Nature provides much inspiration for developing soft millirobots. However, compared with smart and adaptations of lives in nature, these robotic systems still suffer from insufficiency of intelligence. Here, a new untethered soft millirobot with magnetic actuation in the head and function in the tail is presented via implementing control, actuation, and sensing directly in the materials, thereby endowing robots with multimodal locomotion and environment‐adaptive functions. Due to the soft and asymmetric structure, the millirobot not only shows robust multimodal locomotion, including controllable and transformable crawling, swinging and rolling, but also achieves an excellent capability of helical propulsion in water. Moreover, the robot also possesses outstanding obstacle‐crossing abilities, including helically propelling over obstacles (>2 body length), crawling within a 2 mm height tunnel and swinging through a 450 µm width channel. Furthermore, the robot can even squeeze its body to crawl through a tube easily via near‐infrared irradiation, which triggers the osmotic shrinking of its body. Notably, the robots also possess extraordinary environment‐adaptive functions, for example, leptocephali‐like optical camouflage in water, octopus‐like controllable delivery and variable appearance via visible color–shifting for interaction with the changing environment. These smart robotic systems would be of benefit in various fields via seamless integration of bioinspired design and smart materials.
In article number 1909202, Xuemin Du, Xinyu Wu, and co‐workers develop an octopus‐inspired untethered soft millirobot with magnetic actuation solely in the head and multiple functions in the tail. Due to the seamless integration of its bio‐inspired design and smart materials, the millirobot not only shows robust multimodal locomotion, but also possesses extraordinary environment‐adaptive functions in water, for example, an octopus‐like squeezable body, optical camouflage, and variable appearance via visible color‐shifting for interaction with the changing environment.
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.
The Ce17Fe78-xB6Gax (x=0-1.0) ribbons were fabricated by a melt-spinning technique in order to study the mechanism of the valence variation of Ce and their magnetic properties as well as improve the thermal stability of Ce-based rare earth permanent magnets. The systematic investigations of the Ce17Fe78-xB6Gax (x=0-1.0) alloys show that the room-temperature coercivity increases significantly from 352 kA/m at x = 0 to 492 kA/m at x = 1.0. The Curie temperature (Tc) increases monotonically from 424.5 K to 433.6 K, and the temperature coefficients of remanence (α) and coercivity (β) of the ribbons are better off from -0.56 %/K, -0.75 %/K for x = 0 to -0.45 %/K, -0.65 %/K for x = 0.75 in the temperature range of 300–400 K, respectively. The Ce L3-edge X-ray absorption near edge structure (XANES) spectrums reveal that there is more Ce4+ in ribbons under total electron yield than fluorescence yield as Ce has a high affinity with oxygen. The weight of Ce3+ increases while the weight of Ce4+ decreases in Ga-added alloys. The refined grain size and a more uniform microstructure are mainly attributed to the improved magnetic properties and thermal stability with Ga doping. This paper may serve as a reference for further developing the so-called gap magnets and the effective utilization of the rare earth resources.
State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H ∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studies in simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.
This paper considers the stabilization problem of uncertain dynamic nonholonomic systems. New robust and adaptive robust control laws are presented with an aim to stabilize the system to the origin with a simple design procedure and no extensive online computations. The designed controllers have been implemented in a nonholonomic wheeled mobile robot, and the application are discussed. Simulation study demonstrates the e ectiveness of the proposed method.
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