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

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

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

This 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]

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.)

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]

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]

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]

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.)

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]

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]

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.)

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.

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]

This paper investigates the model predictive control (MPC) for the trajectory tracking of the unmanned vehicle system with bounded disturbances and actuator saturation based on the unscented Kalman filter (UKF). In order to obtain accurate system state, the linear matrix inequality (LMI)-constrained UKF is addressed by solving the LMI optimization problem. Moreover, by expressing the saturating linear feedback law as convex hull and describing the stability of the vehicle kinematics model with bounded disturbance via the quadratic bounded theorem, a model predictive controller to achieve trajectory tracking is proposed by solving the infinite horizon optimization problem. The effectiveness of the proposed approach is verified by the co-simulation platform of Matlab/Simulink and Carsim. The results of simulations show that this approach can improve the accuracy of state estimation as well as the trajectory tracking control. [ABSTRACT FROM AUTHOR]

Summary: A model predictive cascade controller is proposed in this article to resolve the actuator saturation problem of underwater human‐occupied vehicle (HOV) trajectory tracking. For the kinematic model, a grasshopper optimization‐based model predictive control (MPC) is developed to obtain control velocities with small fluctuations in allowable regions. Based on the desired velocities obtained from the improved kinematic control, the HOV dynamic model is used to generate the corresponding torques and forces using sliding mode control. With the control velocities and forces given by the cascade control strategy, the elimination of actuator saturation during the HOV trajectory tracking can be realized. The proposed controller is verified by simulations using a numerical example based on a four degrees of freedom (4‐DOFs) HOV. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of controlled atmospheric re-entry, which has recently gained growing interest from the aerospace scientific and industrial communities for the economic and environmental benefits related to the development of reusable space vehicles. In this context, an original technique based on the incremental formulation of the Model Predictive Control methodology is proposed to tackle the trajectory tracking problem of a small satellite equipped with a deployable heat shield in the de-orbiting phase, focusing on a range of altitude from 300 km to 130 km, i.e., before the atmosphere is dense enough to begin a terminal re-entry phase. The proposed control logic allows following a given pre-determined guidance law, limiting position offsets due to unmodelled disturbances or uncertainties in the knowledge of spacecraft parameters (e.g., drag coefficient). An extensive numerical simulation campaign to assess the control performances in different perturbing conditions has demonstrated its ability to reach a km-error level in the position error at the final altitude of 130 km. • Controlled deorbiting based on aerodynamic drag modulation is addressed. • An Incremental formulation of the MPC controller is proposed for trajectory-tracking. • Performance assessment is carried out in a realistic simulation environment. • The achieved tracking error is kept below 1 km at re-entry interface (130 km). [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.)

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

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

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.

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

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

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

Due to the characteristics of good concealment ability and strong mobility, multiple, small spherical underwater robot formations play an important role in near coast defense missions, such as cruising, reconnaissance, surveillance, and sensitive target capturing. Referring to the formation problem for underwater small spherical robots with limited energy, perception, and computation abilities, a trajectory tracking-based formation strategy that transforms the complex formation tracking problem into a simple trajectory tracking problem of a single robot is provided. Two layers are designed in the formation tracking strategy. The upper layer is a virtual structure-based formation algorithm. The bottom layer is a tracking controller based on model predictive control (MPC). The formation algorithm is in charge of calculating reference trajectory for each robot in the formation according to the global formation path. The MPC-based dynamic controller for each robot is designed to track the self reference trajectory. Compared with the model predictive control method used for the traditional trajectory tracking problem of a single robot, this paper additionally considers the formation constraints and the internal collision avoidance. In addition, the extended state observer (ESO) is utilized to estimate the lumped disturbance composed of environment disturbance and the inaccurate dynamic model of a small spherical robot. Not only are the numerical simulations based on MATLAB v.2015a, but physical simulations based on self-building multi-spherical robot formation platform are also carried out. Furthermore, through using two small spherical robots, a formation tracing experiment is conducted. All of the results prove that the proposed formation method is feasible and practical for small spherical robots. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University: Engineering Science is the property of Editorial Office of Journal of Zhengzhou University: Engineering Science 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.)

Accuracy and efficiency are two important performances of model predictive control in trajectory tracking and they are seriously affected by the control parameters of model predictive control. To make the model predictive control with high accuracy and efficiency simultaneous, the paper proposed a strategy to tune the control parameters for model predictive control. The proposed strategy converts the tuning problem to a multi-objective optimization problem and employs non-dominated sorting genetic algorithm (NSGA-II) to solve it. The proposed strategy is employed to tune the control parameters for a classical model predictive control in a typical trajectory tracking condition. The simulation results show that the comprehensive performances of model predictive controller tuned by the proposed method are better than other tuning methods. The proposed tuning strategy is validated and it can be applied to tune the control parameters for model predictive control in trajectory tracking. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry 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.)

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]

Roboat is an autonomous surface vessel (ASV) for urban waterways, developed as a research project by the AMS Institute and MIT. The platform can provide numerous functions to a city, such as transport, dynamic infrastructure, and an autonomous waste management system. This paper presents the development of a learning-based controller for the Roboat platform with the goal of achieving robustness and generalization properties. Specifically, when subject to uncertainty in the model or external disturbances, the proposed controller should be able to track set trajectories with less tracking error than the current nonlinear model predictive controller (NMPC) used on the ASV. To achieve this, a simulation of the system dynamics was developed as part of this work, based on the research presented in the literature and on the previous research performed on the Roboat platform. The simulation process also included the modeling of the necessary uncertainties and disturbances. In this simulation, a trajectory tracking agent was trained using the proximal policy optimization (PPO) algorithm. The trajectory tracking of the trained agent was then validated and compared to the current control strategy both in simulations and in the real world. [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.

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.

Maritime Autonomous Surface Ships (MASS) perfectly fit into the future vision of merchant fleet. MASS autonomous navigation system combines automatic trajectory tracking and supervisor safe trajectory generation subsystems. Automatic trajectory tracking method, using line-of-sight (LOS) reference course generation algorithm, is combined with model predictive control (MPC). Algorithm for MASS trajectory tracking, including cooperation with the dynamic system of safe trajectory generation is described. It allows for better ship control with steadystate cross-track error limitation to the ship hull breadth and limited overshoot after turns. In real MASS ships path is defined as set of straight line segments, so transition between trajectory sections when passing waypoint is unavoidable. In the proposed control algorithm LOS trajectory reference course is mapped to the rotational speed reference value, which is dynamically constrained in MPC controller due to dynamically changing reference trajectory in real MASS system. Also maneuver path advance dependent on the path tangential angle difference, to ensure trajectory tracking for turns from 0 to 90 degrees, without overshoot is used. All results were obtained with the use of training ship in real-time conditions. [ABSTRACT FROM AUTHOR]

The motion of unmanned surface vehicles (USVs) is frequently disturbed by ocean wind, waves, and currents. A poorly designed controller will cause failures and safety problems during actual navigation. To obtain a satisfactory motion control performance for the USVs, a model predictive control (MPC) method based on an improved Nonlinear Disturbance Observer (NDO) is proposed. First, the USV model is approximately linearized and MPC is designed for the multivariable system with constraints. To compensate for the influence of disturbances, an improved NDO is designed where the calculation time for MPC is reduced. Finally, comparison simulations are conducted between MPC with the original NDO and MPC with an improved NDO, and the results show that they have similar performances to the USVs. However, the proposed method has fewer parameters that need to be tuned and is much more time-saving compared to MPC with a traditional NDO. [ABSTRACT FROM AUTHOR]

A novel convertible unmanned aerial vehicle (UAV) with four tiltable rotors and a tandem-wing system has been developed. Considering the aerodynamic effect caused by the rotor-induced velocity, a mathematical model that contains the traditional free airstream analysis and rotor-induced effect analysis is proposed, from which the precise equilibrium point of the control inputs and states can be derived. Moreover, a control allocation algorithm is designed to provide the mapping relationship between traditional input variables and specific input variables of the UAV, so that the complicated mathematical model can be linearized for the design of model predictive control (MPC) system. In order to handle the control input constraints of the UAV system, an MPC system is applied for the trajectory tracking during the cruising phase. The simulation results demonstrate that the proposed model predictive control system has stability, accuracy without a random disturbance and quick response capabilities with a random disturbance during cruising trajectory tracking, which are in high demand for the quick UAV flight system. [ABSTRACT FROM AUTHOR]

Efficient trajectory tracking approaches can enable autonomous vehicles not only to get a smooth trajectory but to achieve a lower energy dissipation. Since vehicle model plays an important role in trajectory tracking, this paper investigates and compares the performance of two classical vehicle models for trajectory tracking of autonomous vehicles using model predictive control (MPC). Firstly, a two-degree-of-freedom kinematic model and a three-degree-of-freedom yaw dynamic model are established for autonomous vehicles. Meanwhile, in order to carry out tracking control more effectively and smoothly, the tire slip angle has been taken into account by the dynamic model. Then, we design two MPC controllers for trajectory tracking, which are based on the kinematic model and the dynamic model, respectively. The performances of two MPC controllers are evaluated and compared on the Carsim/Matlab joint simulation platform. Experimental results demonstrated that, under low speed working conditions, both two MPC controllers can follow the reference trajectory with high accuracy and stability. However, under high speed working conditions, the tracking error of the kinematic model is too large to be used in the real trajectory tracking problem. On the contrary, the controller based on the dynamic model still performs a good tracking effect. In addition, this study offers a guidance on how to select a suitable vehicle model for autonomous vehicles under different speed working conditions. [ABSTRACT FROM AUTHOR]

When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which was divided into a lane-change trajectory correction strategy, a lane-change switchback strategy and forward active collision avoidance strategy according to collision risk. Based on the collision risk function of the rectangular safety neighborhood, the objective functions were designed according to the specific requirements of different strategies. The vehicle lateral controller based on MPC and the vehicle longitudinal motion controller were established. The longitudinal velocity was taken as the joint point to establish the lateral and longitudinal integrated controller. The trajectory planning module, trajectory replanning module and trajectory tracking module were integrated in layers, and the three trajectory replanning strategies of lane-change trajectory correction, lane-change switchback and forward active collision avoidance were respectively simulated and verified. The simulation results showed the trajectory replanning strategy achieves collision avoidance under different scenarios and ensures the vehicle's driving stability. The trajectory tracking layer achieves accurate tracking of the conventional lane-change trajectory and has good driving stability and comfort. [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.

To study the method of trajectory tracking for robotic arms, the traditional tracking method has low accuracy and cannot realize the complex tracking tasks. Compared with traditional methods, deep reinforcement learning is an effective scheme with the advantages of robustness and solving complex problems. This study aims to improve the tracking efficiency of robotic arms based on deep reinforcement learning. Thereby, we propose an approach to improve the proximal policy optimization (Improved-PPO) in this paper, which can be applied to multiple degrees of freedom robotic arms for trajectory tracking. In this study, proximal policy optimization (PPO) and model predictive control (MPC) are integrated to provide an effective algorithm for robotic arm applications. MPC is employed for trajectory prediction to design the controller. Further, the Improved-PPO algorithm is employed for trajectory tracking. The Improved-PPO algorithm is further compared with the asynchronous advantage actor-critic (A3C) and PPO algorithms. The simulation results show that the convergence speed of the Improved-PPO algorithm is increased by 84.3% and 15.4% compared with the A3C and PPO algorithms. This method provides a new research concept for robotic arm trajectory tracking. [ABSTRACT FROM AUTHOR]

To solve the problems of narrow driving condition range, partial evaluation method and insufficient road adaptive control of trajectory tracking, the trajectory tracking accuracy and driving safety under full speed and full road adhesion coefficient were investigated. In the road adaptive model predictive control (MPC), the discrete linear time-varying predictive model was obtained by the three- degree-of-freedom nonlinear dynamic model of the vehicle. The road adaptive speed range matching was realized according to the road adhesion coefficient detected by sensors, and the constraint condition of the speed increment was formulated. The maximum and standard deviation of path tracking error, lateral acceleration, centroid sideslip angle and front wheel sideslip angle with the change of velocity and road adhesion coefficient were obtained by integrating CarSim and Matlab soft wares. Combined with the mechanism of tire sideslip, the driving safety was evaluated, and the trajectory tracking stability/ instability area of vehicle under all working conditions was divided. The results show that the vehicle speed can be adaptively adjusted according to the road information to achieve excellent trajectory tracking accuracy and driving safety under all working conditions. [ABSTRACT FROM AUTHOR]

Model mismatch is inevitable in robot control due to the presence of unknown dynamics and unknown perturbations. Traditional model predictive control algorithms are usually based on constant value assumptions and are not able to overcome the degradation of controller performance due to model mismatch. In this paper, a model predictive control (MPC) algorithm based on Gaussian process regression (GPR) is proposed. Firstly, the kinematic equations of the mobile robot are established by the mechanistic analysis method; similarly, the dynamics of the mobile robot system are modeled using the second-class Lagrangian equations. Secondly, the problem of stability and reliability degradation due to model mismatch during the operation of mobile robot is considered. This paper uses a MPC algorithm with a main model plus residual model to solve the MPC closed-loop control strategy. The state at each moment is decomposed into a predicted state based on the first-principles model and a residual state. The residual state is learned by GPR in real-time and used to compensate for deviations between the real process model and the predicted model. The proposed method requires fewer data samples, enhancing the technique's practicality. Finally, the simulation results show that the proposed algorithm is more stable and achieves the desired tracking faster. Compared with the MPC algorithm, the arrival time of the system is reduced by 28% and the speed error is controlled within 0.07. [ABSTRACT FROM AUTHOR]

This paper focuses on the trajectory tracking problem of unmanned aerial vehicles (UAVs) under external disturbances, and a trajectory tracking method that combines model predictive control with dynamic neural networks was proposed. Firstly, the trajectory tracking problem is transformed into a constrained quadratic programming problem using the idea of model predictive control. Then, the kinematic constraints are taken into account, and control increment constraints and relaxation factors are designed in the objective function. A dynamic neural network is introduced to solve this quadratic programming problem in real-time. In addition, a disturbance compensation observer is designed to overcome external disturbances. Finally, numerical simulations are conducted to verify that the proposed tracking strategy reduces computational

A new method for controlling the position and speed of a small-scale helicopter based on optimal model predictive control is presented in this paper. In the proposed method, the homotopy perturbation technique is used to analytically solve the optimization problem and, as a result, to find the control signal. To assess the proposed method, a small-scale helicopter system is modeled and controlled using the proposed method. The proposed method has been investigated under different conditions and its results have been compared with the conventional predictive control method. The simulation results show that the proposed technique is highly proficient in the face of various uncertainties and disturbances, and can quickly return the helicopter to its path. [ABSTRACT FROM AUTHOR]

This paper discusses the robust trajectory tracking control of an autonomous tractor-trailer in agricultural applications. Firstly, considering the model parameter uncertainties and various disturbances, the kinematic and dynamic models of the autonomous tractor-trailer system are established. Moreover, the coordinate transformation is adopted to convert the trajectory tracking error into a new unconstrained error state space model. On this basis, the prescribed performance control (PPC) technique is designed to ensure the convergence speed and final tracking control accuracy of the tractor-trailer control system. Then, this paper designs a double closed-loop control structure. The posture control level adopts the model predictive control (MPC) method, and the dynamic level adopts the sliding mode control (SMC) method. At the same time, it is worth mentioning that the nonlinear disturbance observer (NDO) is designed to estimate all kinds of system disturbances and compensate for the tracking control system to improve the system's robustness. Finally, the proposed control strategy is validated through comparative simulations, demonstrating its effectiveness in achieving robust trajectory tracking of the autonomous tractor-trailer system. [ABSTRACT FROM AUTHOR]