With the rapid development of autonomous vehicles, heavy-haul freight trains (HhFT), with their capability of transporting large volumes of cargo over long distances, are emerging as promising subjects for research in automatic control. One of the main challenges in operating HhFT lies in ensuring accurate trajectory tracking performance under the influence of adverse factors such as external disturbances, model uncertainties, and actuator-related issues, including actuator faults and input time delays. If not effectively addressed, these factors can significantly degrade the control performance of the system. This paper presents a robust trajectory tracking control system with prescribed performance for HhFTs using multiple electric locomotives. In this framework, a prescribed performance function (PPF) is designed to ensure that the position tracking errors of the locomotives remain within a predefined bound. Simultaneously, an extended state observer (ESO) is employed to estimate the states and the lumped disturbances representing the adverse effects. Based on the outputs of the PPF and the ESO, a robust sliding mode controller (RSMC) is designed. The stability of the closed-loop system is proven through Lyapunov stability theory, showing that all system states converge to a neighborhood of the origin in finite time. Computer simulation results clearly demonstrate the superior control performance of the proposed system compared to previously introduced methods.
The oscillatory motion of floating offshore wind turbines (FOWTs) under erratic sea conditions negatively impacts power generation efficiency, increases structural fatigue loading, and reduces system longevity. While individual blade pitch control can be used to regulate rotor speed and stabilize platform dynamics, it imposes significant mechanical loads on the pitch actuators. To alleviate this burden, mooring line actuation (MLA) offers a promising complementary strategy. This paper investigates the potential and challenges of mooring line actuation for dynamic stabilization of FOWTs. Two platform configurations-a spar-buoy and a tension-leg platform (TLP)-are modeled and validated. For TLP, a tuned mass damper (TMD) strategy is examined as a benchmark for comparative stabilization performance. A comprehensive controllability investigation is conducted to evaluate the effectiveness of MLA in influencing platform degrees-of-freedom (DOFs). Based on these insights, a linear quadratic regulator (LQR) controller is designed to modulate mooring line lengths and associated tensions for active stabilization. Numerical simulations reveal that MLA provides significantly greater stabilization benefits for the TLP configuration compared to the spar-buoy. This underscores the importance of integrated control co-design (CCD) to improve MLA performance, especially for platforms with lower inherent controllability. Across a range of operational scenarios, the proposed MLA strategy demonstrates effective simultaneous surge and pitch suppression with minimal mooring line actuation, offering a viable path toward load-reducing, performance-enhancing control architectures in next-generation FOWTs.
This paper introduces a data-driven sensorimotor control framework for a flapping-wing unmanned aerial vehicle (FWUAV). It integrates an imitation learning algorithm for optimal controls with a deep neural pose estimation scheme. Recognizing that a direct concatenation of the neural pose estimator with the learning-based controller fails, we propose an alternating learning algorithm, namely, ALICE, for the coordinated integration of the two learning schemes. In particular, we enhance the learning capability of the estimator and the controller such that they converge to a synergistic pair. The proposed framework demonstrates excellent stabilizing capabilities compared to alternative ablated strategies or even an end-to-end controller. Furthermore, the presented technique overcomes the common restrictions of existing methods for FWUAV control, particularly the requirement for high-frequency flapping to justify linearization over averaged dynamics.
This work presents a novel geometry for a gerotor hydraulic pump integrated into the rotor of a brushless, direct-current, frameless motor. The design eliminates excess dynamic seals and produces a pump that is more compact, more efficient, higher bandwidth, and capable of greater pressure and flow performance than any servopump previously presented at this scale. Two pumps are presented, with one capable of achieving pressures in excess of 2.9 MPa, flows up to 1.6 L/min, output power levels of 31.5 W with 25% efficiency, and operation at bandwidths up to 37 Hz in a compact form weighing 265 g. Due to its compact size and high output, the proposed design achieved a peak power density of 119 W/kg. Variations of the second produced pressures up to 2500 kPa, flows over 4.5 L/min, hydraulic power of 25 W, and weighed 410 g. The proposed design was quiet in operation, with a maximum sound pressure level at 1 m of 55.8 dBA. Because the proposed design contains no external moving parts, a simple fabric wrap reduced pump noise to a maximum of 47.7 dBA. The pump was evaluated for positional-feedback control of representative systems via integration into two setups: one driving standard rigid hydraulic pistons and one driving a soft robotic actuator. The pump was able to control these setups with positional accuracy on the order of 1 mm and angular accuracy of 2 deg, respectively.
Abstract In this paper, we present a novel formulation involving a coupled ODE–PDE system with memory effects. The system dynamics are modeled using PDEs for heat distribution and ODEs for discrete system states, with an emphasis on incorporating memory terms that influence system behavior over time. A state feedback control law is designed to stabilize the system using the backstepping approach globally. The well-posedness of the closed-loop system is established through rigorous mathematical proofs, ensuring the existence and uniqueness of solutions. Exponential stability is demonstrated using a new Lyapunov function and an analysis of energy decay rates. The article concludes with a numerical example that validates the theoretical findings, showcasing the convergence of system states to equilibrium and confirming the effectiveness of the proposed control strategy.
