1130 results about "Fuzzy control system" patented technology

The invention relates to the technical field of battery monitoring, in particular to an electric vehicle battery state real-time monitoring system based on deep learning. Comprising a battery data acquisition module, a feature extraction module, a battery state prediction module, a fault monitoring module and a safety regulation and control module. The battery data acquisition module acquires battery parameters in real time, the feature extraction module extracts key features through deep learning, and the battery state prediction module predicts battery capacity, health state and performance degradation based on a neural network and a differential equation. The fault monitoring module adopts integrated learning to detect abnormity and output an alarm signal, and the safety regulation and control module optimizes a charging and discharging strategy through reinforcement learning and fuzzy control to guarantee safe and stable operation of the battery. According to the invention, accurate prediction and intelligent monitoring of the battery state are realized through the deep learning technology, comprehensive state evaluation and fault early warning are provided, and the accuracy and stability of battery state monitoring are effectively improved, so that the operation safety of the electric vehicle is improved, and the service life of the battery is prolonged.

The invention provides a control method based on differential rotation speed torque distribution, which relates to the technical field of intelligent control and comprises the following steps: performing difference calculation on an initial input quantity and a preset rotation speed instruction to generate a rotation speed deviation signal and a deviation change rate, and establishing a dynamic membership function library through a fuzzy controller; mapping the rotating speed deviation and the change rate thereof into a fuzzy language variable, performing reasoning operation according to a preset fuzzy control rule base, and outputting a frequency regulation quantity and a torque compensation coefficient of the frequency converter; and reading the generated torque compensation coefficient and shaft system torque values actually measured by the two torquemeters, calculating a dynamic difference value between the current total load torque and the target torque, establishing a torque distribution weight model according to the transmission characteristics of the differential mechanism, and generating differential torque correction instructions of the two motors in combination with the real-time rotating speed values. The control performance of differential drive is improved.

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention relates to a temperature control intelligent optimization method and system for adhesive tape production. The method comprises the following steps that S1, production data in the drying process are collected through a multi-mode sensor; s2, constructing a state prediction model based on GNN and LSTM, inputting the collected production data into the prediction model, and dynamically predicting the state of each temperature control area; s3, modeling each temperature control area into an intelligent agent based on the predicted state between the temperature control areas, and realizing dynamic distribution of heat resources of each heating interval by training a cooperative control strategy between the intelligent agents; s4, a fuzzy control optimization scheme incorporating environment compensation: for short-term changes of environment temperature and humidity, dynamically adjusting temperature zone gradient distribution between drying sections by using fuzzy control, and optimizing energy efficiency; and S5, the temperature control module adjusts the temperature and the wind speed of the temperature control area in real time according to the optimization schemes obtained in the steps S3 and S4. The intelligent level of the temperature control system in the adhesive tape production drying process is comprehensively improved.

The invention relates to the technical field of electromagnetic flowmeter calibration, and discloses an electromagnetic flowmeter online calibration system and an acquisition and storage device. The system comprises a signal acquisition module, a data processing module, an error analysis module, a parameter calibration module and a real-time monitoring module. The signal acquisition module captures an original signal waveform through a multi-channel sensor array and acquires flow signal data to generate a state set; the data processing module uses a wavelet packet transformation and adaptive filtering combined algorithm to reduce noise and extract features; the error analysis module constructs a dynamic time warping error matching model to obtain an error correction coefficient; the parameter calibration module adopts a fuzzy control and proportional integral differential composite strategy, and adjusts excitation current and electrode sensitivity according to an error correction coefficient; the real-time monitoring module establishes a rolling optimization model with time-varying constraints, and generates a calibration instruction in combination with signal stability and power consumption limitation. According to the system, online accurate calibration of the electromagnetic flowmeter is realized, and the measurement accuracy and the equipment operation stability are improved.

The invention discloses a high-precision coating die head coating thickness adjusting method based on intelligent feedback control. The method comprises the following steps: S1, constructing a data set and transmitting the data set to a controller module; s2, calculating an error and inputting the error to a fuzzy controller for preliminary control, and generating a preliminary control instruction; s3, inputting the generated preliminary control instruction into a PID controller; s4, a model prediction controller is used for predicting the change trend of the coating thickness in the future, and an adjustment signal is sent out in advance; s5, dynamically adjusting parameters of a controller module based on a dual adaptive neural network control mechanism; s6, continuously optimizing the control strategy through a'heuristic-feedback 'mechanism; s7, the control signal generated by the controller module is transmitted to the execution mechanism module; and S8, recording and storing all data in the coating process through a data processing and self-learning module, and continuously optimizing the control strategy. According to the method, fuzzy control, PID and the dual adaptive neural network are combined, and accurate control over the coating thickness is achieved.