Addressing the control and maneuverability of surface vehicles with dynamically changing mass distributions is still an open problem. To solve the problem, we propose an intelligent controller for the path-following problem of a surface vehicle, which is controlled through mass distribution. This means that one of the control inputs is mass-centric. Specifically, we developed a Lyapunov-based nonlinear control scheme to enable an unmanned vessel to follow a smooth path according to a line-of-sight guidance law. The control inputs consist of the thrust force for forward motion and the position of a sliding mass that shifts the system's overall mass distribution. Artificial neural networks are employed to estimate unmodeled dynamics and external disturbances. Simulation results demonstrate the effectiveness of the proposed controller in guiding the vessel along the desired path with minimal error.
Unmanned aerial vehicle (UAV) environments present significant challenges for machine learning (ML) due to limited platform resources, heterogeneous sensor data, dynamic mission conditions, and safety-critical requirements. This paper examines these constraints across the core fu…
Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstrations of automated drifting have been limited to research platforms with instantaneous torque delivery and independently actuat…
This paper presents an intelligent control framework for trajectory tracking of robotic manipulators using radial basis function (RBF) neural networks for online disturbance estimation. The proposed control structure combines model-based nonlinear control with an adaptive neural…
This paper proposes a novel unified control framework for achieving hybrid grid-forming (GFM) and grid-following (GFL) inverter operation by integrating dispatchable virtual oscillator control with reference-following synchronization. The proposed inverter control method supports…
Sampling-based model predictive control methods handle nonlinear dynamics and complex cost landscapes through Monte Carlo rollouts, yet typically employ fixed constraint penalties that do not adapt to model-plant mismatch. This paper proposes Residual-Conservative Model Predictiv…
Model Predictive Control (MPC) has been widely applied to autonomous vehicle drifting. Assisted drifting, that is where the driver remains in the loop, is still comparatively underexplored. Existing approaches often rely on restrictive assumptions, such as precomputed drift equil…