During motor operation, the motor parameters change, which causes parameter drift. They are also affected by internal and external unknown disturbances, which lead to reduced motor control performance, poor anti-interference performance, and low robustness. A method termed ultra-local model-free predictive current control (MFPCC) has previously been proposed to solve this problem; it uses only the input and output of the system and does not involve any motor parameters, because of which it is free of problems caused by model mismatch. However, the conventional MFPCC method requires adjustment of several control parameters and the estimated value of the total disturbance of the system has a certain deviation and a large pulsation, which result in obvious chattering of the motor output, low stability, reduced anti-interference performance, and low robustness. Therefore, this paper proposes an MFPCC method based on nonlinear disturbance compensation (NDC). This method does not involve any motor parameters, and it can more accurately and stably estimate the total system disturbance, and feedforward compensation, real-time update control information, only need to adjust two control parameters, the workload is small. Simulation results show that the proposed control method has high anti-interference performance, high robustness, small output ripple, and improved dynamic characteristics and that it can estimate the system disturbance accurately and stably.
At present, the neglect of deployment friction could lead to a significant deviation in the numerical results from the real deployment dynamic characteristics of the nonconductive space tether. To solve this problem, a dedicated experimental setup for tether deployment was firstly employed to measure the deployment friction of nonconductive space tether, the dependences of the deployment friction on the tether diameter, deployment velocity and deployment angle (which is the angle between the deployment direction and the normal direction of the tether exit) were determined. The effect of deployment parameters on the dynamics in both the uncontrolled tether deployment and the station-keeping stages were evaluated based on the "dumbbell" and "extended dumbbell" models, the measured deployment frictions was applied to the models. The numerical results show that the uncontrolled nonconductive tether deployment is great limited by the deployment friction; the tether deployment capability and dynamic stability could be enhanced by increasing the initial deployment velocity (<= 2 m/s) and satellite effective mass, as well as decreasing the orbital altitude. To guarantee maximum tether deployment capability and dynamic stability, an optimal matching relationship between the tether full length and satellite total mass is given, which is of substantial importance for the uncontrolled deployment process of the nonconductive space tether system at the design stage.
A sliding mode control (SMC) strategy based on Byrnes-isidori (B-I) normal form is proposed to solve the tracking control problem of non-minimum phase (NMP) hypersonic flight vehicle (HFV) under external disturbance (ED). A nonlinear disturbance observer (NDO) is used for ED, the estimation error is guaranteed to be bounded stable. A dynamic sliding mode controller is constructed to stabilize internal dynamics, and a dynamic inverse controller is designed to stabilize the external dynamics. Aiming at stabilizing the velocity subsystem, a sliding mode controller is designed. Asymptotic stability of internal dynamics, practical finite time stability of velocity tracking error and bounded stability of external dynamic are proved by Lyapunov theory. Simulation is used to verify the effectiveness of the control strategy.
In the domain of concrete penetration, test data are often limited in quantity and unevenly distributed, which leads to poor accuracy of the machine learning-based model for predicting the depth of concrete penetration. This paper aims to improve the accuracy of the model within the constraints of limited penetration test data. In this paper, based on collecting a large amount of penetration test data, the penetration data was extended by data augmentation methods such as linear interpolation and adding Gaussian noise. The genetic algorithm and greedy algorithm were used to optimize four common machine learning models’ hyperparameters: multilayer perceptron (MLP), radial basis neural network (RBF), support vector regression (SVR), and extreme gradient boosting tree (XGBoost). The results show that using linear interpolation and adding Gaussian noise can effectively alleviate the problems of insufficient data and uneven data distribution. The average error of MLP, RBF, and XGBoost decreases by 2.7
In recent years, high-strength steel bars (HSSB), such as hot-rolled ribbed bars (HRB) and heat-treated ribbed bars (HTRB), have been promoted and used in reinforced concrete (RC) structures worldwide. There is still some controversy in existing research regarding the crack development process, failure modes, and the applicability of current code-specified calculation formulas for high-strength RC beams. The study of the mechanical properties of high-strength RC beams is of great significance in promoting the development of construction engineering science. In order to investigate the flexural behavior of high-strength RC beams, four-point bending static loading tests were carried out on 11 groups of RC beams with different parameters (reinforcement strength, concrete strength, and reinforcement ratio). The test results indicate that the crack development process and failure mode of high-strength RC beams exhibit obvious bending failure characteristics. Increasing reinforcement strength and reinforcement ratio significantly improves the bearing capacity of the beams, while the impact of increasing concrete strength on the beam's capacity is relatively small. The applicability of the calculation formulas in the current codes was analyzed, it was found that the yield moment formula in the current code accurately predicts the yield moment of high-strength RC beams, and the formula for calculating the maximum crack width was modified by proposing the maximum crack width adjustment factor k omega. Subsequently, the failure process of highstrength RC beams was simulated using explicit dynamic finite element software LS-DYNA. Parameterized analyses were conducted to broaden the research scope. The ultimate moment prediction factor ku was proposed and a formula that can accurately predict the ultimate moment of high-strength RC beams was established.
