6 results about "Fuzzy neural network controller" patented technology

The invention discloses a fuzzy neural network-based intelligent tension control method and system for a plateau tunnel pre-stressed anchor rod, and the system is characterized in that a free section of the anchor rod is sleeved with a negative Poisson ratio outer sleeve, a magneto-rheological regulation and control cavity is formed in the anchor rod, magneto-rheological fluid is injected into the magneto-rheological regulation and control cavity, and a friction power generation-sensing assembly is mounted at the tail end of the anchor rod; therefore, the anchor rod unit integrating friction increase along with the load, adjustable damping and self-energized sensing is formed. In the tensioning process, the friction power generation-sensing assembly is used for measuring the elongation of the anchor rod and providing self-powered energy, the pressure of a magneto-rheological regulation and control cavity and the displacement of a piston are collected, a state vector containing design prestress, the elongation, equivalent damping force and environment vibration indexes is constructed and input into a fuzzy neural network controller, and the state vector is calculated. The expansion amount of the hydraulic jack and the current of the electromagnetic coil are output, and intelligent application, dynamic compensation and long-term monitoring of prestress of the anchor rod group are achieved. Self-sensing, self-adaptive adjustment and active friction increasing of the pre-stressed anchor rod group can be achieved without an external power source.

This invention provides a horizontal strip machining center oil injection device, belonging to the field of machining center equipment technology. It includes a data acquisition and preprocessing module, a collaborative trajectory planning and linkage control module, an intelligent oil injection decision module, a motion and fluid collaborative control module, and a self-optimization and health management module. It constructs a machining state feature vector through multimodal sensor fusion, generates the desired nozzle position using a bidirectional decoupled following strategy, dynamically adjusts the oil injection flow rate, pressure, frequency, and injection angle based on a T-S fuzzy neural network controller, and achieves collaborative control of motion and fluid through a cascaded double closed-loop structure. Simultaneously, it constructs an incremental random forest learning engine for parameter self-optimization and equipment health assessment. This invention achieves precise nozzle position tracking of the tool trajectory, dynamic adaptation of oil injection parameters to machining states, and collaborative operation of oil injection and chip removal, improving cooling and lubrication accuracy and system operational reliability.

This invention discloses a precise deagglomeration and dispersal modification control system for non-metallic powders, belonging to the field of intelligent control technology based on deep learning. Specifically, it includes: a non-metallic powder identification module, a non-metallic powder deagglomeration module, an atomization coating module, and a coordination control module. The non-metallic powder identification module deploys hardware units to collect raw powder data; the non-metallic powder deagglomeration module constructs a calculation model to calculate the energy required for dispersing the non-metallic powder and implements precise deagglomeration; the atomization coating module uses reverse reasoning to charge and directionally adsorb droplets, completing the atomization of the modifier and coating of the newly formed surface in the same space where deagglomeration occurs; the coordination control module, through the construction of a time-window preemptive scheduling control strategy and the embedding of a main control board with a built-in feedforward fuzzy neural network controller, triggers the injection of the modifier during the interval of deagglomeration energy release, achieving precise synchronous control of "deagglomeration equals modification".

This invention relates to a fuzzy neural network-based self-tuning PID control system for corrugated board warpage deformation, applied in the field of corrugated board production technology. The system includes: a data acquisition unit for real-time acquisition of raw paper moisture content, preheating cylinder temperature, ambient relative humidity, production line speed, and composite tension; a warpage detection device for detecting the amount of board warpage; a fuzzy neural network controller for outputting PID parameter adjustment values; a PID parameter self-tuning unit for real-time calculation of PID parameters; a PID controller for calculating control values; an actuator unit for adjusting operating parameters; and a graded trigger optimization module for graded online fine-tuning based on a superior product threshold and a qualified product threshold: no update when deviation ≤ superior product threshold, data is stored only when superior product threshold < deviation ≤ qualified product threshold, and an update is triggered when deviation > qualified product threshold. This invention solves the problems of low control accuracy and frequent ineffective adjustments in existing technologies, improving system stability and adaptability.

The application relates to the technical field of motor control, and discloses a permanent magnet synchronous motor control method based on K-Means optimized fuzzy RBF neural network, which initializes fuzzy RBF neural network parameters through K-Means clustering; actual output values V of a permanent magnet motor vector control system are obtained through sampling, and the actual output values V are subtracted from given values V ★ to calculate the deviation and the deviation change of the system; a fuzzy RBF neural network controller is constructed, e(k) and e c (k) of the system are taken as inputs of the fuzzy RBF neural network controller, k p , k i , k d are obtained through a four-layer network, and the outputs are input into a PID controller to obtain an output control amount iq, and current loop PID control is continuously carried out; the motor control method has obviously improved steady-state and dynamic performance, has strong robustness, and can effectively enhance the anti-interference capability of the motor.

The application provides a large workpiece-based ultrasonic sensitivity detection method, relates to the technical field of detection, and aims to solve the problems of inconsistent sensitivity in the full-thickness range and insufficient detection precision of deep defects in traditional ultrasonic detection. The method sets multiple groups of depth-adaptive equivalent reflectors on the test block, dynamically adjusts the gain value in combination with the fuzzy neural network controller, realizes sensitivity adaptive compensation in the full-thickness range, and ensures that the detection error is stable within ±1%. Based on the dynamic reflector interval design of the material attenuation coefficient and the acoustic parameter, the reflector distribution is optimized, and the complexity of artificial calibration is significantly reduced. The TCG curve is fitted by the piecewise weighted least square method and simulated to generate a high-precision DAC curve, realizing high-confidence output of defect quantitative evaluation. The sensitivity, accuracy and engineering applicability of ultrasonic detection of large workpieces are improved, and the method is particularly suitable for defect detection of complex curvature workpieces.