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Copyright of Acta Scientiarum Naturalium Universitatis Pekinensis is the property of Editorial Office of Acta Scientiarum Naturalium Universitatis Pekinensis 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.)

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.

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.)

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.)

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]

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]

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]

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]

[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]

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.)

This paper analyzes the characteristics of the trajectory tracking control problem of the manipulator and establishes a two-degree-of-freedom manipulator dynamic model. In order to solve the problem that generalized predictive control(GPC) algorithm is difficult to apply to nonlinear systems, a feedback linearization-based generalized predictive control(FL-GPC) algorithm framework is designed. The bottom layer of the algorithm is the linear system predictive control and the non-linear term is replaced by the estimated value. The upper level is iteratively revised estimates and the non-linear term is estimated using the iterative calculation method. Finally, the FL-GPC algorithm is used to simulate the static and dynamic trajectory tracking tasks of a two-degree-of-freedom manipulator. Simulation results show that the algorithm can perform effective manipulator trajectory tracking control.

An active disturbance suppression and model predictive control (MPC) strategy is proposed for the quadrotor precision trajectory tracking control problem under disturbance action and model uncertainty. The model predictive controller estimates and compensates the disturbance by the extended state observer (ESO) and disturbance observer (DO) to achieve the position loop accurate control. In the presence of external disturbances and parameter uncertainties, it is demonstrated through simulation experiments that the proposed method improves the robustness to modeling errors and disturbances, while achieving smooth tracking of the reference trajectory.

ObjectiveIn order to tackle the issue of the poor navigation stability of unmanned surface vehicles (USVs) under interference conditions, an intelligent control parameter adjustment strategy based on the deep reinforcement learning (DRL) method is proposed. MethodA dynamic model of a USV combining the line-of-sight (LOS) method and PID navigation controller is established to conduct its navigation control tasks. In view of the time-varying characteristics of PID parameters for course control under interference conditions, the DRL theory is introduced. The environmental state, action and reward functions of the intelligent agent are designed to adjust the PID parameters online. An improved deep deterministic policy gradient (DDPG) algorithm is proposed to increase the convergence speed and address the issue of the occurrence of local optima during the training process. Specifically, the original experience pool is separated into success and failure experience pools, and an adaptive sampling mechanism is designed to optimize the experience pool playback structure. ResultsThe simulation results show that the improved algorithm converges rapidly with a slightly improved average return in the later stages of training. Under interference conditions, the lateral errors and heading angle deviations of the controller based on the improved DDPG algorithm are reduced significantly. Path tracking can be maintained more steadily after fitting the desired path faster.ConclusionThe improved algorithm greatly reduces the cost of training time, enhances the steady-state performance of the agent in the later stages of training and achieves more accurate path tracking.

ObjectiveAiming at the difficulties of the accuracy maintenance and tracking control of unmanned surface vessels (USVs) operating in narrow lakes and culverts, an intelligent predictive control method for trajectory tracking is proposed on the basis of a self-developed small torpedo-type USV.MethodsFirst, a self-developed nonlinear state space model of the underactuated USV is constructed. An intelligent predictive controller is designed on the basis of the model predictive control design concept and combined with an improved particle swarm optimization （PSO） algorithm to make online decisions, optimize the performance indicators at every moment and correct the predicted state. Finally, simulation and lake tests are carried out to test the tracking performance of the system on reference trajectories, and the tracking performance is compared with that of the linear model predictive controller. ResultsThe results show that the designed intelligent predictive controller has fast response speed, small overshoot and good anti-interference capabilities.ConclusionThe proposed method can not only be applied to the tracking systems of small torpedo-type USVs, but can also provide references for other USV tracking systems.

To improve the stability and tracking accuracy of intelligent driving vehicle tracking controller, a lateral tracking control strategy based on LQR control algorithm and driver preview model is proposed, and combined with the longitudinal PID control algorithm to achieve lateral and longitudinal control. Firstly, a LQR controller with feed-forward is established, and the weight parameters of the LQR controller are optimized by gradient descent optimization algorithm. On this basis, the preview model of the driver is introduced, and an adaptive controller for preview distance based on experience is designed. Secondly, dual PID longitudinal controllers are established for velocity control. Finally, the test verification is carried out through Carsim and Matlab/Simulink joint simulation and real vehicle. The results show that the maximum lateral deviation of simulation is less than 0.035 m, the maximum heading deviation is less than 0. 09 rad, the overall trend of the planned trajectory can also be well followed under real vehicle test conditions, and the front wheel rotation angle and yaw rate change smoothly. The controller can ensure high-precision and smooth trajectory tracking, and it is more obvious at high velocity. [ABSTRACT FROM AUTHOR]

To address the issues of complex disturbance and input saturation that affect the trajectory tracking accuracy of the robotic manipulator, an adaptive integrated sliding mode control technique based on input saturation compensation was proposed. Firstly, an auxiliary function was designed based on the adaptive control method to get rid of the input saturation trait. Secondly, the trajectory tracking control law was designed by the estimated upper limit of complex disturbance and the non-singular integral terminal sliding mode control method, which made the tracking error converge to the zero neighborhood quickly and weakened chattering. The Lyapunov stability theorem was used to demonstrate that the algorithm could ensure the finite-time convergence of trajectory tracking error. Finally, the effectiveness of the adaptive non-singular fast terminal sliding mode control method was verified by comparing with the existing control methods through simulation and experimentation. The results show that each joint of the manipulator can track the desired trajectory within 0.5 s and 0.8 s in simulation and experiment, respectively, and the tracking error can be reduced to 1.2×10-3 rad and 8.7×10-3 rad, which can effectively solve the input saturation problem of the manipulator and lessen the influence of complex disturbance on the control accuracy of the system trajectory tracking. [ABSTRACT FROM AUTHOR]

An active disturbance suppression and model predictive control (MPC) strategy is proposed for the quadrotor precision trajectory tracking control problem under disturbance action and model uncertainty. The model predictive controller estimates and compensates the disturbance by the extended state observer (ESO) and disturbance observer (DO) to achieve the position loop accurate control. In the presence of external disturbances and parameter uncertainties, it is demonstrated through simulation experiments that the proposed method improves the robustness to modeling errors and disturbances, while achieving smooth tracking of the reference trajectory. [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 Food & Machinery is the property of Food & Machinery 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.)

Copyright of Automobile Technology is the property of Automobile Technology 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 Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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.)

A sliding mode trajectory tracking control with fast stationary power reaching law was proposed to address the problem of position and attitude deviation in the trajectory tracking process of automated guided vehicle(AGV) wheeled parking robots applied in underground garages. First, a kinematic model was established under the coordinate system of the parking robot, and the kinematic model of the parking robot under the global coordinate system was obtained through the transformation relationship between the global coordinate system and the robot coordinate system. Then, the Lyapunov direct method was used to design the sliding mode switching function, and a fast stationary power reaching law was adopted to quickly reach the sliding mode switching surface of the parking AGV from the deviation state, enabling the AGV parking robot to quickly and stably track the given reference trajectory. Finally, simulation experiments were conducted using MATLAB, and the simulation results verified the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Regarding with problems of low quality of underwater images and difficulty of underwater autonomous localization faced by bionic robotic fish in underwater operations, an enhanced underwater image algorithm with color equalization and a priori fusion of G-B channels was proposed. The algorithm was combined with visual SLAM construction methods to enhance visual 3D reconstruction of underwater images. Underwater image processing experiments, underwater environment visual 3D reconstruction experiments, and motion trajectory tracking experiments were carried out in different water environments. The results show that the method effectively improves the comprehensive quality of underwater images. The feature matching efficiency is improved by 16.03%, and the error between the real trajectory and the estimated trajectory is about 7.99 mm on average. [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 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.)

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.

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.

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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

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To address the poor tracking accuracy, the high probability of sideslip and instability of intelligent vehicles in extreme working conditions with high speed and low adhesion, this paper proposes a layered control architecture trajectory tracking algorithm for distributed electric drive intelligent vehicles. Firstly, a Linear Time-Varying Model Predictive Control (LTVMPC) with varying degrees of freedom is built on the three-degree-of-freedom monorail vehicle dynamics model. LTVMPC is employed to solve the constrained optimization problem by rolling to obtain the optimal front wheel angle. In the meantime, the desired torque output of the PID longitudinal driver model is empolyed to achieve vehicle speed tracking. Secondly, a lower direct yaw moment controller is designed through Linear Quadratic Regulator (LQR) and the calculated additional yaw moment is distributedbased on vertical load optimization distribution method. Finally, the co-simulation platform of Carsim and Matlab/Simulink is built, and the effectiveness of the trajectory tracking control is simulated and analyzed. It is shown the traj'ectory tracking control strategy significantly improves the trajectory tracking accuracy and lateral stability of distributed electric drive intelligent vehicles on both high-speed & high adhesion road surfaces and high-speed & low adhesion ones. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China 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.)

Aiming at the problems of low intelligence of current tunneling equipment, difficulty in describing over-excavation, under-excavation and abnormal collision in tunneling process, and difficulty in adapting traditional automatic cutting and memory cutting technology to complex geological conditions, a digital twin-driven virtual teaching memory cutting control method for cantilever roadheader is proposed. By analyzing the research situation of digital twin technology in the field of intelligent coal mining, the overall scheme of memory cutting control system of cantilever roadheader driven by digital twin is designed, and the key technology of memory cutting of cantilever roadheader under complex working conditions is studied. Firstly, the characteristics of digital twin and virtual reality technology are fully utilized to study the virtual teaching strategy under complex working conditions. Based on the Unity3D platform, the virtual twin model of the working face and equipment with the same size of the corresponding entity, the kinematics model of the cutting unit and the virtual collision detection model are established. The virtual model movement is controlled through the intelligent interactive interface at the virtual end, and the teaching trajectory is designed and optimized according to the worker’s experience, so that it can be used as the target expected trajectory of trajectory tracking to make up for the excessive dependence on the worker’s experience caused by the traditional underground manual teaching due to the harsh working conditions. Secondly, in order to improve the quality of section forming, the control method of teaching trajectory tracking and reproduction in memory automatic cutting stage is studied. The dynamic model of cutting part is established by Lagrange method, and the tracking control accuracy of end effector to teaching trajectory is improved by combining iterative learning with sliding mode control. Finally, the simulation control platform of the memory cutting of the cantilever roadheader is built. Through the real-time data transmission and interaction between the virtual space and the physical space and between the modules, the three-dimensional visual simulation of the memory cutting virtual teaching and trajectory tracking control process is completed in the virtual space, and then the memory automatic cutting trajectory tracking control command is generated and sent to the end effector of the physical entity of the cantilever roadheader to drive it to carry out the section forming cutting according to the teaching trajectory. At the same time, the physical sensor collects the pose data of the cantilever roadheader fuselage and the cutting arm, and reversely drives the virtual model to move synchronously. The closed-loop control of robot virtual model and physical entity is realized. On this basis, the virtual and real synchronization of the system, the motion consistency between the virtual prototype and the physical prototype, and the trajectory tracking and reproduction control accuracy are verified. The experimental results show that the system data transmission delay is low, which can ensure the virtual and real consistency and synchronization, and the trajectory tracking control accuracy meets the actual use requirements. This method provides a new idea for memory cutting and intelligent control of tunneling equipment.

Aimed at the problem of insufficient tracking accuracy caused by the uncertainty of the system model during the motion of a two-joint manipulator, a trajectory tracking control method for the manipulator based on Immersion and Invariance (I&I) adaptive was proposed. The method was used to design a parameter estimator for the mass matrix. In order to solve the problem of difficulty in solving the partial differential equation in the design process of the method, a state filter was introduced to obtain an approximate solution, and the error between the approximate solution and the real solution was eliminated by introducing a dynamic scaling factor. The designed I&I adaptive parameter estimator was combined with a backstepping controller to make the designed controller adaptive to uncertain parameters. Simulation results demonstrate that the method can improve the control accuracy of the manipulator significantly, and it has certain application value.

ObjectiveThis study addresses the engineering problem of underwater tracked dredging robots sliding due to insufficient adhesion when dredging caissons in bridge construction. Therefore, a trajectory tracking controller based on the barrier Lyapunov function (BLF) is designed. MethodThe algorithm considers the non-coincidence of the centroid and geometric center, influence of unknown bounded disturbances and system dynamic uncertainty to establish kinematic and dynamic motion models. A terminal sliding mode observer (TSMO) is used to approximate external disturbances and system dynamic uncertainty in finite time. The stability of the control system is verified by time-varying symmetric finite time BLF stability analysis, and control failure is prevented by limiting the velocity state of the system. ResultsThe simulation results show that the robot reaches the desired trajectory smoothly and quickly under the control of the designed controller. ConclusionThe proposed algorithm can limit the velocity state of the robot system to an interval that conforms to the actual engineering practice.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department 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 Automotive Engineer (1674-6546) is the property of Auto Engineering 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.)

Quadruped robot has become a hot topic in the international community because it can adapt to the complex terrain environment using the way of detached feet. In view of the lack of bionic characteristics of the foot trajectory of the quadruped robot and the inconsistency between the walking effect and that of mammals, this study establishes the three-joint leg mechanism with the help of SolidWorks, obtains the leg kinematic model, and analyzes the leg trajectory motion by referring to the laws of biological movement. Combined with bionic characteristics and based on compound cycloid and quintic polynomial, a foot trajectory of compound cycloid quintic polynomial is planned in this study, which not only meets the law of bionic motion, but also meets the requirements of trajectory planning. In this study, MATLAB and ADAMS co-simulation is used to realize the kinematic simulation of single leg, and the prototype and control system are used to realize the physical motion of single leg. The experimental results show that the single-leg motion of the quadruped robot can realize the planned trajectory curve motion, and the acceleration is 0 at the second second, which is basically consistent with the simulation trajectory effect, which verifies the effectiveness of the proposed algorithm to a certain extent. [ABSTRACT FROM AUTHOR]