7 results about "Fuzzy inference engine" patented technology

This invention relates to a cascaded fuzzy feedforward direct-drive speed control method and system based on LC filtering. The method includes: obtaining a speed error and error rate of change from a reference speed and a real-time measured speed; inputting the speed error and error rate of change into a Mamdani fuzzy inference engine to output a power setpoint coefficient; inputting the power setpoint coefficient and a normalized measured speed signal into a two-dimensional torque-current reference lookup table, performing bilinear interpolation calculation, and outputting an electromagnetic torque command; converting the electromagnetic torque command into a quadrature-axis command current, generating a PWM signal through current closed-loop regulation and SVPWM modulation, and driving a two-level voltage-type inverter; the output of the two-level voltage-type inverter drives a low-voltage permanent magnet synchronous motor through an LC sine wave filter with an RC damping branch. Compared with the prior art, this invention has advantages such as low computational load, improved dynamic response, and enhanced disturbance rejection.

This invention discloses a fuzzy logic energy-saving control method for a mechanical jigging screen, relating to the field of mineral sorting equipment control technology. The method includes real-time acquisition of raw multi-source state data from the equipment, preprocessing to generate a standardized state feature set containing normalized feed load characteristics, particle size distribution feature vectors, bed thickness variation trend characteristics, and motor power spectrum characteristics. This set is then input into a preset fuzzy inference engine, where fuzzy logic operations are performed using a predefined and tuned fuzzy rule base. The output is a fuzzy set of optimal driving parameters covering fuzzy suggestions for screen stroke, stroke frequency, and acceleration. This set is then defuzzified and converted into deterministic driving command parameters. This method enables precise adaptation of the equipment's multi-source operating states and driving parameters, enhances the collaborative adjustment capability of multi-dimensional driving parameters, optimizes equipment operating adaptability, and achieves the technical effects of dynamic matching of operating conditions and energy-saving regulation.

This invention discloses an energy management method for hybrid energy storage systems based on fuzzy model predictive control. It constructs a linear variable parameter model predictive control architecture incorporating fuzzy decision-making for closed-loop optimization control. First, an LPV prediction model for the system is established, and the load power sequence is introduced as a measurable feedforward disturbance into the state equation. During the rolling optimization preparation stage, a fuzzy inference mechanism is used to analyze multi-dimensional operating conditions such as system power fluctuations, SOC, and SOV in real time, dynamically adjusting the weighting factors of energy tracking, battery loss, and current smoothing in the cost function to achieve an adaptive trade-off in the control strategy. Simultaneously, a hierarchical setpoint tracking strategy is adopted, intelligently switching the voltage state reference target based on the real-time energy level of the supercapacitor. Finally, based on the time-varying model, dynamic weights, and the reference target, a quadratic programming problem is solved, outputting the optimal control quantity applied to the power converter, thereby significantly improving the system's adaptive coordination capability and overall operating efficiency under complex and variable operating conditions.

The application provides a TBM jamming risk intelligent early warning method and system based on knowledge-data double driving, which comprises the following steps: acquiring multi-source parameter information collected in the TBM tunneling process of TBM tunneling cases under different stratum tunneling conditions, and constructing a multi-source parameter database; processing the data in the multi-source parameter database, and calculating data-driven jamming probability based on a robust time sequence anomaly detection model for abnormal changes; extracting expert experience knowledge based on TBM jamming literature research and experience knowledge in actual construction cases, and constructing a TBM tunneling jamming event knowledge base; determining the membership degree and the reliability of the fuzzy condition, constructing a TBM tunneling jamming event rule reasoning machine, and calculating knowledge-driven jamming probability based on a fuzzy reasoning machine model; multiplying the data-driven probability and the knowledge-driven probability by using a factor multiplication method, calculating the TBM tunneling jamming probability, and obtaining the TBM tunneling jamming risk prediction result based on the knowledge-data double driving.

This invention discloses a method, system, equipment, and medium for real-time quality assessment in workshop production processes, specifically relating to the fields of intelligent manufacturing and industrial automation control technology. The method includes: S1 collecting multi-source heterogeneous data and forming a fused feature vector through spatiotemporal matching; S2 constructing a digital twin benchmark model based on historical good product data to generate a dynamic time-varying tolerance band; S3 dynamically adjusting thresholds based on equipment health status and calculating a multi-dimensional comprehensive deviation index; S4 matching rules using a fuzzy inference engine to output normal, warning, and out-of-control status labels and potential root cause hints; and S5 storing data and incrementally updating the model using newly added good product data. This invention solves the problems of poor real-time performance, high false alarm rate, and difficulty in root cause localization in traditional assessment methods through multi-source data fusion, dynamic threshold adaptation, equipment status perception, and model iterative optimization. It improves the accuracy, adaptability, and intelligence level of quality assessment, making it suitable for real-time quality control in workshop production processes.

The application relates to the field of avionics, and discloses a method and system for friendly behavior prediction and multi-platform autonomous decision-making in communication interruption, which comprises the following steps: constructing a multi-level fuzzy reasoning tree, fuzzy processing multi-source heterogeneous input information to form multiple fuzzy sets; decomposing complex input relations into several subsystems based on a hierarchical fuzzy reasoning mechanism, and obtaining a subtask response benefit value through layer-by-layer reasoning; and fuzzy processing input variables based on a triangular membership function, and predicting a potential task allocation scheme of a friendly formation in combination with a hierarchical rule reasoning mode. The application can realize multi-platform autonomous task allocation without a center, with low dependence and high robustness, and can improve the cooperative decision-making ability and task execution efficiency of formation members under non-perfect information conditions in a complex environment.

This invention discloses an intelligent diagnostic expert system for underground drainage pipe networks based on fuzzy rules for water flow velocity, comprising external factors, internal pipe network processes, water flow velocity data, and a working condition analysis and diagnostic system. The working condition analysis and diagnostic system in this expert system is implemented using fuzzy rules for water flow velocity, including two parts: establishing a fuzzy rule base and fuzzy inference. Establishing the fuzzy rule base includes the steps of: determining the fuzzy inference engine structure, determining the fuzzy subset membership functions, establishing fuzzy control rules, and identifying water flow velocity parameters. Fuzzy inference includes the steps of: fuzzifying water flow velocity data, establishing a fuzzy inference engine, and defuzzifying the data. This invention, using fuzzy rules for water flow velocity to implement an intelligent diagnostic expert system for underground pipe networks, has the characteristic of requiring no manual operation. It relies on water flow velocity data to analyze and diagnose the status of underground drainage pipes and can be applied to the complex working conditions of underground pipe networks.