Reinforcement Learning-Based Model-Free Controller for Feedback Stabilization of Robotic Systems

Two-Time Scale Tracking Control of Flexible Robots With Primal-Dual Inverse Reinforcement Learning

Flexible robots (FRs) are generally designed to be lightweight to achieve rapid motion. However, accompanying vibrations and modeling errors influence tracking control, especially in situations involving reference signal loss. This article develops a two-time scale primal-dual inverse reinforcement learning (PD-IRL) framework for FRs to perform tracking tasks with incomplete reference signals. First, consider the admissible policy as a nonconvex input constraint to guarantee the stable operation of the equipment. Then, FRs imitate the demonstration behaviors of an expert, including both rigid and flexible motions, to achieve a balance in tracking speed and vibration suppression. During the imitation process, nonconvex optimization problems of FRs are transformed into corresponding dual problems to obtain the global optimal policy. Moreover, employing multiple linearly independent paths to explore the state space simultaneously can improve convergence speed. Convergence and stability are studied rigorously. Finally, simulations and comparisons show the effectiveness and superiority of the proposed method.

The application of machine learning in 3D/4D printed stimuli-responsive hydrogels

The integration of machine learning (ML) in materials fabrication has seen significant advancements in recent scientific innovations, particularly in the realm of 3D/4D printing. ML algorithms are crucial in optimizing the selection, design, functionalization, and high-throughput manufacturing of materials. Meanwhile, 3D/4D printing with responsive material components has increased the vast design flexibility for printed hydrogel composite materials with stimuli responsiveness. This review focuses on the significant developments in using ML in 3D/4D printing to create hydrogel composites that respond to stimuli. It discusses the molecular designs, theoretical calculations, and simulations underpinning these materials and explores the prospects of such technologies and materials. This innovative technological advancement will offer new design and fabrication opportunities in biosensors, mechatronics, flexible electronics, wearable devices, and intelligent biomedical devices. It also provides advantages such as rapid prototyping, cost-effectiveness, and minimal material wastage.