Human motion prediction based on the biological signal has been improved because of the improvement of an artificial intelligent (AI) technology. Conventional methods to predict a human motion have been based on a machine learning such as a neural network, a regression model and so on. From the above, the model by them cannot generate the model whose calculation process is not clear. Then, it is impossible to interpret the relationship between input and output information. In this paper, the element description method (EDM) was applied to generate the model and the prediction of human motion and the analysis about muscle characteristics had been done. An EDM is one of the system identification methods and it is possible to interpret the relationship between input and output because it can generate a block diagram of the model. Especially, the fingertip force and the surface-electromyography (sEMG) of the muscles which move the joint were focused in this paper Moreover, the fingertip force was estimated from the SEMG and it was also done to analyze the muscle characteristics based on the model by an EDM.
The accurate knowledge of the synchronous reluctance machine (SyR) model is essential for advanced control implementation and high-fidelity simulation. The accuracy of experimental constant-speed identification methods is mainly influenced by inverter nonlinearities; however, factors such as the digital control effect, the current harmonics effect of the machine under the test, and the speed harmonics effect of the prime mover should be considered. Therefore, this paper investigates the influence of the aforementioned effects on the accuracy of model identification for SyR motor drives.
Batteries are widely applied in industrial systems, promoting industrial production efficiency. The system prognosis needs to establish a battery health monitoring model and maintain regular maintenance, improving reliability and safety while reducing unnecessary economic losses. An accurate data-driven state-of-health (SOH) estimation method is crucial, as it mainly relies on the information related to battery SOH values in the extracted features. Therefore, we propose a statistical-based feature extraction method, which makes it physically meaningful and effective. To explore more information, we propose a feature analysis framework to analyze battery features, including visualization, correlation, and importance analysis. We utilize a random forest regression model as an effective learner and validate it based on NASA’s real-world dataset. The feature analysis contents are beneficial for understanding the role and importance of features in SOH estimation tasks, which can also provide an extended scenario for feature extraction of batteries.
Realizing both dexterity in a large workspace and mechanically efficient architecture is a challenge for robotic manipulators. Conventional industrial robots, including serial and parallel robots, have suffered from the trade-off between the weight and the size of the workspace. On the other hand, cable-driven parallel robots (CDPRs) perform excellent lightness of the platform in a large workspace. However, its workspace is surrounded by a box, which leads to a large occupied space. This paper presents a hybrid cable-driven parallel robot with an articulated manipulator. The end effector is a platform whose orientation is directly controlled by four cables. The proposed architecture realizes 6-DOF motion by a light body. Since the tendons do not go through the intermediate joints of the articulated manipulator, the mechanical efficiency of the cables is improved compared to conventional tendon-driven robots. The forward kinematics is analyzed, and the numerical solution using the Newton-Raphson method is derived. The position control method, considering the statics of the platform, is explained. The experiments of position control confirm the performance of both translational and orientational motion.
Recent research on quadruped robots has been achieving high-performance motion control based on optimization and reinforcement learning. However, there is still ongoing research aimed at demonstrating high-performance motion based on simple and dominant dynamic principles. In this paper, we proposed a novel control approach that projects Spring-Loaded Inverted Pendulum (SLIP) dynamics to articulated legs, utilizing admittance control based force observer within a rotating workspace (RWFOB). Unlike other legged robots that depend on sensor-based estimation of external forces, the proposed method presents an alternative approach that reduces the reliance on sensors. Additionally, we introduce a comprehensive control framework for quadruped robot motion control, establishing the connection between trunk and SLIP-realized leg movements using Jacobian. The effectiveness of the proposed framework as a robust and reliable trunk feedback controller is validated through simulation and experiments.
