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This paper presents an adaptive neural fault-tolerant control algorithm for the path-following activity of the underactuated surface vehicle (USV) using the novel output-based triggering approach. In the algorithm, the event-triggered mechanism is designed utilizing the attitude states from the kinematics aspect of USV. Both the control inputs and the related calculation thread are implemented only at the trigger instants. Furthermore, neural networks (NNs) are employed to remodel the model uncertainties, and the adaptive observer is developed to estimate and compensate for the effect of the unknown actuator faults. With the direct Lyapunov theorem, the semi-global uniformly ultimately bounded (SGUUB) stability can be guaranteed for the closed-loop system in aspects of both the trigger instant and the continuous interval. The comparison experiment has been illustrated to verify the effectiveness of the proposed algorithm, which can effectively improve the information transmission performance and the fault-tolerant capability. [ABSTRACT FROM AUTHOR]

Maneuvering a truck-trailer system while docking is extremely challenging. This article aims to alleviate this problem by presenting an enhanced path-following control framework for autonomous semi-trailer docking. In the proposed system, adaptive controllers that utilize gain scheduling are introduced for forward and reverse path-following tasks in docking maneuvers to increase the robustness and path-following performance. The system includes an improved pure pursuit controller with adaptive look-ahead distance for forward path following; a cascade controller of reverse pure pursuit and a gain-scheduled LQ control for reverse path-following. In the evaluation of the path-following performance of forward and reverse controllers, the closed-loop system of path-following controllers with the truck-trailer kinematic model is simulated in MATLAB/Simulink for various test cases, and the results are compared with those of other studies. Furthermore, different docking scenarios are generated via the cascade path planning algorithm for autonomous semitrailer docking. These are tested with a high degree semi-trailer model within the IPG TruckMaker simulation environment, and with a full truck-trailer vehicle in the test field. The results of both simulations and physical testing clearly demonstrate improvements in terms of the control problem formulation, i.e., the stabilized path-following is obtained with acceptable path-following errors. [ABSTRACT FROM AUTHOR]

This paper focuses on the controller design for path-tracking problem of autonomous ground vehicles (AGVs) by employing a multi-constraint nonlinear predictive control (NMPC) schema. It is aimed to improve the transient performance of the vehicle and to consider a rollover prevention criterion in the proposed method. The path-tracking problem is transformed into the yaw stabilization issue, and a feedback control law with input saturation is developed to decrease the steady-state errors. Furthermore, the yaw-rate signal is generated for the desired path-tracking performance. The major contributions of the present paper are, first, developing a neural network autoregressive with exogenous input system to assist in obtaining an accurate and explicit model in order to contribute to the control of the system over the prediction horizon; second, describing a Frenet–Serret differential geometry based path-following agenda and developing AGV dynamic model by incorporating the vehicle vertical mode of motion to prevent vehicle rollover during critical maneuvers, and finally, achieving an enhanced yaw stabilization and transient tracking performance considering saturated input signal by employing the proposed system identification algorithm. The effectiveness of the proposed control system is verified by comparing with the traditional NMPC method by employing a high fidelity CarSim/MATLAB framework. [ABSTRACT FROM AUTHOR]