15 results about "Fuzzy neural network controller" patented technology

The invention provides a brushless motor attitude detection and balance control method and system based on a gyroscope, and relates to the technical field of control, and the method comprises the steps: collecting angular velocity and acceleration data through a six-axis gyroscope, carrying out the noise reduction through wavelet transform, and carrying out the attitude calculation through the combination of an extended Kalman filter and a quaternion algorithm. A rotor position signal is obtained through a magnetic encoder, nonlinear compensation is carried out, and rotating speed data are calculated. A motor state is modeled by adopting a long-short-term memory network, a double-layer adaptive fuzzy neural network controller is constructed, and attitude error compensation and rotation speed fluctuation suppression are realized. A controller model is optimized through particle swarm optimization and a genetic algorithm, a compensation current vector is corrected in real time, and the control precision and stability of the brushless motor are improved. According to the method, the operation efficiency and the dynamic response capability of the brushless motor are effectively improved.

The invention belongs to the technical field of laser welding, and discloses a real-time feedback control method and system for laser welding penetration stability, and the method comprises the steps: obtaining plasma electron temperature characteristics in a laser welding process in real time through a spectrum monitoring system, and enabling the plasma electron temperature characteristics to be associated with penetration fluctuation as a core input signal of feedback control. The signal has better real-time performance and more accurate feature extraction capability in real-time feedback control of laser welding by virtue of broadband coverage and multi-dimensional information acquisition capability of the signal in combination with a more efficient data analysis mode. A parallel type self-learning fuzzy neural network controller is used for executing real-time feedback control output of laser welding penetration fluctuation. On a control architecture, a traditional PD controller and a fuzzy neural network are connected in parallel and are respectively used as a PD control module and a fuzzy neural network control module. And the process database is embedded into the forepart structure of the neural network control module in a fuzzy rule form.

The invention provides an ultrasonic sensitivity detection method based on a large workpiece, relates to the technical field of detection, and aims to solve the problems of inconsistent sensitivity in a full thickness range and insufficient deep defect detection precision of traditional ultrasonic detection. According to the method, multiple groups of depth-adaptive equivalent reflectors are arranged on a test block, and a gain value is dynamically adjusted in combination with a fuzzy neural network controller, so that sensitivity adaptive compensation in a full thickness range is realized, and a detection error is ensured to be stabilized within + / -1%; according to the dynamic reflector interval design based on the material attenuation coefficient and the acoustic parameter, the reflector distribution is optimized, and the manual calibration complexity is remarkably reduced. And fitting a TCG curve through a segmented weighted least square method and carrying out simulation verification to generate a high-precision DAC curve, so that high-confidence output of quantitative defect evaluation is realized. The method improves the sensitivity, accuracy and engineering applicability of ultrasonic detection of large workpieces, and is especially suitable for defect detection of workpieces with complex curvatures.

The invention relates to the technical field of servo drivers, in particular to a servo driver control method, and the technical scheme comprises the steps: carrying out the online identification of load inertia and external disturbance through a self-adaptive observer, generating a dynamic compensation parameter, constructing a multi-model parallel self-adaptive observer group, and carrying out the online identification of the load inertia and external disturbance through the self-adaptive observer group, a dynamic confidence evaluation mechanism is designed, disturbance dynamic compensation is realized, parameter sudden change and slow change scenes can be taken into consideration, the real-time performance of dynamic response can be ensured, compensation parameters are input into a fuzzy neural network controller, an optimal control quantity is generated in combination with a preset control target, a multi-source compensation parameter input channel is constructed, and the optimal control quantity is generated. A dynamic input weighting module is designed, fuzzy rule confidence is updated through an online strategy gradient algorithm to generate an optimized control quantity, the working condition adaptability can be enhanced, a dynamic target can be dealt with, finally, a sliding mode variable structure algorithm is adopted to carry out high-frequency buffeting suppression on the control quantity, and a final driving signal is output to a power module. And the control precision of the servo driver is improved.

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.

The present invention provides a combine harvester operating speed control system and method based on multiple operating parameter reward systems. The control system includes the following steps: inputting actual operating speed, number of harvested crop supervoxels, grass-to-grain ratio, plant height, stubble height, and swath width information into a Gaussian process regression model of a gravitational search algorithm to obtain a predicted feed rate; establishing a DQN neural network, inputting the predicted feed rate into the DQN neural network, and inputting the trash content, breakage rate, loss rate, and rotation speed as reward functions into the DQN neural network, which outputs a theoretical operating speed; inputting the difference between the actual operating speed and the theoretical operating speed, and the change in this difference, into a fuzzy neural network PID controller, and controlling the forward speed of the combine harvester based on the output of the fuzzy neural network PID controller. This invention improves harvesting quality and efficiency while reducing failure rates and alleviating operator skill requirements and workload.

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".

The application relates to the technical field of robot control, in particular to a robot control method and system based on a fuzzy neural network, which comprises the following steps: collecting the contact force, acceleration and joint angular velocity of the end of a robot in real time; predicting a future interaction force vector through a dynamic time series prediction model; calculating a prospective disturbance index; generating a dynamic reconstruction factor according to the prospective disturbance index; adjusting a preset fuzzy neural network controller to obtain a reconstructed fuzzy neural network; generating a feedforward pre-adaptation control component; generating a feedback compensation control component based on a feedback controller and a current state error; and superimposing the feedforward pre-adaptation control component and the feedback compensation control component to output a total control output; and adjusting the weight vector of the dynamic time series prediction model online by using a prediction error; the application greatly shortens the time for the system to recover from disturbance, significantly reduces the error peak value, and thus improves the stability and safety of the robot in a complex interaction task.

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 invention discloses a brushless direct current motor control method based on a sparrow search algorithm and a fuzzy control algorithm, the sparrow search algorithm is combined with the fuzzy control algorithm, parameters of a fuzzy neural network controller are updated in real time by using the sparrow search algorithm, and the sparrow search algorithm can avoid falling into local optimum and improve convergence speed. The neural network has good learning ability and is common, the control ability can be greatly improved when the neural network is combined with fuzzy control, the control precision and robustness of the brushless direct current motor can be effectively improved, and the method is suitable for complex and changeable working conditions and has wide application prospects.

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.

A multi-condition double-layer optimization control method for sewage treatment process based on task clustering belongs to the field of sewage treatment. In order to achieve multi-condition double-layer optimization control in the sewage treatment process, the present invention establishes a data-based multi-condition double-layer optimization model for the sewage treatment process, which respectively describes the relationship between the optimized set value and the double-layer optimization objectives of each condition, including the greenhouse gas emission model of the leadership optimization objective and the operating energy consumption model of the follower layer optimization objective in each condition. It studies an optimization setting method based on task clustering, solves the optimized set values of dissolved oxygen and nitrate nitrogen in the sewage treatment process, and designs a fuzzy neural network controller to complete the tracking control of the optimized set value, thereby promoting the multi-condition double-layer operation optimization control of the sewage treatment process.