192 results about "Neural network controller" patented technology

The invention provides an intelligent temperature adjusting method and system for an injection molding mold. The method comprises the steps that real-time multi-source sensor data flow is generated by collecting temperature data and pressure data of all areas of the injection molding mold; performing feature extraction on the real-time multi-source sensor data flow to obtain temperature field distribution features; a temperature field distribution prediction model is obtained through training based on the temperature field distribution characteristics and a differential private Bayesian optimization algorithm, and then a mold temperature prediction result is output through the model; performing feature fusion on the mold temperature prediction result and the pressure data, constructing input features of a neural network controller, training based on the input features and a positive definite curvature learning method to obtain the neural network controller, and generating a power control signal of a heater and a flow control instruction of a water cooling system through the neural network controller, and based on the control signal and the instruction, the temperature field of the mold is dynamically controlled through the adjustable clearance compensation mechanism. According to the invention, the precision and response speed of temperature adjustment are improved.

The invention relates to the field of power plant control, discloses a virtual power plant control method, system and equipment based on a neural network, and is used for solving the core problems of high data dependence, low topology safety and difficulty in multi-scale collaboration in traditional virtual power plant control. According to the virtual power plant control method based on the neural network, a correction instruction set, a joint estimation value and a topology constraint matrix are input into a neural network controller, and a cooperative control signal is output through singular perturbation decoupling of a fast-varying subsystem and a slow-varying subsystem. And the cooperative control signal is issued to the distributed power supply inverter, the energy storage converter and the intelligent switch, and meanwhile, an execution result is monitored in real time and fed back to the phase space reconstruction module, so that closed-loop control is formed. By constructing the Lyapunov candidate function and calculating the virtual damping coefficient, the transient stability, real-time persistent homologous analysis and topology self-healing instruction generation of the system are enhanced, and the self-healing capability and the fault-resistant capability of the system are improved.

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.

A method and system for robot skill learning for high precision assembly tasks employing a compliance controller. A reinforcement learning (RL) controller is pre-trained in an offline mode using human demonstration data, where several repetitions of the demonstration are performed while collecting state and action data for each repetition. The demonstration data is used to pre-train a neural network in the RL controller, with no interaction of the RL controller with the compliance controller / robot system. Following pre-training, the RL controller is moved to online production where it is coupled to the compliance controller / robot system in a self-learning mode. During self-learning, the neural network-based RL controller uses action, state and reward data to continue learning correlations between states and effective actions. Co-training is provided as needed during self-learning, where a human operator overrides the RL controller actions to ensure successful assembly operations, which improves the learned performance of the RL controller.

The self-adaptive switching control method for the unmanned surface vehicle comprises the following steps: establishing a mathematical model of the unmanned surface vehicle, and determining a control target of the unmanned surface vehicle; designing a virtual control law and a first Lyapunov alternative function for controlling the unmanned surface vehicle based on a backstepping method; designing a second Lyapunov alternative function, establishing an adaptive neural network controller model and a robust controller model, and designing control input of the unmanned surface vehicle switching control system; designing updating laws of an adaptive neural network controller and a robust controller, and designing a third Lyapunov function to perform stability analysis on the system; and testing and simulating the unmanned surface vehicle system in a simulation environment by using the adjusted control strategy. According to the control method, on the premise of ensuring the stability of the system, the path tracking error of the unmanned surface vehicle is reduced, and the energy consumption of the controller is reduced.

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.

An adaptive impedance-based multi-mobile-robot collaborative transportation control method. Each mobile robot estimates the actual pose and ideal pose of a reference point and the first and second derivatives of the ideal pose by means of two finite-time fully-distributed observers, respectively; and then, on the basis of the estimated poses of the reference point, the pose of an end-effector of a mechanical arm, and closed-chain constraints for collaborative transportation, an ideal trajectory of the end-effector of the mobile robot, and an estimated value of a pose deviation between the end-effector of the mobile robot and the reference point are obtained. An adaptive impedance system of each mobile robot is interconnected with a virtual energy tank, and the energy tank is used to guide the updating of impedance parameters, thereby ensuring the passivity of the entire collaborative adaptive impedance system. To process unknown system dynamics of mobile robots, an asymptotic tracking adaptive neural network controller is designed using a neural network, thereby asymptotically achieving an ideal adaptive impedance relationship. The operational accuracy of multi-robot collaborative transportation systems is improved while ensuring safe collaboration.

The invention relates to an equipment automatic control technology, and discloses an automatic control method and device for textile manufacturing equipment and electronic equipment, and the method comprises the steps: obtaining historical parameter data of a textile loom, and building a textile tension mechanical model according to the historical parameter data; generating a tension compensation curve according to the spinning tension mechanical model and the historical parameter data; generating a fuzzy neural network controller by using the historical parameter data and the tension compensation curve; and performing dynamic parameter adjustment on the pre-acquired real-time parameters of the textile loom by using a fuzzy neural network controller and the tension compensation curve so as to realize automatic control of the textile manufacturing equipment. The tension control capability of the textile manufacturing equipment can be improved.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

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 a tractor clutch test bench cooperative control method based on MPC-BP neural network PID, and belongs to the technical field of tractor clutch performance testing. The method specifically comprises the steps of establishing an enhanced mathematical model of a tractor clutch test bed system; an MPC prediction controller is designed, and an optimal control sequence is generated through rolling optimization of an objective function; a BP neural network PID controller is improved; a multi-unit cooperative control strategy is designed, MPC prediction and BP neural network PID control are combined, and cooperative adjustment of the torque of the loading unit, the rotating speed of the driving unit and the displacement of the clutch separation mechanism unit is achieved; a self-adaptive adjustment and load compensation mechanism is introduced, and the adaptability of the system to different working conditions and clutch models is improved through online model correction, heat fading modeling and fault diagnosis. The control precision, the dynamic response speed and the robustness of the test bed are remarkably improved, and the test bed can be widely applied to research, development and performance testing of the tractor clutch.

The invention discloses an asymptotic tracking control method for a mobile double-flexible mechanical arm network. The method comprises the following steps: constructing the mobile double-flexible mechanical arm network; the uncertainty of a leader is considered, and a self-adaptive distributed switching observer is constructed; a servo system is introduced, transverse displacement is generated, and a tracking error model is constructed; an adaptive neural network controller is constructed by considering unknown gain faults and parameter uncertainty; on the basis of a distributed switching observer and a neural network controller, asymptotic consistency tracking control over a mobile double-flexible-mechanical-arm network is achieved. According to the method, asymptotic fault-tolerant consistency tracking control of the mobile double-flexible mechanical arm network can be effectively realized under heterogeneous linear leader and denial of service attacks, and the problems of unknown gain faults and unknown parameters are solved by utilizing a self-adaptive method and a neural network technology; the moving position of the moving double-flexible mechanical arm and the angle position of the two flexible mechanical arms reach the specified transient performance, and asymptotic consistency tracking is achieved.

A system for controlling the lateral movement of an autonomous vehicle (101) is disclosed, comprising: a trajectory planner (102) configured to generate a target trajectory for the vehicle (101); and a trajectory controller (104) configured to receive the target trajectory from the trajectory planner (102) and output a steering control signal for controlling the lateral movement of the vehicle (101). The trajectory controller (104) includes a control circuit (104a), wherein the control circuit (104) comprises an integrated adaptive PID neural network controller (124).The integrated adaptive PID neural network controller (124) comprises: a PID controller (116) that receives an input signal dependent on a target lane position parameter of the target trajectory and is configured to minimize an error value that specifies the difference between the actual trajectory of the vehicle (101) and the input signal dependent on the target lane position parameter, wherein the PID controller (116) is further configured to receive an input signal dependent on a target heading angle parameter of the target trajectory and is configured to minimize an error value that specifies the difference between the actual trajectory of the vehicle (101) and the input signal dependent on the target heading angle parameter; and a neural network (118) that is configured to determine the P-gain, I-gain, and D-gain terms used by the PID controller (116).The integrated adaptive PID neural network controller (124) is configured to use the P and I gain terms provided by the neural network (118) to control the lane position of the vehicle (101) and the D gain term provided by the neural network (118) to control the heading angle of the vehicle (101), whereby the P, I and D gain terms used by the PID controller (116) are provided by the same neural network (118).

The invention relates to a PMSM control method based on a BP neural network and a superspiral sliding mode. Comprising the following steps: (1) constructing a mathematical model of a permanent magnet synchronous motor (PMSM); (2) on the basis of the mathematical model in the step (1), constructing a BP neural network PI controller which takes an expected value omega *, an actual value omega and an error e as inputs, takes a proportionality coefficient Kp and an integral coefficient Ki of the PI controller as outputs, and contains seven nodes in a hidden layer; the rotating speed ring continuously updates the weight value through the BP neural network so as to adjust the values of Kp and Ki in real time; (3) based on the mathematical model in the step (1), constructing an STA current loop controller for super-spiral sliding mode and feedforward decoupling control; and (4) PMSM control is realized based on the BP neural network PI controller in the step (2) and the STA current loop controller established in the step (3). The method has smaller overshoot and anti-interference capability, and has very positive significance for improving the control performance of the permanent magnet synchronous motor.

The invention discloses a novel device and method for intelligently disassembling a waste photovoltaic silicon cell panel. The device comprises two layers of transmission rollers and a conveying belt penetrating through the two layers of transmission rollers. The main control board is integrated with a fuzzy controller and a PID (Proportion Integration Differentiation) controller, a hierarchical decision algorithm and a BP (Back Propagation) neural network are deployed on the main control board, the PID controller is connected with the fuzzy controller, and the BP neural network is used for dynamically adjusting PID parameters of the PID controller; the support is fixedly provided with a six-axis servo motor, a back plate collecting bin, an air cooling fan and a copper solder strip absorbing device, the six-axis servo motor is fixedly connected with a vacuum suction cup array, the air cooling fan is fixedly provided with an infrared imager, and the support is provided with a double-layer heat cutter head. A resistance heating module and an electromagnetic induction heating module are nested on the outer surface of the double-layer hot cutter head, and a pressure sensor, a laser sensor and a temperature sensor which are fixedly connected with the main control board are fixedly arranged on the outer surface of the double-layer hot cutter head. According to the invention, the waste photovoltaic silicon cell panel can be intelligently and accurately segmented, the dependence on manpower is reduced, and high-quality recovery of the silicon cell panel is realized.

The invention provides a motion control method of a coupled output limited Mecanum wheel trolley based on a neural network and event triggering, a storage medium, equipment and a computer program product. The motion control method comprises the following steps: adopting a conversion matrix of Mecanum wheels; establishing a dynamic model of the Mecanum wheel trolley; decoupling the coupling output limitation, and converting the coupling output limitation into time-varying non-coupling limitation; improving the design of a sliding mode surface by adopting an error conversion function and a barrier function, and constructing a sliding mode controller of a trolley with limited coupling output; constructing a neural network controller; introducing an event triggering mechanism, and constructing a neural network sliding mode controller for the coupled output limited trolley based on the neural network and sliding mode control; and verifying the stability of the designed controller. According to the invention, the trajectory tracking problem of the Mecanum wheel trolley with limited coupling output based on the neural network and event triggering is solved.

The invention belongs to the technical field of lower limb rehabilitation, and discloses a man-machine coupling system intelligent cooperative control method and device based on deterministic learning. Establishing a state-space equation of the lower limb exoskeleton robot, defining a tracking error, and designing a self-adaptive neural network controller of the lower limb exoskeleton robot; defining an expected position state vector and a real position state vector of the walking robot, solving a position state vector error, and designing a self-adaptive neural network controller of the walking robot; the method comprises the following steps: constructing a time-varying linear matrix, verifying the exponential stable convergence characteristic of the time-varying linear matrix under a continuous excitation condition on the basis of an adaptive neural network controller, storing nonlinear dynamic knowledge of a man-machine coupling system, and constructing a learning controller by using the learned knowledge. According to the method, while the unknown dynamic state of the man-machine coupling system is accurately modeled, the continuous excitation condition is ensured, so that accurate and stable trajectory tracking control is realized.

The invention relates to an electrostatic dust collection control method and device, a medium, electronic equipment and a program product, and relates to the field of electrostatic dust collection, the method comprises the steps that real-time data in the electrostatic dust collection process are obtained, the real-time data comprise real-time environment information and operation parameters of an electrostatic dust collector, and the real-time environment information of the electrostatic dust collector is obtained based on the real-time data; operation of the electrostatic dust collector is controlled through various controllers, and the controllers comprise at least one of a fuzzy controller, a neural network controller and a robust controller. According to the technical scheme, based on the real-time data in the electrostatic dust collection process, the fuzzy controller is used for automatically controlling the operation of the electrostatic dust collector, the neural network is used for optimizing the control process, the robust controller is used for reducing the influence of external interference, the control precision of electrostatic dust collection can be improved, and therefore the dust collection efficiency and accuracy are improved; the labor cost is reduced.

The invention provides a collaborative robot man-machine interaction and anti-collision control method and device based on admittance, and relates to the technical field of robot intelligent control. The method comprises the following steps: acquiring control input force; according to the control input force, constructing a relationship between a state variable and control input in the man-machine interaction system; according to the relation, an expected admittance model and a soft saturation function are adopted, and a reference trajectory of the robot end effector is generated; according to the reference trajectory, an Actor-Critic algorithm is adopted, and a reinforcement learning neural network controller based on admittance control is designed; according to the reinforcement learning neural network controller based on admittance control, a control strategy in the man-machine interaction system is adjusted, and an anti-collision strategy in the man-machine interaction system is obtained. According to the invention, the safety and stability of the man-machine interaction system are improved.

The invention relates to the technical field of micro-plastic treatment, in particular to an enhanced coagulation dosing method and system based on tap water plant micro-plastic treatment, and the method comprises the following steps: collecting a first water quality parameter of raw water at a water inlet of a tap water plant; inputting the first water quality parameter into a BP neural network feedforward controller, and predicting the adding amount of a flocculating agent; a micro-plastic abundance value of a water outlet and a corresponding abundance set value are used as input linguistic variables, and a fuzzy PID feedback controller is used for controlling the adding amount of a flocculating agent; and detecting the turbidity of the outlet of the sedimentation tank, inputting the second water quality parameter into the post-dosing BP neural network controller when the turbidity value of the water outlet does not meet the set condition, and outputting the adding amount of the flocculating agent in the water outlet pipe of the sedimentation tank. By adopting an intelligent feedforward-feedback composite dosing and secondary dosing control strategy, quick response and accurate dosing can be realized, the medicine consumption and the labor cost are reduced, and micro-plastics and turbidity in tap water are effectively controlled.

The invention provides a rotor unmanned aerial vehicle bidirectional thrust control method and device based on deep reinforcement learning, and the method comprises the steps: S01, constructing an unmanned aerial vehicle bidirectional thrust dynamics model which comprises an unmanned aerial vehicle dynamics model and a motor-blade model for achieving the bidirectional thrust control; s02, using a deep reinforcement learning model to construct a neural network controller for controlling the action of the unmanned aerial vehicle based on the unmanned aerial vehicle bidirectional thrust dynamical model, the input of the neural network controller being the difference between the current state and the target state of the unmanned aerial vehicle, and the output being the expected thrust of each motor of the unmanned aerial vehicle; and step S03, training the neural network controller, and using the trained neural network controller to control the unmanned aerial vehicle, so that the unmanned aerial vehicle stably hovers to a target state. The method has the advantages of being high in control precision, good in stability and high in adaptability, and stable hovering of the unmanned aerial vehicle in severe states such as a large posture, a large speed and a large angular speed is achieved.

In the field of magnetic suspension bearing control, the invention provides a magnetic suspension bearing controller construction method based on feature dominant control, and the method comprises the steps: constructing a magnetic suspension bearing mathematical model; mathematical models of the displacement sensor and the power amplifier are constructed; carrying out kinematic mechanics analysis on the magnetic suspension bearing; a PID controller is constructed; constructing an improved PINN neural network; constructing a model prediction controller based on reinforcement learning; constructing a leading controller; factor coefficient selection and adjustment of the three control algorithms are based on a mathematical model between a stator and rotor gap and electromagnetic force in the five-degree-of-freedom hybrid magnetic suspension bearing and kinematic mechanics characteristics of a magnetic suspension rotor. The magnetic suspension bearing is stabilized and corrected by means of a feature leading controller composed of a PID controller, a PINN neural network controller and a model prediction controller based on reinforcement learning, and a more accurate and stable control effect can be achieved.

The invention discloses a position-limited master-slave heterogeneous bilateral teleoperation control method based on an improved wave variable, a storage medium and a robot, and the method comprises the steps: building an improved wave variable algorithm framework, and adding a compensation wave to a slave input wave; a master robot task space reference trajectory and mapping of positions in a master-slave robot task space are constructed, and a master-slave robot joint space reference trajectory is solved based on a closed-loop inverse kinematics algorithm; based on a human operator, a far-end environment and master-slave robot characteristics, a combined robot dynamic model in a joint space is constructed; and aiming at the combined robot dynamic model, designing an adaptive neural network controller under limited joint positions based on a state transfer function. By means of the method, high-precision safety control over teleoperation of the master-slave heterogeneous robot with the limited position is effectively achieved, the stability and transparency of a teleoperation system are improved based on the designed improved wave variable algorithm, and a new safety operation method is provided for teleoperation control of the robot.

The present invention relates to a machine learning-based MMC model predictive control method and system. First, data is collected using an MPC-MMC simulation platform and preprocessed, and then neural network training is performed to obtain a neural network-MPC controller. To improve the neural network training efficiency, random forest is used to optimize the initial weight threshold of the neural network. Finally, a random forest-neural network-MPC controller is obtained to simulate the MPC controller. The results show that RF-NN-MPC is superior to NN-MPC in terms of learning efficiency and learning accuracy; while maintaining good control effects, MPC-MMC is not restricted by the number of sub-modules, and the online calculation amount is always 1 time. The calculation amount is greatly reduced, which is suitable for engineering applications.

The invention belongs to the field of oilfield separated layer water injection. The invention discloses an improved particle swarm optimization neural network PID (Proportion Integration Differentiation) separated layer water injection flow control method, which specifically comprises the following steps of: collecting underground water injection layer section flow through a flow meter, uploading the underground water injection layer section flow to a neural network PID controller through a data processing module, proposing a group fitness variance strategy, setting a variance threshold value, and if the variance is smaller than the threshold value, controlling the flow rate of the underground water injection layer section. In order to drive particles to continue searching, a new weight updating formula is provided in combination with a tanh function, a neural network PID is optimized after a particle swarm algorithm is improved, a signal is sent to control a direct current motor to drive a transmission mechanism to enable a water nozzle to reach a proper opening degree, and accurate control over the water injection flow is achieved. According to the method, the optimal solution of PID is approached by improving the global optimization ability of the particle swarm algorithm and the rapid solution of the BP neural network, the control precision and robustness are improved, and the waste of resources is reduced.

The invention relates to the technical field of industrial automation control, in particular to a boiler combustion stability intelligent control method based on a self-adaptive neural network, and the method comprises the steps: collecting multi-source heterogeneous data of a boiler system, the multi-source heterogeneous data comprises a high-frequency vibration data flow and a low-frequency process data flow, carrying out space-time alignment preprocessing on the multi-source heterogeneous data; and calculating a self-adaptive penalty factor based on the current load characteristics and the vibration energy, and performing variational mode decomposition on the high-frequency vibration data stream by using the self-adaptive penalty factor to obtain a combustion characteristic component. According to the method, the gain of the neural network controller is dynamically adjusted through the comprehensive stability risk index, smooth switching of a control strategy between steady-state fine adjustment and fault strong control is achieved, and therefore the fast inhibition capacity for combustion instability is remarkably improved while the steady-state precision of a combustion system is guaranteed.

The invention provides a ship path planning and tracking control method based on an improved differential evolution algorithm, and the method comprises the steps: constructing a collision risk degree model, and improving the calculation precision of the collision risk degree through combining with parameters of a quaternary ship domain optimization model; determining a fitness function Fitness to evaluate the quality of the path points according to the constraints of the ship collision risk degree, the voyage, the steering angle, the international marine collision avoidance rule and the optimal collision avoidance distance; a self-adaptive cross factor dynamic adjustment strategy based on individual fitness and population statistical indexes is introduced, candidate path points are screened according to an improved differential evolution algorithm, and collision with obstacles and dynamic ships is avoided; and establishing a ship course motion mathematical model, taking the reference path as an input instruction of a ship motion control subsystem, and tracking a ship planning path by using an adaptive neural network controller. According to the invention, the ship collision avoidance path planning is more in line with the actual navigation, and the safety of the collision avoidance path and the accuracy of tracking control are improved.

The invention provides a rail train traction and brake control method and system, and the method comprises the steps: firstly monitoring the wheel rotation speed and motor torque of a rail train, inputting a state observer with pole configuration to estimate an adhesion coefficient in real time, and combining the creep speed at the current moment to obtain a traction and brake coefficient; calculating the stick-slip slope of the adhesion characteristic curve by using an adhesion coefficient-creep speed empirical formula; the method comprises the following steps: setting a target creep speed by adopting a steepest gradient method, inputting the target creep speed and a current creep speed into a neural network controller, outputting a rail train correction torque, combining the rail train correction torque with a current actual braking torque to obtain a target torque, and controlling a rail train motor through a DTC method so that the actual torque is consistent with the target torque. And the motor torque and the wheel speed are input into the state observer to form closed-loop control. The adhesion coefficient is estimated and optimized in real time through the control method, the train traction and braking efficiency is remarkably improved, and a safer and more efficient solution is provided for modern rail transit.

The invention provides a fuzzy neural network PID (Proportion Integration Differentiation) pressure control method based on an improved black wing plinary algorithm, and belongs to the technical field of engineering. The technical problems of strong nonlinearity, dynamic lag and insufficient anti-interference capability in an ozone dissolving tank pipeline pressure control system are solved. According to the technical scheme, the method comprises the following steps that S1, pressure modeling is conducted on a water outlet pipeline of an ozone water tank; s2, improving the design of a black-wing optimization algorithm IBKA; s3, designing a fuzzy neural network PID controller; and S4, performing simulation experiment and performance verification on the pressure control system. The method has the beneficial effects that the dynamic response speed and the steady-state precision of pressure control are improved by improving an optimization algorithm and adjusting online parameters, and the requirement for pressure stability under the complex working condition is met.

The invention relates to the technical field of distributed rendering synchronization, and discloses a distributed rendering node display picture synchronization method. The method comprises the following steps: acquiring real-time display picture data and timestamp information of a plurality of nodes, extracting picture display delay characteristics of each node and determining a delay change period through time domain statistical analysis of timestamps, and further constructing a synchronous fluctuation degree of each node; calculating the current overall network interference degree by combining the relevance of the synchronization fluctuation degree in the historical data and the historical mean value, and analyzing the delay growth mode of the display timestamp in the current continuous time point sequence to obtain the synchronization lag trend degree; and combining the two to obtain a synchronization offset index, calculating a feedback synchronization adjustment amount by using the variation of the synchronization offset index, the current synchronization control intensity and a preset adjustment increment, and finally performing dynamic synchronization adjustment on a display image of the distributed rendering node based on the adjustment amount and an actual image synchronization measurement value by applying a neural network controller.