vehicle speeds was obtained. The model can select the relative optimal time domain according to the change of vehicle trajectory tracking speed, and improve the tracking accuracy and stability of the controller under different vehicle speeds. Selecting 50 and 100 km/h as representative vehicle speeds, the control effects of the traditional time-domain MPC controller and the weighted time-domain MPC controller were compared by simulation experiments. The results show that the weighted variable time domain controller can effectively improve the tracking performance of the controller, the maximum lateral deviation is reduced by 1.98%, the maximum yaw angle deviation is reduced by 60%, and the controller solution time is reduced by 7.2%. And it also has strong adaptability to different target speed conditions. [ABSTRACT FROM AUTHOR]

In response to the trajectory tracking control problem of manipulators under measurement disturbances, a novel multi-input multi-output discrete integral terminal sliding mode control scheme is proposed. Initially, this scheme establishes a dynamic model of a two-joint manipulator based on the Lagrangian dynamics analysis method. Subsequently, a discrete integral terminal sliding mode control law based on the dynamic model of the two joints is designed, incorporating delayed estimation of unknown disturbances and discretization errors in the manipulator system. To enhance the trajectory tracking accuracy of the control scheme and suppress the impact of sliding mode chattering on the manipulator system, an adaptive switching term is introduced into the discrete integral terminal sliding mode control law. The paper derives an adaptive discrete integral terminal sliding mode control scheme and provides stability proof for the proposed approach. Simulation experiments are conducted to compare the proposed adaptive discrete integral terminal sliding mode control scheme with classical discrete sliding mode control schemes and discrete integral terminal sliding mode control schemes. The simulation results demonstrate that the designed adaptive discrete integral terminal sliding mode control scheme maintains trajectory tracking errors within 0.004 radians for each joint of the manipulator, with minimal changes in control torque for each joint. The absolute integral of the control torque variations is calculated at 5.85 × 10 3 , which is lower than other control schemes, thereby validating the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

A novel variable structure controller based on sliding mode is developed for addressing the trajectory tracking challenge encountered by wheeled mobile robots. Firstly, the trajectory tracking error model under the global coordinate system is established according to the kinematic model of the wheeled mobile robot. Secondly, the novel sliding mode algorithm and backstepping method are introduced to design the motion controller of the system, respectively. Different sliding mode surfaces are formulated to guarantee rapid and stable convergence of the system's trajectory tracking error to zero. Ultimately, comparative simulation trials validate the controller's ability to swiftly and consistently follow the reference trajectory. In contrast to traditional controllers, this controller shows rapid convergence, minimal error, and robustness. [ABSTRACT FROM AUTHOR]

This paper addresses a trajectory tracking control algorithm for underactuated marine vehicles moving horizontally in which the current in the North–East–Down frame is constant. This algorithm is a modification of a control scheme based on the input-output feedback linearization method, for which the application condition was that the vehicle was symmetric with respect to the left and right sides. The proposed control scheme can be applied to a fully asymmetric model, and, therefore, the geometric center can be different from the center of mass in both the longitudinal and lateral directions. A velocity transformation to generalized vehicle equations of motion was used to develop a suitable controller. Theoretical considerations were supported by simulation tests performed for a model with 3 degrees of freedom, in which the performance of the proposed algorithm was compared with that of the original algorithm and the selected control scheme based on a combination of backstepping and integral sliding mode control approaches. [ABSTRACT FROM AUTHOR]

Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit–Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors. [ABSTRACT FROM AUTHOR]

The bridge crane, as an efficient transportation tool, isss widely used in various fields. Currently, research on bridge cranes mainly focuses on the single-pendulum domain. However, in the actual transportation process, the mass of the hook cannot be ignored, and it can cause relative oscillations of the crane, similar to a double-pendulum model. Therefore, many experts and scholars have researched double-pendulum bridge cranes. In the operation of a double-pendulum bridge crane with load hoisting/lowering, uncertainties such as frictional resistance and air resistance cannot be accurately fed back to the controller input, posing a significant challenge in designing anti-swing control strategies. In this paper, an adaptive coupled tracking anti-swing control strategy is proposed to address the above issues. Specifically, based on the principle of energy dissipation, more system parameter information is incorporated into the design of coupled trajectories. By combining the S-shaped trolley motion reference trajectory, an error-tracking trajectory for the trolley is designed. Considering the uncertainties of air resistance and frictional resistance, an adaptive rate is used to estimate the resistance coefficients online and feedback to the system input, ultimately designing an adaptive trajectory tracking coupled controller. The asymptotic stability of the equilibrium point of the closed-loop system is proven using the Lyapunov method and Barbalat lemma. Extensive experimental simulations are conducted to demonstrate the good control performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn. [ABSTRACT FROM AUTHOR]

We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive–evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the tracking performance and antidisturbance ability of the widely used proportional-integral-derivative (PID) controllers in practice. Towards this end, we consider a basic class of second-order nonlinear stochastic control systems subject to model uncertainties and external disturbances, and focus on the ability of the classical PID controller to track time-varying reference signals. First, under some suitable conditions on the system nonlinear functions, reference signals, and external disturbances, we show that such control systems can be stabilized in the mean square sense, provided that the three PID gains are selected from a stability region constructed in the paper. Besides, it is shown that the steady-state tracking error has an upper bound proportional to the sum of the varying rates of reference signals, the varying rates of external disturbances, and the intensity of random noises. Meanwhile, its proportional coefficient depends on the selection of PID gains, which can be made arbitrarily small by choosing suitably large PID gains. Finally, by introducing a desired transient process which is shaped from the reference signal, a new PID tuning rule is presented, which can guarantee both the expected steady state and transient tracking performance. [ABSTRACT FROM AUTHOR]

The study proposes a method for the trajectory tracking control of a fixed-wing unmanned aerial vehicle (UAV) based on the deep deterministic policy gradient (DDPG). First, the problem of controlling the trajectory of a fixed-wing UAV is combined with the reinforcement learning framework and transformed into a Markov decision process, and a DDPG agent is established in the framework of TensorFlow. Second, we conducted simulations to train and optimize the model in a 3D environment of trajectory tracking control and obtained an integrated DDPG-based trajectory tracking controller that can regulate functions ranging from the state of flight of the UAV to rudder control. Third, we constructed a digital simulation system to verify the proposed method while considering the influence of parametric uncertainties, measurement-induced noise, and delays in the response of the control system. The effectiveness and robustness of the proposed DDPG controller were verified by comparing its performance with that of traditional proportional-integral-derivative (PID) control. Unmanned aerial vehicles have gained widespread use across numerous sectors of society. However, the control process for fixed-wing UAVs is more intricate and challenging than that for rotor type UAVs due to the coupled and nonlinear nature of their dynamic models. To address this issue, decoupling and splitting hierarchical control methods are commonly employed. However, these methods can introduce errors and affect flight performance. In contrast, artificial intelligence, particularly deep learning and reinforcement learning, has made significant advancements and found successful applications in areas such as robot control and gaming. To address the challenges posed by complex forms, excessive measurement information, and manual parameter tuning associated with traditional control approaches for fixed-wing UAVs, this paper introduces a deep reinforcement learning algorithm, i.e., the deep deterministic policy gradient, into the control process of fixed-wing UAVs. The paper presents a trajectory tracking control policy for UAVs, which efficiently achieves the task of tracking a desired trajectory without the need for selecting multiple working state nodes or designing linear control laws. Further, through simulation verification, the proposed policy demonstrates adaptability to interference caused by parameter bias, measurement noise, and control system response delays while achieving control accuracy comparable with classical control methods. [ABSTRACT FROM AUTHOR]

To improve the accuracy of path tracking and the vehicle handling stability in the process of automatic driving tracking, a trajectory tracking control method based on model predictive control principle and angle compensation was proposed. The overall control structure was divided into two layers, and the upper layer was the trajectory tracking control layer. According to the obtained road information and the driving state of the vehicle, the front wheel steering control input was determined by the model predictive control algorithm. In the lower layer, the sliding mode control theory was used to design the angle compensator, and the yaw rate deviation of the vehicle was taken as the control target to achieve accurate tracking. The results show that compared with the single-point preview strategy, the trajectory tracking control strategy based on model prediction and angle compensation can better control the vehicle to achieve trajectory tracking, and the peak values of yaw rate and sideslip angle are significantly reduced with better stability. [ABSTRACT FROM AUTHOR]

To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method. [ABSTRACT FROM AUTHOR]

[Objective] The technology of multi-machine convoy driving has emerged as a focal point in the field of agricultural mechanization. By organizing multiple agricultural machinery units into convoys, unified control and collaborative operations can be achieved. This not only enhances operational efficiency and reduces costs, but also minimizes human labor input, thereby maximizing the operational potential of agricultural machinery. In order to solve the problem of communication delay in cooperative control of multi-vehicle formation and its compensation strategy, the trajectory control method of multi-vehicle formation was proposed based on model predictive control (MPC) delay compensator. [Methods] The multi-vehicle formation cooperative control strategy was designed, which introduced the four-vehicle formation cooperative scenario in three lanes, and then introduced the design of the multi-vehicle formation cooperative control architecture, which was respectively enough to establish the kinematics and dynamics model and equations of the agricultural machine model, and laied down a sturdy foundation for solving the formation following problem later. The testing and optimization of automatic driving algorithms based on real vehicles need to invest too much time and economic costs, and were subject to the difficulties of laws and regulations, scene reproduction and safety, etc. Simulation platform testing could effectively solve the above question. For the agricultural automatic driving multi-machine formation scenarios, the joint simulation platform Carsim and Simulink were used to simulate and validate the formation driving control of agricultural machines. Based on the single-machine dynamics model of the agricultural ma‐ chine, a delay compensation controller based on MPC was designed. Feedback correction first detected the actual output of the object and then corrected the model-based predicted output with the actual output and performed a new optimization. Based on the above model, the nonlinear system of kinematics and dynamics was linearized and discretized in order to ensure the real-time solution. The objective function was designed so that the agricultural machine tracks on the desired trajectory as much as possible. And because the operation range of the actuator was limited, the control increment and control volume were designed with corresponding constraints. Finally, the control increment constraints were solved based on the front wheel angle constraints, front wheel angle increments, and control volume constraints of the agricultural machine. [Results and Discussions] Carsim and MATLAB/Simulink could be effectively compatible, enabling joint simulation of software with external solvers. When the delay step size d=5 was applied with delay compensation, the MPC response was faster and smoother; the speed error curve responded faster and gradually stabilized to zero error without oscillations. Vehicle 1 effectively changed lanes in a short time and maintains the same lane as the lead vehicle. In the case of a longer delay step size d =10, controllers without delay compensation showed more significant performance degradation. Even under higher delay conditions, MPC with delay compensation applied could still quickly respond with speed error and longitudinal acceleration gradually stabilizing to zero error, avoiding oscillations. The trajectory of Vehicle 1 indicated that the effectiveness of the delay compensation mechanism decreased under extreme delay conditions. The simulation results validated the effectiveness of the proposed formation control algorithm, ensuring that multiple vehicles could successfully change lanes to form queues while maintaining specific distances and speeds. Furthermore, the communication delay compensation control algorithm enables vehicles with added delay to effectively complete formation tasks, achieving stable longitudinal and lateral control. This confirmed the feasibility of the model predictive controller with delay compensation proposed. [Conclusions] At present, most of the multi-machine formation coordination is based on simulation platform for verification, which has the advantages of safety, economy, speed and other aspects, however, there's still a certain gap between the idealized model in the simulation platform and the real machine experiment. Therefore, multi-machine formation operation of agricultural equipment still needs to be tested on real machines under sound laws and regulations. [ABSTRACT FROM AUTHOR]

Wheeled Mobile Robots (WMRs) are systems with applications in diverse fields such as transportation, civilian services, military use, and space exploration. Then, their use will continue increasing, making WMRs an essential research topic that deserves further study. To this end, this work presents a novel observer-based finite-time controller for trajectory tracking control of WMRs disturbed by kinematic disturbances. In the proposed approach, the kinematic model of the WMR is transformed into a set of two decoupled second-order systems. Then, the proposed controller is divided into two parts. The first one employs an observer to estimate the effect of the kinematic disturbances. The second part consists of a finite-time controller designed to achieve finite-time convergence of the tracking error. A detailed synthesis procedure theoretically demonstrates the feasibility of the proposed controller. Subsequently, the proposed scheme is compared against finite-time, feedback, and H ∞ controllers. Exhaustive numerical simulations show that the proposed new control methodology achieves the trajectory tracking objective despite kinematic disturbances and outperforms the other control procedures. Finally, some comments and numerical results are given to clarify how the proposed control methodology can be used to design new controllers for trajectory tracking in WMRs and demonstrate that the new proposal remains to have a good performance when the system's coordinates are corrupted by measurement noise. [Display omitted] • A new observer-based finite-time control for wheeled mobile robots is proposed. • The new control scheme permits achieving the trajectory-tracking task efficiently. • The novel methodology outperforms existing finite-time, feedback, and H ∞ methodologies. • Excellent performance is obtained even in the presence of kinematic disturbances and measurement noise. • The proposed scheme permits the design of new controllers for WMRs easily. [ABSTRACT FROM AUTHOR]

With the rapid development of today's society, the traffic environment has become more and more complex. As an essential part of intelligent vehicles, trajectory tracking has attracted significant attention for its stability and safety. It is prone to poor accuracy and instability in extreme working conditions like high speed. In this paper, a trajectory tracking control strategy to ensure lateral stability at a high speed and low attachment limit conditions is proposed for distributed drive vehicles. The model predictive controller (MPC) was used to control the front wheel angle, and the particle swarm optimization (PSO) algorithm was designed to optimize the MPC control parameters adaptively. The sliding mode controller controls the rear wheel angle, and the vehicle instability degree is judged by analyzing the β − β ˙ phase plane. The controllers of different instability degrees are designed herein. Finally, a torque divider is designed to consider the actuation anti-slip. The designed controller is verified by Carsim and MATLAB-Simulink co-simulation. The results show that the trajectory tracking controller designed in this paper effectively improves the tracking accuracy under the premise of ensuring stability. [ABSTRACT FROM AUTHOR]