MPPT Control Strategy of Photovoltaic Cells Based on Duty Cycle Disturbance

Photovoltaic cell is a key part of solar power generation system, and whether its photoelectric conversion is sufficient is also called the maximum power point tracking problem, that is, photovoltaic cell MPPT. Different from the traditional MPPT control algorithm, this paper models and analyzes the output characteristics of solar cell. on this basis, proposes a fuzzy control algorithm based on duty cycle disturbance, and simulates it with MATLAB. The result shows the algorithm can well take into account the tracking speed and control accuracy when the external environment change.

Vibration Analysis of Civil Engineering Structure Based on Fuzzy Control Algorithm

In the continuous development of China’s industrialization process, urban industrial land is more and more tight, the distance between planning roads or rail transit and buildings is getting closer and closer, and the vibration propagation caused by the load of the ground or underground rail transit is easy to cause the surrounding buildings to vibrate, so as to affect the normal life of urban residents. Therefore, during the design and construction of modern building engineering, in order to fully understand the impact of traffic load on the structure of building vibration, it is necessary to use effective methods to predict the vibration phenomenon caused by various reasons, and thus to conduct in-depth discussion on the overall structure design. On the basis of understanding the traditional vibration analysis method, this paper uses the fuzzy control algorithm to study the influence of single source or compound source fuzzy number on the structure vibration, and carries on the empirical analysis accordingly.

A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions

The paper aims at the incompatibility between the speed and stability of the traditional MPPT algorithm and the imprecise search of the fuzzy control algorithm. An improved photovoltaic adaptive fuzzy control MPPT algorithm is proposed in this thesis. The solar irradiance changes dramatically and hence four kinds of fuzzy control algorithms with different input are modeled and simulated. The results indicate that the proposed fuzzy control algorithm using slope and slope change rate of P-U curve as input is the best. On this basis, dP/dU and duty cycle D(n-1) at n-1 moment are used as input to improve the tracking speed and optimal range. At the same time using shrinkage factor 1/I*|dP/dU| real-time adjustment of D(n-1) further shortens the optimal time of the algorithm. The algorithm is simulated and applied in a block. Simulation results show that the proposed algorithm is superior to the fuzzy control algorithm in steady-state oscillation rate, tracking speed and efficiency, and the algorithm is simple and easy to implement.

Artificial Intelligence Based Sensor Network Congestion Fuzzy Control Algorithm

The topology control of sensor sensor network was studied based on fuzzy control algorithm. Aiming at the dynamic changes of the topology of large-scale and heterogeneous artificial intelligence sensor networks and the incomplete information between nodes, a smart network-based congestion control algorithm for sensor networks was proposed and the performance of fuzzy control algorithms was analyzed. Based on this, a fuzzy control algorithm was designed. The algorithm fully considered the residual energy of nodes and the distribution of nodes in the network. Therefore, the reasonable election of the cluster head can be realized through the game between nodes, which effectively avoided energy holes, made the network energy consumption more uniform, prolonged the network life cycle, and optimized the network topology.

Space robot motion path planning based on fuzzy control algorithm

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

Python-based Fuzzy Control of Ore Feeding System

Using Python as a programming language, this study investigates the problem of controlling the ore amount in the field of mineral processing. First, data on the influencing factors collected from a certain beneficiation mill in Yuanyang are quantitatively analyzed using the NumPy module library. Factors having a greater influence are screened out and then selected as the input while the motor frequency is generated as the output by the fuzzy control algorithm developed using the SciKit Fuzzy module library. The range of values of the fuzzy control variable is defined, the fuzzy membership function is generated, and the fuzzy control rule is established. Finally, the fuzzy controller is activated to realize fuzzy control of the mine. The NumPy algorithm can be effectively applied to the quantitative analysis of data, and the calculation results are reasonable and interpretable. The simulation results are as follows: the hardness of the raw ore and the weight of the belt scale are the key factors for controlling the ore amount, and they can be used as the input variables of the fuzzy control system. The fuzzy controller developed using the SciKit Fuzzy module library can be effectively applied to the field of mineral processing, and it overcomes the limitations of current computer technology in industrial mineral processing.