23 results about "Fuzzy logic controller" patented technology

A control method for a turbine speed regulation optimization system based on hybrid intelligent feedforward compensation belongs to the field of turbine speed regulation system control technology. This method simultaneously triggers two feedforward channels—data-driven and knowledge-driven—when a change in head or load is detected: the data-driven channel outputs a first opening compensation amount using a neural network model trained on historical operating data, while the knowledge-driven channel outputs a second opening compensation amount using a fuzzy logic controller built based on the turbine's physical characteristics and expert experience. The severity index of the operating condition change is calculated based on the changes in head and load, and the two compensation amounts are adaptively weighted and fused to generate the final feedforward opening compensation amount. This compensation amount is then superimposed on the PID controller output command to form the main guide vane opening control signal. This invention overcomes the lag of traditional feedback control through a hybrid feedforward mechanism, improving the dynamic response speed and regulation stability of the unit under conditions of large head fluctuations and frequent load changes.

This application relates to the field of artificial intelligence and discloses a spatiotemporal graph convolutional method for order demand prediction that integrates functional semantic similarity. The method includes: collecting multi-source heterogeneous data streams containing orders, delivery personnel poses, road conditions, and the environment; constructing a unified grid feature vector representation through spatiotemporal gridding processing; performing order demand prediction using a spatiotemporal graph convolutional network that integrates spatial adjacency edges and functional semantic similarity edges; capturing spatiotemporal correlation features and generating prediction results through a multi-head attention mechanism and gated recurrent units; encoding the prediction results as state vectors and inputting them into a reinforcement learning policy network to output optimal allocation instructions; triggering a pre-scheduling mechanism based on the prediction results to guide idle delivery personnel to move to high-demand grids before orders are generated; performing distributed computing using an edge-cloud collaborative architecture; and dynamically adjusting and optimizing weights and performing anomaly detection and emergency recovery through a fuzzy logic controller.

This application relates to the field of multi-terminal flexible DC transmission system equipment, specifically to a method and system for adaptive adjustment of droop coefficient in a multi-terminal flexible DC transmission system. The method includes: real-time monitoring and acquisition of local electrical quantities; inputting the electrical quantities as input variables to a main fuzzy logic controller; outputting an initial adjustment amount for the droop coefficient; inputting the absolute values ​​of the real-time power margin deviation and DC voltage deviation to the corresponding auxiliary fuzzy logic controllers to obtain a power margin weighting factor and a DC voltage weighting factor, which are then used to weight and correct the initial adjustment amount to obtain the droop coefficient adjustment amount; adding the droop coefficient adjustment amount to the initial droop coefficient to obtain the real-time droop coefficient; and performing amplitude limiting processing on the real-time droop coefficient to obtain the final real-time droop coefficient; applying the method to the droop controller of the corresponding converter station to complete the adaptive adjustment of the converter station's output characteristics, thus forming a hierarchical intelligent decision-making system.

The present application relates to the technical field of Internet of Vehicles edge computing, and particularly relates to a sensing context task offloading method for Internet of Vehicles edge computing, comprising: constructing an upper fuzzy logic controller and a lower reinforcement learning decision model; acquiring a current time and an urgency of a to-be-processed task and inputting the upper fuzzy logic controller, reasoning through a predefined fuzzy rule base, and outputting dynamic reward weights of time delay, energy consumption and economic cost; constructing a state space and an action space of the reinforcement learning decision model; generating a binary action mask according to a link connection time between a source vehicle and a candidate task offloading node, eliminating unreachable actions in the action space, and remodeling an action probability distribution; executing an action based on the remodeled action probability distribution, calculating a reward value according to a reward function containing the dynamic reward weights, and iteratively executing until convergence to obtain an optimal task offloading strategy; the present application is significantly superior to a benchmark algorithm in comprehensive utility, time delay guarantee and convergence efficiency.

The application discloses a cab cleanliness determination method, device, equipment and storage medium, and belongs to the technical field of automobiles. The method specifically comprises the following steps: acquiring environment image information of a cab and air sensing information of the cab; based on the environment image information, a preset visual cleanliness evaluation strategy is used to obtain visual cleanliness; based on the air sensing information, a preset air cleanliness evaluation strategy is used to obtain air cleanliness; based on the visual cleanliness and the air cleanliness, a fuzzy logic controller is used to determine the cab cleanliness and a processing suggestion corresponding to the cab cleanliness; and the cab cleanliness and the processing suggestion corresponding to the cab cleanliness are output.

This invention provides a method and system for controlling construction waste crushing based on load feedback. The method includes: acquiring the stator current signal of the crusher's main shaft motor; extracting the active current component representing torque; constructing a time sliding window sequence; calculating the algebraic difference between the current active current component and a preset target value to obtain the original load deviation; constructing a weighted function to correct the load deviation amplitude; obtaining the load change rate by taking the first derivative with respect to time; building a fuzzy logic controller, using a Gaussian membership function in the fuzzification stage to establish a mapping between sample entropy and the width of the Gaussian distribution, and setting the membership function width in real time; adjusting the fuzzy subset of PID parameters according to fuzzy rules; using the peak-to-mean ratio of the sliding window sequence as an impact factor to weight and correct the centroid method defuzzification results; superimposing the adjustment increment onto the PID reference parameters; and combining the corrected load deviation to calculate the control command and adjust the output frequency of the feed motor inverter. This invention enables multi-dimensional adjustment of the crusher.

The present application relates to the field of physical therapy equipment, especially to a kind of spinal step quantitative traction physiotherapy control system based on closed-loop force feedback, the system includes: data acquisition module, real-time traction force value, electromyographic signal data, displacement rate of change between traction bed movable bed plate and traction bed fixed bed body are collected;Traction bed drive module, according to the traction force control instruction output by self-learning fuzzy logic controller, execute traction action to traction bed body;Self-learning fuzzy logic controller, according to real-time traction force value, electromyographic signal data, displacement rate of change and the pain level signal feedback by the object to be traction, using step traction strategy, generates traction force control instruction, traction force control instruction is set to use the fuzzy parameter adjustment model pre-constructed based on neural network and fuzzy inference, dynamically generated by updating the shape parameter of membership function of first fuzzy logic rule.The present application effectively improves the self-adaptability and accuracy of traction force regulation.

This invention discloses a fuzzy adaptive intelligent wiper motor control method and system based on noise evaluation. It includes an initial speed curve design module for generating an initial angular velocity curve based on the wiper operating frequency and wiping angle; an adaptive algorithm optimization module that processes real-time wiper operating data through a fuzzy logic controller, outputting speed and acceleration adjustment values; a neural network optimization module that uses a multi-layer feedforward neural network to optimize the fuzzy logic controller parameters to minimize the objective function; and a motor control module that adjusts the motor setpoint in real time based on the optimized adjustment values. This invention solves the problem of abnormal noise from automotive wipers, which is caused by excessive noise or insufficient wiping efficiency due to improper speed control. It significantly reduces operating noise, ensures that the wiping frequency meets regulatory requirements, and improves the adaptability and stability of the control system.

This invention relates to the field of wind power grid-connected control technology, and provides a method and system for controlling the virtual impedance of a converter adapted to the parallel operation of multiple wind turbines. Instead of using a fixed virtual impedance value, it acquires two key dynamic indicators in real time: the normalized active power characterizing the converter's own operating conditions and the estimated short-circuit ratio characterizing the external power grid. These two dynamic indicators are used as inputs to a fuzzy logic controller, which uses a fuzzy inference mechanism to comprehensively determine the current complex operating conditions, thereby dynamically and online generating adjustment coefficients for virtual resistance and virtual inductance. Based on these adjustment coefficients, the final virtual impedance value is adjusted in real time, enabling it to automatically adapt to power fluctuations and changes in grid strength, ensuring that the wind turbines maintain good damping characteristics and power output performance across a wide operating range.

This invention relates to a fuzzy adaptive inertia control method for a supercapacitor-based doubly-fed induction generator (DFIG) wind turbine, belonging to the field of power systems. The invention addresses the problems in existing wind-storage joint frequency regulation control methods, such as poor adaptability due to fixed parameters, susceptibility to overcharging and over-discharging due to lack of consideration for energy storage state-of-charge constraints, and the lack of a coordinated control mechanism for frequency support and voltage stability. The invention includes: establishing a doubly-fed induction generator (DFIG) and supercapacitor energy storage system; establishing a mathematical model of virtual rotational inertia and inertia coefficient; designing a fuzzy logic controller using frequency deviation, frequency change rate, DC bus voltage, and SOC as inputs, outputting inertia coefficient correction, voltage deviation coefficient correction, and energy storage output correction coefficient; updating the virtual rotational inertia and voltage compensation to synthesize a composite power command; and when the SOC enters the danger zone, the supercapacitor is deactivated, and the DFIG switches to VSG mode to compensate for active power deficit.

This invention relates to a seamless indoor / outdoor positioning method and system based on dynamic entropy fuzzy evaluation and adaptive filtering. The method includes: real-time acquisition of observation data through a sliding window; dynamic weighting of multiple signal quality evaluation indicators using a time-varying entropy method based on the observation data to calculate an adaptive signal quality evaluation value for the current environment; calculation of a comprehensive confidence level using a fuzzy logic controller based on the signal quality evaluation value; adaptive adjustment of the measurement noise covariance matrix according to the comprehensive confidence level using a nonlinear mapping function; determination of the current system state by comparing the signal quality evaluation value with a preset threshold, and EKF measurement update and positioning calculation based on the current system state and IMU motion state data to obtain the current positioning; and repeated data acquisition and positioning calculation to achieve smooth handover of the positioning trajectory. Compared with existing technologies, this invention achieves continuous, stable, and smooth seamless positioning in complex indoor / outdoor mixed environments.

This invention discloses a method and system for coordinated control of power generation and energy storage in off-grid drilling. The method includes: collecting and processing real-time drilling operation data to form a structured input sequence; inputting the structured input sequence into a pre-trained drilling power prediction model to obtain a drilling load power prediction curve; initializing the safe range of the energy storage system's state of charge (SOC) and determining the initial target SOC based on the daily work plan; calculating the power deviation; generating dynamic charge / discharge threshold boundaries using a fuzzy logic controller; executing coordinated action decisions for power generation and energy storage based on the power deviation and the dynamic charge / discharge threshold boundaries; periodically updating the current SOC and feeding it back to the fuzzy logic controller; and optimizing the target SOC using a rolling optimization scheduling strategy. This invention enables forward-looking power scheduling, improves the effectiveness of energy storage regulation, improves the operating conditions of generator sets, and significantly enhances system energy utilization efficiency and power supply stability.

This invention provides a method and system for controlling construction waste crushing based on load feedback. The method includes: acquiring the stator current signal of the crusher's main shaft motor; extracting the active current component representing torque; constructing a time sliding window sequence; calculating the algebraic difference between the current active current component and a preset target value to obtain the original load deviation; constructing a weighted function to correct the load deviation amplitude; obtaining the load change rate by taking the first derivative with respect to time; building a fuzzy logic controller, using a Gaussian membership function in the fuzzification stage to establish a mapping between sample entropy and the width of the Gaussian distribution, and setting the membership function width in real time; adjusting the fuzzy subset of PID parameters according to fuzzy rules; using the peak-to-mean ratio of the sliding window sequence as an impact factor to weight and correct the centroid method defuzzification results; superimposing the adjustment increment onto the PID reference parameters; and combining the corrected load deviation to calculate the control command and adjust the output frequency of the feed motor inverter. This invention enables multi-dimensional adjustment of the crusher.

The application provides a kind of unmanned aerial vehicle autonomous inspection trajectory planning method and system based on channel environment real-time map.The method first constructs real-time map containing semantic information by using multi-sensor fusion data online and generates Euclidean signed distance field synchronously;Then, based on the distance field, a safe flight corridor is generated, and a planning algorithm guided by a dynamic fuzzy logic controller that integrates energy state perception is used to plan a global spatiotemporal trajectory within the corridor that meets the dynamics and energy constraints;Next, taking the global trajectory as a reference, a local planner that integrates a velocity obstacle mechanism is used to perform real-time trajectory optimization and dynamic obstacle avoidance under the constraint of the safe corridor;Finally, the control instructions are executed and the image quality is evaluated, and based on the evaluation results and the state of the unmanned aerial vehicle, a dynamic decision is made on whether to adjust the trajectory or execute an emergency strategy.The application significantly improves the safety, efficiency and data acquisition quality of the unmanned aerial vehicle in the complex distribution network channel.

The application discloses a kind of based on cloud edge coordination's hydrogen production station operation scheduling method and system, method includes: by hydrogen production station respectively from cloud server obtains current scheduling instruction and from edge node obtains target scheduling instruction, constructs instruction difference vector;According to instruction difference vector calculation conflict index, determines fusion weight set based on conflict index, utilizes fuzzy logic controller to carry out smooth transition processing to fusion weight set, generates intermediate instruction sequence;Intermediate instruction sequence is issued to edge node execution, obtains execution deviation and judges whether there is response coordination demand, if there is then determine coordination parameter set;Coordination parameter set is applied to the level switching process of hydrogen production station group, extracts final power regulation value, and determines whether it meets grid peak shaving requirement, if it meets then output final operation instruction sequence, the present application improves hydrogen production station scheduling stability and grid peak shaving response accuracy.

The application discloses a fuzzy energy management method based on LSTM-solar radiation prediction, first, the historical solar radiation data and the battery state of charge SOC of a photovoltaic system are collected, and pretreatment and time characteristic coding are carried out, then the trained LSTM model is used to predict the solar radiation trend in the future set time period, the future effective radiation intensity is obtained, and a fuzzy logic controller is designed, the battery SOC, the current effective radiation intensity and the predicted future effective radiation intensity are taken as input variables, through fuzzy reasoning and defuzzification calculation, finally, the load cut-off grade is output to control the multi-stage load in the system in a graded on-off mode. The method can combine the current energy state and the future energy acquisition trend for active management, effectively solve the problem that the existing fixed threshold strategy is prone to mis-cutting or energy waste under high-cold or variable weather, and maximize the service time and energy utilization rate of the system while ensuring the continuous operation of the key load.

This invention discloses a VSG control method for wind power storage systems based on fuzzy logic and particle swarm optimization. This method addresses the problem of permanent magnet synchronous wind turbines in high-proportion renewable energy power systems being unable to respond to system frequency fluctuations and the fixed parameters of traditional VSGs. It constructs a wind power grid-connected system model with a lithium battery energy storage system integrated into the DC side. First, maximum wind energy capture is achieved through turbine-side vector control, utilizing the turbine-side lithium battery energy storage system to smooth power fluctuations and maintain stable bus voltage. Second, in the grid-side DC / AC converter VSG control, a fuzzy logic controller is introduced to adaptively adjust the rotational inertia in real time based on frequency deviation and rate of change to respond to transient frequency changes. Simultaneously, a particle swarm optimization algorithm (PSO) is used with the time-integral-absolute-equation (ITAE) as the objective function to globally optimize the damping coefficient, resolving parameter correlation issues. This invention effectively reduces frequency deviation and settling time under load abrupt changes and wind speed fluctuations, significantly improving the system's active frequency support capability and dynamic stability.

This invention provides a method, system, device, and readable storage medium for recharging unmanned aerial vehicles (UAVs). The method includes: acquiring multi-dimensional state information of a target UAV; based on the multi-dimensional state information of the target UAV, indexing a preset membership function library and a fuzzy rule library, and outputting a quantified value of charging demand; when the quantified value of charging demand is greater than or equal to a preset charging threshold, determining the charging mode of the target UAV and the rendezvous coordinates of the target UAV and the mobile charging platform based on real-time environmental state information, and recharging the target UAV at the rendezvous coordinates. Compared with existing technologies, this invention weighted and fused information from three dimensions: energy state, task urgency, and arrival difficulty, and simulated the judgment method of human experts through a fuzzy logic controller, achieving on-demand intelligent charging. Simultaneously, this invention can ensure that the target area is always covered by UAVs, allowing for zero-interruption mission execution.