Advanced Control for Applications最新文献

With the speed growth of petroleum engineering, the requirements for the temperature control performance of petroleum heat transfer oil boilers are becoming higher. Traditional temperature control systems have problems such as poor temperature control accuracy. To address these issues, a temperature control system for petroleum heat transfer oil boilers based on microprocessors and proportional-integral-derivative is designed. The research first studies the fuzzy proportional-integral-derivative control system, and then combines it with a microprocessor to design a new temperature control system. Finally, experiments and practical applications are used to assess the effectiveness of the temperature control system. The results denote that in the simulation experiment, the temperature recognition accuracy of the microprocessor proportional-integral-derivative system is 93.26%. At the same time, the system increases the temperature of the oil outlet to around 100°C after about 4 min of boiler operation, and maintains the stable temperature of the oil outlet continuously. In the study of overshoot, the average overshoot value of the system is 10.03%, and the average steady-state error value is 3.71%. These verification indicators are superior to the comparative control system, indicating that the fuzzy control proportional-integral-derivative temperature control system based on microprocessors has good effects in the application of petroleum heat transfer oil boilers. Through this system, the stability and control accuracy of boiler temperature can be improved, and intelligent control of the boiler can be achieved. This is of great meaning for raising the energy and work efficiency of boilers, and reducing energy waste.

With the continuous development of virtual reality technology (VRt), the clothing industry has begun to use VRt to build three-dimensional clothing fitting models, which provide consumers with a comprehensive range of fitting effects. However, the traditional 3D (three-dimensional) imaging model construction method for clothing has certain shortcomings. For example, the construction of fitting models is a process from planar to three-dimensional, while the three-dimensional fitting model is a process from two-dimensional to three-dimensional, which makes it impossible for users to obtain a more intuitive, visual, and comprehensive fitting effect during fitting. On the basis of summarizing the existing methods for building 3D imaging models of clothing, this paper proposed a method for building 3D clothing fitting models based on VRt and applied it to actual clothing fitting. This provided consumers with a comprehensive method for fitting clothing, thereby improving the shopping efficiency and quality of consumers when purchasing clothing. The research results show that the proportion of positive evaluations using VRt systems was 92%, while the proportion of positive evaluations using conventional technology systems was only 10%, indicating a positive relationship between VRt and the construction of 3D imaging models for clothing fitting. The VRt-based clothing fitting system has strong practical significance, but it is still in the preliminary exploration stage. The system proposed in this paper does not include face modeling function. Therefore, in the future, we can consider adding a face reconstruction module to build a more personalized human model through photo reconstruction and other ways.

With the growth of Internet of Things technology, more and more businesses are implementing automated cargo storage systems. By using an appropriate automated storage space allocation model, these businesses can significantly reduce their storage pressure while saving money on logistics and increasing the effectiveness of their product distribution. Therefore, the study is based on the non-dominated sorting genetic algorithms II (non-dominated sorting genetic algorithm, NSGA II), which combines the three basic principles of space allocation as the objective function applied to the allocation model of the algorithm, in order to optimize the space model for automated storage of finished cigarettes. The algorithm is run to obtain 20 Pareto solutions and examine their three objective functions. The experiment's findings revealed, after optimizing the NSGA-II algorithm in this study, the average reduction rate of shipping efficiency is 32%, the average reduction rate of shelf stability is 54%, and the average reduction rate of product correlation is about 77%, indicating that the algorithm optimization is highly effective.

Power transformers undertake the task of transforming voltage and transmitting electrical energy. Its operating status is directly connected with the stability and safety of the whole power system, and it is very important to judge the operating conditions of power transformers and diagnose fault types. The use of dissolved gas analysis technology in oil can provide preliminary fault diagnosis for transformers. However, with the increasing demand for fault diagnosis accuracy in modern electrical equipment, relying only on dissolved gas analysis technology in oil cannot satisfy the demands. To lift the transformer fault diagnosis accuracy, this study introduces the K-means algorithm into the model and constructs a high-precision and fast convergence diagnosis method and a power transformer fault location recognition model. In the example analysis, kernel functions were selected for training five typical gases to obtain the optimal parameters, and their prediction curves and errors were analyzed. Its diagnostic accuracy is 98.4%, and the error in all five gases is within 1 (uL/L). The average error of the improved support vector machine intelligent algorithm is lower than that of the previous model and other prediction methods. By testing the same sample data, the correctness of this method was verified. The significance of improving support vector machines lies in further improving the performance and applicability of the original support vector machine algorithm, providing a basis for future transformer maintenance and contributing to social development and continuous improvement of economic benefits.

With the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane-changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane-changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.

In this article, we are interested in the problem of adaptive synchronization of a voltage source converter with a possibly weak grid with unknown angle and frequency. To guarantee a suitable synchronization with the angle of the three-phase grid voltage we design an adaptive observer for such a signal requiring measurements only at the point of common coupling. Then we propose an alternative certainty-equivalent, adaptive phase-locked loop that ensures the angle estimation error goes to zero for almost all initial conditions. Although well-known, for the sake of completeness, we also present a PI controller with feedforward action that ensures the converter currents converge to an arbitrary desired value. Relevance of the theoretical results and their robustness to variation of the grid parameters are thoroughly discussed and validated in the challenging scenario of a converter connected to a grid with low short-circuit-ratio.

A Flocking obstacle avoidance algorithm based on the extensive game with perfect information is proposed for the blockage problem of UAV swarm in front of multi-narrow type obstacles. The two UAVs closest to the target are selected as participants of the game, and the game tree is defined to determine the combination of the motion strategies of the two UAVs to obtain the payoff matrix. Determine the subgame perfect Nash equilibrium to get the optimal strategy, and give the UAVs different motion states respectively so as to ensure that the UAVs can successfully pass multi-narrow type obstacles. Simulation results demonstrate that the proposed algorithm has a higher over-hole rate in the case of the multi-narrow type obstacle compared to the static game-based Flocking obstacle avoidance algorithm.

Search and rescue operations are all time-sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real-life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators.

Industrial robot is a and multi-output complex system with strong coupling and high nonlinearity. The motion control accuracy of the system is affected by many factors. To solve the difficulty in establishing the input and output characteristics of robot dynamics modeling, the robot motion model is established through the Lagrangian energy function. At the same time, the nonlinear relationship between angular velocity, angular acceleration, and robot torque is accurately expressed through improved cascaded neural network. In addition, the optimal time planning of the robot's trajectory in joint space is studied using multinomial interpolation method and the particle swarm optimization (PSO). In the simulation experiment, the effect of the proposed dynamic model fitting was outstanding. Under the mixed multinomial difference calculation planning, the angular position trajectories of the three joints changed very smoothly. In the data set application test, the average error of the PSO algorithm was 0.4061 mm and the average task time was 9.101 s, which were lower than other planning algorithms. Experiments showed that the Lagrangian dynamic model analysis based on genetic algorithm cascaded neural network and PSO trajectory scheduling method under mixed multinomial difference had better trajectory planning performance in handling tasks.

The flow field of the environment plays a crucial role in cigarette combustion cone fallout propensity test, with air velocity exhibiting a positive correlation with combustion volume. In order to minimize the impact of the environmental flow field on the test results, it is necessary to control the air speed within the range of 200 ± 30 mm/s in the test area of each tobacco test channel. To address this concern, which used the Realizable k-ε model to develop a mathematical model of the testing environment. The uniformity of air speed in each channel and its relationship with structural parameters were then analyzed. Based on these findings, the key structural parameters of the ventilation hood are optimized. After restimulated the optimized model, the results demonstrate a higher level of uniformity in the environmental flow field of the optimized section. To validate the accuracy of the simulation results, measurements indicated that the maximum air speed value at all points is 225.6 mm/s, while the minimum value is 178.44 mm/s. These values fall within the specified range of 200 ± 30 mm/s, thus meeting the design requirements. This study ensures that the cigarette can burn in a steady state during the cigarette combustion fallout propensity test and improves the stability of the cigarette combustion cone drop tendency test results.