Dynamical System-Based Imitation Learning and Neuroadaptive Control for Trajectory Recovery in Autonomous Ships

Fixed-Time Stable Fault-Tolerant Control of Underactuated Hovercraft via Physics-Informed Neural Adaptation

This study addresses the trajectory tracking control problem for an underactuated hovercraft subject to additive bias and multiplicative loss-of-effectiveness thruster faults under environmental disturbances. In these systems, actuator degradation structurally breaks the differential flatness mapping, driving nominal controllers to generate control actions that induce severe actuator saturation and cause instability. To resolve this challenge, a hierarchical physics-informed neural adaptive control (PINAC) framework is proposed. First, a gated-recurrent-unit physics-informed neural observer (PINO) is designed to isolate thruster faults from exogenous hydrodynamic disturbances. Second, a constrained Safe-TD3 reinforcement learning agent functions as a supervisor, computing an online dilation factor to slow down the mission timeline, thereby reconfiguring the reference trajectory to accommodate degraded actuator boundaries. Third, a low-level non-singular terminal sliding mode (NTSM) controller is implemented as a tracking-guarantee layer. Unlike classical asymptotic schemes where convergence is only achieved as time approaches infinity, or finite-time controllers where the settling time depends on the initial state, the proposed PINAC framework guarantees practical fixed-time stability, ensuring that the settling-time bound is independent of initial conditions. Simulation results demonstrate that the designed controller prevents actuator saturation, provides smooth trajectory adjustment, and reduces tracking errors under severe composite faults.

Underactuated USV Fixed-Time Trajectory Tracking Using Neural Network and Sliding Mode Control

In recent years, underactuated unmanned surface vehicles have attracted considerable research interest. These vehicles exhibit underactuated characteristics, meaning the independent control inputs are outnumbered by the degrees of freedom. This characteristic poses significant challenges to controller design. Furthermore, unmodeled dynamics within the system and external ocean disturbances further complicate the controller design process. This paper proposes a fixed-time control scheme for underactuated unmanned surface vehicles. First, the trajectory tracking error is reformulated by using center-of-gravity shift technique (CGST). Second, to handle unknown marine disturbances, a radial basis function neural network (RBFNN) is established, complemented by the the minimum learning parameter (MLP) method to lower the controller’s computational complexity. Third, a fixed-time sliding mode control (FTSMC) strategy is developed to achieve fixed-time convergence of the tracking error to a bounded set. Finally, simulations demonstrate the performance of the overall approach.

Neural Backstepping Control for Trajectory Tracking of Wheeled Mobile Robots

This paper presents a Neural Backstepping control strategy for trajectory tracking of a differential-drive mobile robot. The proposed approach combines a dynamic-level backstepping controller with a lightweight single-hidden-layer adaptive neural network to compensate uncertain nonlinear dynamics through online adaptation. The backstepping component provides a Lyapunov-based stabilizing structure, whereas the neural approximator improves tracking performance without requiring deep architectures, offline training stages, or computationally demanding optimization procedures. The adaptive law for the neural output weights is derived from the stability analysis, ensuring bounded closed-loop signals and uniformly ultimately bounded tracking errors in the presence of bounded approximation uncertainties. The controller is evaluated through simulations using four reference trajectories: circular, lemniscate, Lissajous, and waypoint-based paths. The same control gains and neural network configuration are used in all cases, showing that the proposed scheme can track different trajectory geometries without trajectory-specific retuning. The simulation results show satisfactory tracking performance, with position RMSE values below 0.04 m for all evaluated trajectories. These results indicate that the proposed Neural Backstepping controller provides a suitable balance between tracking accuracy, online adaptation capability, and implementation simplicity for differential-drive mobile robot trajectory tracking.

On Optimal Event-Triggered Distributed Control for Stochastic Multi-Agent Systems via Reinforcement Learning

This work proposes a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties that uses the actor-critic-identifier structure and provides a Lyapunov-based stability proof that guarantees all errors are bounded, ensuring precise tracking between the leader and followers.

Underactuated AUV course stability control based on optimal deep reinforcement learning with external disturbances and input constraints

Reinforcement Learning for Real-Time Auto-Tuning of Robot’s Controllers

This research introduces an innovative control technique for Series Elastic Actuators (SEAs) that utilizes Reinforcement Learning (RL) to address the shortcomings of previously fixed-gain adaptive controllers, which are a hybrid of State Feedback Control (SFC) and Model Reference Adaptive Control (MRAC) by using Lyapunov Stability Analysis. This controller is optimized by adjusting the adaptation factor. b. This study presents an intelligent agent based on reinforcement learning to find the value of b with a dynamic auto-tuner. It trains via the Soft Actor-Critic (SAC) algorithm for 100,000 time steps. A comparison between the two methods was presented according to simulation results under different conditions; the RL-based controller shows much better tracking accuracy, how quickly it reaches the target output, and how little control torque it uses, where the agent's policy could automatically adjust in real-time based on system conditions, such as uncertainties and disturbances, where it has a settling time of 1.7 seconds, while the fixed parameter controller has a 1.95-second settling time, resulting in a reduction of 15.3%. It also lowers the control torque caused by disturbances by 19.5% compared to the fixed parameter controller, which has a control torque of 3.99 Nm, while the maximum control torque for the RL-optimized controller is 3.21 Nm.