Search

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]

Aiming at the unmanned surface vehicle (USV) trajectory tracking control under complex time-varying environment, a predefined-time convergence sliding mode disturbance observer (PTC-SMO) is introduced to effectively handle the internal parameter uncertainties and external environmental disturbances, thereby guaranteeing precise compensation of the lumped disturbance term within a set time. Then, in order to achieve precise tracking of the desired trajectory using USV under a predetermined time constraint, a novel fast trajectory tracking control strategy with predefined-time convergence (PTC-FTTCS) is established to improve tracking performance and ensure that the trajectory tracking error converges quickly in the predefined time. Through rigorous comparative simulation under ideal conditions and time-varying disturbances, the results demonstrate reliable trajectory tracking and disturbance handling effects, and the tracking performance and disturbance observation performance are significantly better than state-of-the-art methods. [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]

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]

The trajectory tracking issue of unmanned vehicles has attracted much attention recently, with the rapid development and implementation of sensing, communication, and computing technologies. This paper proposes a nonlinear model predictive controller (NMPC) for the trajectory tracking application of an unmanned vehicle (UV). First, a two-degree-of-freedom (2-DOF) kinematics model of this UV is used to derive the desirable controller with two control variables as forward velocity and yaw angle. Next, the one-step Euler method is employed to establish the nonlinear prediction model, then a nonlinear optimization objective function is formulated to minimize the tracking errors of forward velocity and yaw angle from a preset time-varying reference road. Finally, the effectiveness of the proposed NMPC scheme is assessed under two different driving scenarios via MATLAB simulations. The simulation results confirm that the proposed NMPC scheme reveals better control accuracy and computational efficiency than the standard MPC controller under two different prescribed roads. Moreover, an outdoor field test is conducted to verify the performance of the proposed NMPC scheme, and the results show that the proposed NMPC can be applied to the real vehicle and can improve the tracking accuracy and the driving stability of trajectory tracking. [ABSTRACT FROM AUTHOR]

Longitudinal motion controllers based on over- simplified models result in steady-state errors, oscillations, and overshoots of the velocity, all of which impair the unmanned ground vehicle (UGV) multiple objectives (trajectory tracking capability, energy economy, and ride comfort). While it is challenging for complicated methods to accomplish real-time control in the vehicle-mounted electronic control unit (ECU), which meets harsh working conditions but has limited computing power. This paper proposes a multi-objective model predictive longitudinal motion control approach for an ECU based on a trajectory tracking model. For the trajectory tracking model, we establish an internal powertrain/brake dynamics model and divide the external resistance into three components. They are identified using a variety of practical and straightforward methods. The control strategy is composed of a high-level multi-objective model predictive controller (MPC) that obtains the optimal internal acceleration and a low-level controller that calculates control inputs based on the powertrain/brake model. To implement real-time control on the ECU, the MPC optimization problem and its solver that combines offline and online calculation are elaborately designed. Finally, the method is validated through simulations in three typical driving scenarios and vehicle tests on a hybrid-electric sport utility vehicle (SUV) and a 6-speed dual- clutch transmission (DCT) fuel sedan. Simulations and experiments demonstrate that the proposed approach is superior to the other four commonly used methodologies in terms of achieving multiple objectives. [ABSTRACT FROM AUTHOR]

A robust trajectory tracking control scheme for quadrotor unmanned aircraft vehicles under uncertainties is proposed herein. A tracking controller combined with the sliding mode and integral backstepping is performed for position and attitude tracking. The stability of the trajectory tracking controller of the quadrotor is investigated via Lyapunov stability analysis. By incorporating force and torque disturbances into numerical simulations, the results demonstrate the effectiveness of the proposed quadrotor trajectory controller. Finally, the experiments validate the feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

Trajectory tracking is a basis of motion control for Unmanned Surface Vehicles (USVs), which has been researched well for common USVs. The twin-propeller and twin-hull USV (TPTH-USV) is a special vehicle for applications due to its good stability and high load. We propose a three-layered architecture of trajectory tracking for the TPTH-USV which explicitly decomposes into trajectory guidance, a motion limitator and controller. The trajectory guidance transforms an expected trajectory into an expected speed and expected course in a kinematic layer. The motion limitator describes some restriction for motion features of the USV in the restriction layer, such as the maximum speed and maximum yaw rate. The controller is to control the speed and course of the USV in the kinetic layer. In the first layer, an adaptive line-of-sight guidance law is designed by regulating the speed and course to track a curved line considering the sideslip angle. In the second layer, the motion features are extracted from an identified speed and course coupled model. In the last layer, the course and speed controller are designed based on a twin-PID controller. The feasibility and practicability of the proposed trajectory tracking scheme is validated in sea experiments by a USV called 'Jiuhang 490'. [ABSTRACT FROM AUTHOR]