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The logistics industry has made significant progress in recent years. However, there are still issues with low operational efficiency and high costs. Unmanned logistics vehicles have gained attention as an efficient and intelligent mode of transportation with the rapid development of the industry. The study utilizes an advanced path tracking control algorithm, in combination with model predictive control technology, to monitor and adjust the path, speed, and direction of unmanned logistics vehicles in real-time. The aim is to enhance the stability, safety, and efficiency of travel. The experiments revealed that the average accuracy of path deviation prediction of the proposed model on two different datasets is 88.33% and 82.1%, which is 3.96% and 4.72% higher than that of the control model, respectively. The control accuracy of the proposed model reached 94.19% on the KITTI Vision Benchmark Suite dataset and 95.61% on the CARLA Simulator dataset, which are both higher than the other control models. In addition, the study also tested the proposed model for energy consumption, controller switching frequency, lateral error and other indexes, and the findings revealed that the proposed model of the study exhibits high stability and efficiency. This research not only provides new ideas for the control of unmanned logistics vehicles, but also verifies the effectiveness of the control strategy through experiments.

The transmission of satellite payload data is critical for services provided by aerospace ground networks. To ensure the correctness of data transmission, the TCP data transmission protocol has been used typically. However, the standard TCP congestion control algorithm is incompatible with networks with a long time delay and a large bandwidth, resulting in low throughput and resource waste. This article compares recent studies on TCP-based acceleration algorithms and proposes an acceleration algorithm based on the learning of historical characteristics, such as end-to-end delay and its variation characteristics, the arrival interval of feedback packets (ACK) at the receiving end and its variation characteristics, the degree of data packet reversal and its variation characteristics, delay and jitter caused by the security equipment’s deep data inspection, and random packet loss caused by various factors. The proposed algorithm is evaluated and compared with the TCP congestion control algorithms under both laboratory and ground network conditions. Experimental results indicate that the proposed acceleration algorithm is efficient and can significantly increase throughput. Therefore, it has a promising application prospect in high-speed data transmission in aerospace-ground service networks.

The adaptability of traditional PID, fuzzy PID and feedback linearization sliding mode variable structure control intelligent algorithm to automatic drilling is analyzed. The results show that the feedback linearization sliding mode variable structure control algorithm has more ideal response speed, control accuracy and robustness, which solves the nonlinear problem of the electro-hydraulic control system of the truck-mounted drilling rig and improves the tracking control accuracy of the system during automatic drilling.

In this study, a power control algorithm of a variable-speed fixed-pitch horizontal-axis lift-type 20 kW small wind turbine (SWT) was proposed and verified through dynamic simulations. The power control algorithm proposed in this study consists of algorithms for Region II to track the maximum power coefficient, for Region II-1/2 to maintain the rated rotor speed, and for Region III to maintain the rated power. To verify the proposed power control algorithm, simulations were performed at the rated wind speed and above the rated wind speed, to which turbulence intensity based on the IEC regulation’s normal turbulence model was applied. As a result, it was confirmed that the proposed controller operates properly in the whole three regions including Regions II, II-1/2, and III. The controller performance was then compared with the variable-speed variable-pitch power controller. Although the performance of the proposed controller was considered good for the target VSVP wind turbine, it was lower than that of the conventional controller applied to the same wind turbine. Compared to the VSVP wind turbine, the VSFP wind turbine with the proposed controller was found to have higher mean loads on the blade and the tower but the fatigue loads in terms of Damage Equivalent Load (DEL) were found to be reduced.

Nonlinear vibration control of the twelve-poles electro-magnetic suspension system was tackled in this study, using a novel control strategy. The introduced control algorithm was a combination of three controllers: the proportional-derivative (PD) controller, the integral resonant controller (IRC), and the positive position feedback (PPF) controller. According to the presented control algorithm, the mathematical model of the controlled twelve-poles rotor was established as a nonlinear four-degree-of-freedom dynamical system coupled to two first-order filters. Then, the derived nonlinear dynamical system was analyzed using perturbation analysis to extract the averaging equations of motion. Based on the extracted averaging equations of motion, the efficiency of different control strategies (i.e., PD, PD+IRC, PD+PPF, and PD+IRC+PPF) for mitigating the rotor’s undesired vibrations and improving its catastrophic bifurcation was investigated. The acquired analytical results demonstrated that both the PD and PD+IRC controllers can force the rotor to respond as a linear system; however, the controlled system may exhibit the maximum oscillation amplitude at the perfect resonance condition. In addition, the obtained results demonstrated that the PD+PPF controller can eliminate the rotor nonlinear oscillation at the perfect resonance, but the system may suffer from high oscillation amplitudes when the resonance condition is lost. Moreover, we report that the combined control algorithm (PD+IRC+PPF) has all the advantages of the individual control algorithms (i.e., PD, PD+IRC, PD+PPF), while avoiding their drawbacks. Finally, the numerical simulations showed that the PD+IRC+PPF controller can eliminate the twelve-poles system vibrations regardless of both the excitation force magnitude and the resonant conditions at a short transient time.

The article describes the application of a straight forward energy converter, a multiphase matrix converter (MC), as part of a device, connected parallel to the power grid, and able to supply variable reactive power flow to the power system. The research performed by authors included the development of control procedures for a multiphase MC, based on a new approach and power system (application) requirements. The multiphase MC structure (6 × 6, 12 × 12) was used since the proposed control procedure creates output as the combination of input voltages. The increased number of phases decreases the order of harmonics in the MC converter similarly as in multilevel converters. This manuscript concentrates on the mathematical analysis of MC work under the “one periodical” algorithm and links it introduces in the power system. The previously developed, spatial-temporal mathematical model of the MC was limited to the dominant (first) harmonic and applied between the grid and reactive load. The results obtained from the analysis of the model showed that, for the applied control procedure (one periodical algorithm), the output voltage is built only from positive or negative sequences of input voltage. Three cases were recognized where the sign input power factor depends on input voltage and control sequence as well as on the value of control frequency. The effects of the model simulation were compared to those obtained from the MATLAB simulation and from the real laboratory 30 kVA-rated model. The main factors analyzed during this research include the expected value and distortion of input current and sign of reactive input power.

In the process of extension sleeve press-fitting of the bearing pressing machine, electro-hydraulic servo system of valve-controlled symmetrical cylinder (ESSVSC) is an important and critical control module, and its performance has a significant impact on the working accuracy of such equipment. People have proposed various related control algorithms. However, external disturbances and unmodeled dynamic factors are often inevitable in practical work and may produce significant influences on the performance of the control system. Existing researches on this issue remains to be enriched and the performance of related algorithms still needs further improvement. An adaptive backstepping control algorithm (ABCA) is proposed in this paper. Firstly, a mathematical model of the ESSVSC is established which takes into account external disturbances and unmodeled dynamic factors. Secondly, the adaptive backstepping controller is designed by using the backstepping algorithm, and the control law and adaptive parameter estimation law are given. The stability of the control system is also proved. The analysis results of the numerical examples show that the proposed algorithm can effectively suppress the adverse effects of typical external disturbances and unmodeled dynamic factors and maintain good control accuracy. The output displacement error of the proposed algorithm is smaller and the tracking performance is better. The control accuracy of our algorithm is improved by 48.33% and 94.76% compared to BSMCA and PID, respectively, which illustrates the rationality of the established mathematical model and the effectiveness of the algorithm. This work is expected to provide useful reference for improving the control performance during the pressing process of the extension cylinder and the algorithm design of ESSVSC.

The objective of the proposed work is to develop a Maximum Power Point Tracking (MPPT) controller and inverter controller by applying the adaptive least mean square (LMS) algorithm to control the total harmonics distortion of a solar photovoltaic system. The advantage of the adaptive LMS algorithm is given by its simplicity and reduced required computational time. The adaptive LMS algorithm is applied to modify the Perturb and Observe (P&O), MPPT controller. In this controller, the adaptive LMS algorithm is used to predict solar photovoltaic power. The adaptive LMS maximum power point tracking controller gives better optimal solutions with less steady error 0.7% (6 watts) and 0% peak overshot in power with the tradeoff being more settling time at 0.33 s. The development of the inverter control law is performed using the d-q frame theory. This helps to reduce the number of equations to build a control law. The load current, grid current and grid voltage are sensed and transformed into d and q components. This adaptive LMS control law is used to extract the reference grid currents and, later, to compare them with the actual grid currents. The result of this comparison is used to generate the switching gate pulses for the inverter switches. The proposed controllers are developed and implemented with a solar PV system in MATLAB Simulink. The total harmonics distortion in grid and load current (3.25% and 7%) and voltage (0%) is investigated under linear and non-linear load conditions with changes in solar irradiations. The analysis is performed by selecting step incremental values and sampling time.

The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss l box , confidence loss l obj , and classification loss l cls , enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

This paper presents a simulation model environment for the popular and low-cost remotely operated vehicle (ROV) BlueROV2 implemented in Simulink™ which has been designed and experimentally validated for benchmark control algorithms for underwater vehicles. The BlueROV2 model is based on Fossen’s equations and includes a kinematic model of the vehicle, the hydrodynamics of vehicle and water interaction, a dynamic model of the thrusters, and, lastly, the gravitational/buoyant forces. The hydrodynamic parameters and thruster model have been validated in a test facility. The benchmark model also includes the ocean current, modeled as constant velocity. The tether connecting the ROV to the top-site facility has been modeled using the lumped mass method and is implemented as a force input to the ROV model. At last, to show the usefulness of the benchmark model, a case study is presented where a BlueROV2 is deployed to inspect an offshore monopile structure. The case study uses a sliding mode controller designed for the BlueROV2. The controller fulfills the design criteria defined for the case study by following the provided trajectory with a low error. It is concluded that the simulator establishes a benchmark for future control schemes for position control and trajectory tracking under the influence of environmental disturbances.