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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]

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

• Construction of a BiLSTM-based internal model control architecture for USV trajectory tracking: The paper establishes an architecture that incorporates a BiLSTM neural network as the internal model for trajectory tracking. It demonstrates that a model trained using BiLSTM possesses the necessary conditions for effective control, thereby addressing the nonlinear control problem in USV systems. • Addressing the challenge of online nonlinear optimization in control steps: The paper acknowledges that neural networks require solving online nonlinear optimization problems at each control step, which can be challenging in practical control applications. To overcome this, the proposed method introduces adaptive internal model control based on BiLSTM. By utilizing the computationally efficient internal model control approach, the paper effectively resolves this issue, enabling the deployment of the neural network in engineering applications. • Implementation using Raspberry Pi 3 and TensorFlow Lite, The trained model is applied in control by leveraging Raspberry Pi 3, and TensorFlow Lite. This implementation showcases the practical applicability of the proposed approach in real-world control scenarios. This paper investigates a practical adaptive internal model control (IMC) for an unmanned surface vehicle (USV) with unknown time-varying nonlinear model parameters and environmental disturbances. Firstly, an internal model of the USV is presented using the Bidirectional Long Short-Term Memory (BiLSTM) neural network. Then, an adaptive neural IMC controller is designed for the trajectory tracking of USV by using the IMC method. The internal model and the controller are updated by an error threshold algorithm. The proposed control scheme comprises of a trajectory guidance module via the Line-of-Sight (LOS) guidance method and a tracking control module designed by IMC theory. Under the proposed control scheme, the development processes of the vehicle platform and the control algorithms are described, and accurate tracking control can be achieved. Finally, the results of simulation and field experiments are presented and discussed to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]