258 results about "Control error" patented technology

The invention belongs to the technical field of robot control, particularly provides a force feedback teleoperation control system and method for the tail end of a mechanical arm of a quadruped robot, and aims at the key challenges that control errors are caused by communication time delay and soft obstacles are difficult to recognize in a dynamic environment. Modeling and judgment are conducted on the contact state of the tail end of the mechanical arm in advance, and feedforward control and buffer adjustment oriented to communication time delay are achieved. And meanwhile, a dynamic semantic map is constructed in combination with multi-source sensing information, and soft obstacle reasoning and path optimization are performed by fusing a tail end force sense change trend, so that the recognition and avoidance capabilities of the system in a complex and invisible obstacle environment are remarkably improved. The system has good perspectiveness, self-adaptability and high redundancy safety characteristics, is suitable for multi-task inspection operation of industrial sites such as a thermal power plant, and is especially suitable for a remote man-machine cooperative operation scene in a narrow space.

The invention relates to a vehicle queue cooperative control method fusing deep reinforcement learning communication time delay compensation, and belongs to the technical field of intelligent vehicle control. For the problems of control error accumulation and queue instability caused by communication time delay in the vehicle-road cloud cooperative system, a solution integrating deep reinforcement learning prediction and preset performance control is provided. According to the technical scheme, the method comprises the following steps: constructing a vehicle state prediction model based on a DDPG algorithm, and generating a time delay compensation amount in real time; establishing a vehicle dynamics model containing communication time delay, and designing a sliding mode controller based on a barrier function; and dynamically adjusting the control constraint in combination with an adaptive error boundary mechanism. According to the method, through closed-loop fusion of prediction compensation and dynamic constraint, under the scene of burst communication delay (100-400 ms), the queue spacing error is reduced by 62%, the speed tracking error is still kept to be smaller than 0.3 m / s, and the queue stability and the anti-jamming capability are remarkably improved.

The invention discloses a welding robot self-adaptive control system and method based on digital twinning, and relates to the field of welding robots. Comprising a multi-source data acquisition module which acquires real-time information by using a multi-mode sensor array; the multi-source data fusion module is used for fusing the collected information, fusing the data by adopting an improved self-adaptive Kalman filtering algorithm, introducing a nonlinear compensation item to carry out data compensation, and determining the posture of the welding gun; the digital twin model is used for establishing a simulation model by adopting a thermal-mechanical coupling field and determining output parameters of a welding gun in combination with welding requirements; and an adaptive control module. According to the method, the state equation and the observation equation of the welding gun are established, the process noise and the observation noise are introduced, control errors caused by the noise can be prevented on the basis of achieving closed-loop feedback control over the pose of the welding gun, meanwhile, a nonlinear compensation item is added, and the control precision of the pose of the welding gun is improved.

The invention relates to the technical field of vehicle control, in particular to a vehicle trajectory tracking and driving stability fault-tolerant control method and device, and can solve the challenges that lumped disturbance prior upper bound unknown, communication network congestion and actuator faults are difficult to cope with at the same time in the prior art to a certain extent. And the buffeting problem caused by the traditional sliding mode switching gain and the singularity problem in the solving process are solved. The method comprises the following steps: establishing a vehicle dynamics model, a control error model and a related model; designing an adaptive robust terminal sliding mode trajectory tracking controller and a stability controller; event triggering conditions are designed, and the influence of communication network congestion is reduced; and a torque optimization distribution strategy with an actuator fault factor is designed, so that the fault-tolerant capability of the system is improved. The problems of system parameter uncertainty, external disturbance, communication resource limitation, buffeting, singularity, actuator faults and the like can be effectively solved, and therefore accurate control over the intelligent vehicle is achieved under the complex driving working condition.

The invention provides a cascade AGC (Automatic Gain Control) frequency modulation regulation control optimization method, which comprises the following steps: acquiring frequency deviation and tie line power deviation in a power system in real time, calculating a regional control error ACE based on the two deviations, and decomposing the ACE into a frequency deviation component and a tie line power deviation component; calculating a ratio coefficient of the frequency deviation component in the ACE and a ratio coefficient of the tie line power deviation component in the ACE; acquiring a climbing rate and an adjustable capacity of each frequency modulation unit, and performing normalization processing on the climbing rate and the adjustable capacity; calculating the rate-capacity double weight of each unit through a dynamic weight formula in combination with the ratio coefficient, the normalized climbing rate and the normalized adjustable capacity, and distributing frequency modulation power to each unit according to the normalized weight; in the operation process, on-line self-adaptive adjustment is carried out on dynamic weight calculation and unit adjustment related parameters according to system performance indexes.

The invention discloses a power transmission line foundation pit three-dimensional laser radar equipment error self-correction control method, and relates to the technical field of power transmission line construction, and the method comprises the following steps: firstly, defining the distance from three-dimensional laser radar equipment to a target point, a horizontal included angle and a vertical included angle based on a Cartesian coordinate system; determining an initial coordinate of the target point; secondly, selecting a reference control point, removing noise and gross error points, correcting offset in the vertical direction, and finely processing point cloud data; thirdly, defining errors in the X-axis direction, the Y-axis direction and the Z-axis direction, and establishing an error correction model; and finally, based on the observation value and an error correction model, carrying out self-correction on the target coordinate, and carrying out correction through an environment feature self-adaptive error weight distribution mechanism. According to the method, closed-loop iterative optimization is performed through a multi-level data verification mechanism, the measurement precision is ensured, the operation efficiency is improved, the environmental adaptability is enhanced, and the method is suitable for power transmission line foundation pit division operation under complex terrains.

The invention relates to the technical field of electro-hydraulic servo systems, in particular to a multi-cylinder electro-hydraulic position servo system joint control method and device, and the method comprises the steps: constructing a plurality of independently operating single-cylinder electro-hydraulic position servo system models; a DDPG algorithm architecture and a reward function R are configured for each model; training an intelligent agent of the DDPG algorithm; the observation data of the intelligent agent comprises a current position, a control error of the system, an accumulated error of the system and a future trajectory of the target; the trained intelligent agent is migrated to a multi-cylinder electro-hydraulic position servo system; constructing a multi-agent cooperative control framework based on a dynamic interaction network; carrying out joint training on the multiple agents; and based on the trained intelligent agent group, completing control of the multi-cylinder electro-hydraulic position servo system. Through the joint control method and equipment, the highly nonlinear dynamic characteristic of a single-cylinder electro-hydraulic position servo system can be fully dealt with, and joint actions of synchronization, following and the like of multiple cylinders are realized.

The invention relates to the field of multi-machine operation control systems, and discloses a multi-machine operation energy-saving intelligent control system for an air blower, and the system comprises a data collection and fusion module which is used for collecting and fusing operation parameters and environment parameters of the air blower; the prediction modeling module constructs a system load prediction model based on historical data; the multi-agent decision module generates a control strategy according to the current state; the collaborative optimization module carries out non-cooperative strategy coordination among the multiple air blowers; and the parameter updating module updates the prediction model and the control strategy through the control error and training feedback. All the modules work cooperatively, and efficient energy-saving control and dynamic optimization of the system are achieved. The multi-agent collaborative optimization technology is adopted, the overall energy efficiency of the system is guaranteed, meanwhile, the load fluctuation of a single air blower is reduced to the maximum extent, the service life of equipment is prolonged, the problem of disordered scheduling when the load fluctuation is large in a traditional control method is solved, and therefore energy consumption is greatly reduced.

The invention provides a mobile hydraulic mechanical arm control system and method. The system comprises a central control system, a mobile platform control system and a hydraulic mechanical arm control system, wherein the mobile platform control system and the hydraulic mechanical arm control system are in communication connection with the central control system; the central control system is configured with a decision model based on a depth deterministic policy gradient algorithm (DDPG); the mobile platform control system adopts a PID feedback control algorithm to control the movement speed of the mobile platform; and the hydraulic mechanical arm control system controls the motion trail and action of the hydraulic mechanical arm according to the reasoning result of the decision model. According to the method, the depth deterministic strategy gradient algorithm (DDPG) is introduced into control over the throttling valve and the reversing valve of the hydraulic mechanical arm, the action strategy is dynamically optimized through Actor and Critic networks, the trajectory tracking precision and the dynamic response capacity of the mechanical arm under the complex load are remarkably improved, and control errors caused by hydraulic oil compressibility and mechanical friction are reduced.

The invention provides an intelligent all-in-one machine with a body, a system and an operation method, and belongs to the technical field of industrial automation and robot control systems.The all-in-one machine comprises an automation system IDE module, an automation system driving module, a robot body driving module and a function supply module, the function supply module is used for providing a data acquisition function, a simulation task execution function, a model training function and a model reasoning function with time synchronization for the robot body driving module according to a control command which can be executed by the robot; the intelligent all-in-one machine solves the problem of time synchronization between the all-in-one machine and the robot, between the acquisition module and the robot and between the acquisition module and the multi-frequency sensor data, and ensures that the time synchronization between the acquisition module and the robot and between the acquisition module and the multi-frequency sensor data is realized in the sensor data acquisition, model reasoning and task execution processes through accurate timestamp management and a multi-level time synchronization mechanism. Millisecond-level time alignment of data of all the modules can be achieved, and control errors caused by time drift and data delay are effectively avoided.

The invention discloses an energy-saving power distribution control system and method for a multi-load power distribution cabinet, and relates to the technical field of power system automation control, and the system comprises a main control module, a multi-path power distribution execution module, a load feature extraction module, a priority control module, and a strategy judgment and control execution module. The main control module, the multi-path power distribution execution module, the load feature extraction module, the priority control module and the strategy judgment and control execution module are electrically connected with one another; wherein the main control module is used for centrally managing operation data from a plurality of load branches, executing load state identification, controlling strategy calculation and issuing a power-off decision; the multi-path power distribution execution module comprises a plurality of intelligent circuit break control units corresponding to different load branches; according to the invention, the problems of extensive load perception, single energy-saving strategy, high control error rate and weak linkage protection capability in the prior art are solved.

The invention relates to the technical field of electrical automation, in particular to an electrical automatic control system and control method.The electrical automatic control system comprises a hardware part and a software part, and the hardware part comprises an intelligent control module (1), a data preprocessing unit used for preprocessing collected original data; (2) a deep learning network construction unit for designing and constructing a deep learning network architecture suitable for a specific control task; (3) a training and optimizing unit is used for minimizing control errors and improving control precision; (4) a self-learning and evolution unit which continuously adjusts and optimizes a control strategy according to real-time operation data and feedback; the parameter adjusting and strategy updating unit automatically adjusts control parameters and strategies, when facing complex and changeable network environments or service requirements, the system can conduct resource scheduling and management more flexibly and efficiently so as to overcome the defects that in the prior art, fixed control logic and manual intervention are relied on, and the system can not conduct resource scheduling and management more flexibly and efficiently. And complex and changeable working conditions and continuously changing control requirements are difficult to deal with.

The invention discloses a double-magnetic-conductance control method and system of an AGV, the AGV, equipment and a medium, the centers of two magnetic sensors are used as control target points, forward kinematics equations of each wheel and the control target points are constructed, the current speed of a vehicle body and the current speed deflection angles of the two control target points are obtained through inverse kinematics solution, and the control target points of the AGV are obtained. And calculating a predicted deviation correction angle of each control target point by combining a track deviation measurement result of the magnetic sensor, and finally performing forward kinematics solution to obtain a target speed and a target speed deflection angle of each wheel. According to the method, the positions of the two magnetic sensors serve as control targets, a front control point and a rear control point can be well considered by adopting a double-control-target-point angle deviation correction mode, rapid deviation correction of the AGV can be achieved, the deviation correction response speed is greatly increased, the method can be suitable for complex and variable paths, various variable paths can be well followed, and the method is suitable for large-scale popularization and application. And the path following precision is improved, and the control error is greatly reduced.

The invention relates to the technical field of electro-hydraulic servo systems, in particular to a control method and system for a single-cylinder electro-hydraulic position servo system based on reinforcement learning, and the method comprises the steps: building a single-cylinder electro-hydraulic position servo system model; constructing a DDPG algorithm architecture, and defining a reward function R; constructing a target future trajectory prediction model based on dynamic recursive prediction and multi-modal information fusion, and generating a target future trajectory; training an intelligent agent of the DDPG algorithm; the observation data of the intelligent agent comprises a current position, a control error of the system, an accumulated error of the system and a future trajectory of the target; and based on the trained intelligent agent, the output action is controlled to obtain continuous action signals within a set threshold range, and control over the single-cylinder electro-hydraulic position servo system is completed. Through the control method and the control system, the highly nonlinear dynamic characteristic in the single-cylinder electro-hydraulic position servo system can be fully dealt with, and the control precision, the stability and the robustness of the intelligent agent under the complex working condition are improved.

The invention discloses a bidirectional six-switch buck-boost topology circuit and a control method thereof, and belongs to the technical field of power electronics. According to the scheme, two switch tubes and one inductor are additionally arranged on the basis of a four-switch buck-boost circuit, and an input negative electrode and an output negative electrode are separated through the additionally arranged switch tubes and inductor, so that the problem that when different input source outputs are connected in parallel, a negative bus is directly connected to form circulating current is effectively solved; a loop does not need to judge an input and output voltage relation, the duty ratio of a power switch tube is automatically adjusted directly according to the set voltage, current and energy transfer direction, and the boost-buck state of the topology is determined according to the calculation result of the duty ratio, so that the control on the energy transfer direction is realized; the on / off of the synchronous rectifier tube is determined by the magnitude of the output current. By adopting the scheme of the embodiment of the invention, the circulating current problem caused by multi-path input difference can be solved, the topology control difficulty is reduced, and the control error-tolerant rate is improved.

The invention discloses a rope-driven mechanical arm trajectory tracking control method based on adaptive time delay estimation, and belongs to the technical field of flexible mechanical arm control. The method comprises the following steps: firstly, establishing an n-degree-of-freedom mechanical arm kinetic equation; further, determining a self-adaptive time delay estimation item; then, according to the adaptive time delay estimation item and the sliding mode surface, determining an adaptive time delay estimation controller; and finally, trajectory tracking control is carried out on the mechanical arm based on a self-adaptive time delay estimation controller. According to the method, by arranging the self-adaptive time delay estimation controller, the control performance is improved, the control error is reduced, and the method has good self-adaptive capacity and can adapt to different working conditions of the mechanical arm.

The invention relates to the technical field of error compensation, and discloses a multi-device cooperative control error compensation method based on a field master control system, which comprises the following steps: step 1, taking stations, reservoir areas and device positions as nodes, and establishing a node stream, a relation and communication; step 2, generating a token by an arrival node and an departure node, and judging a process phase and a downstream task; step 3, obtaining a time deviation according to a real arrival and plan difference, and combining a time window and a phase to determine release; step 4, sequentially testing for standby application when the first choice is unavailable or the second choice is unavailable, and determining a scheduling direction according to material detection when the first choice is unavailable; 5, avoiding and returning are carried out before and after the main task, and the execution permission and the blocking body position are obtained; step 6, according to the reference position, the tolerance and the stepping amount correction, the tolerance is reached or the scheduling is changed; and step 7, integrating parameter classification abnormity, executing relay control and outputting a result. According to the invention, global unified scheduling and error compensation of multiple devices in a complex operation environment are realized, and the continuity and stability of system operation are ensured.

The invention belongs to the field of control, relates to a resin production waste gas recovery control method and system, and aims to solve the technical problems that in the prior art, working condition division cannot be matched with the real state of a multivariable system, and PID parameters are balanced among a plurality of control targets. The neurons are divided into stable working condition neurons, transition working condition neurons and disturbance working condition neurons; s2, obtaining a Pareto optimal solution set composed of a PID parameter group, wherein the Pareto optimal solution set comprises an ITAE optimal solution, a control output change rate optimal solution and an equilibrium solution; s3, determining an optimal matching unit of the potential feature vector in the self-organizing mapping network; s4, when the optimal matching unit is a stable working condition neuron and the absolute value of the current control error is greater than a high-order threshold value, selecting an ITAE optimal solution; when the absolute value of the current control error is smaller than a low-level threshold value, the optimal solution of the control output change rate is selected; and a balance solution is selected between the two. Accurate identification and division of the operation state of the resin production waste gas recovery process are realized.

The present disclosure belongs to the technical field of robot control, and specifically provides a four-legged robot mechanical arm end force feedback teleoperation control system and method. In view of the key challenges of control errors caused by communication time delay and soft obstacles difficult to identify in a dynamic environment, the system introduces a predictive contact response mechanism, pre-models and judges the end contact state of the mechanical arm, realizes feedforward control and buffer adjustment for communication time delay. At the same time, a dynamic semantic map is constructed combined with multi-source sensing information, and the soft obstacle inference and path optimization are carried out by fusing the end force sense change trend, so as to significantly improve the identification and avoidance ability of the system in a complex and invisible obstacle environment. The present application has good foresight, adaptability and high redundancy safety characteristics, and is suitable for multi-task inspection operation in industrial sites such as thermal power plants, and is particularly suitable for remote human-machine collaborative operation scenes in narrow spaces.

The invention relates to the technical field of centrifugal machine control, in particular to a centrifugal machine temperature control parameter optimization method and system, and the method comprises the steps: collecting a plurality of parameters of a centrifugal machine, and constructing a sample data set; an XGBoost stage classification model is trained based on the sample data set, the working condition probability of the centrifugal machine is output, the chamber temperature and the refrigeration power of the centrifugal machine at the next moment are preset, and a temperature precision penalty coefficient, a temperature change rate risk coefficient and an energy consumption penalty coefficient are calculated; the weights of the temperature precision penalty coefficient, the temperature change rate risk coefficient and the energy consumption penalty coefficient are dynamically adjusted based on the proportions of the steady speed, the accelerated speed and the decelerated speed in the real-time working condition, an objective function is constructed, and a comprehensive control error is calculated; and multiple groups of historical data most similar to the working condition at the current moment are selected as reference data, all the reference data are traversed, the compressor rotating speed corresponding to the reference data with the minimum comprehensive control error is selected as the optimal rotating speed, the optimal rotating speed is issued to the compressor for execution, and the temperature control precision of the centrifugal machine is improved.

The present invention discloses a control optimization method and system for an intelligent phase-changing switch, which relates to the field of smart grid technology. The method includes: traversing multiple key nodes of the intelligent phase-changing switch to deploy sensors and determine a sensor device group; periodically collecting data on the intelligent phase-changing switch through the sensor device group to generate an electrical operation parameter set; performing error calculation on the electrical operation parameter set to generate a control error data set; constructing a predictive control channel to generate multiple control signals; introducing control constraints to formulate a control strategy; executing the control strategy to perform error feedback, dynamically adjusting the control strategy, and generating a control optimization strategy. The present invention solves the technical problems in the prior art that the phase-changing switch is insufficient in accuracy and adaptability, resulting in unstable power quality and low system operation efficiency, and achieves the technical effect of improving control accuracy and the operation efficiency of the power system and enhancing adaptability.

The invention discloses an intelligent control method for equipment for chemical production, and relates to the technical field of intelligent control, and the method comprises the following steps: collecting process variable data, an automatic control instruction, an operator intervention behavior and equipment health state information; based on the collected process variable data, constructing a causal association relationship model between the operator intervention behavior and the equipment health state; identifying abnormal points by combining sliding time window prediction and a dynamic tolerance threshold value, and removing polluted data; constructing a multivariable causal diagram based on process variable deviation, and distinguishing control error paths caused by equipment degradation and intervention; deploying a multi-version control strategy model in the control system, and evaluating and screening an optimal strategy; an operator is guided to record the intervention purpose and mode on a human-computer interface; continuously evaluating the performance of the control strategy, and triggering a model retraining and replacing mechanism; the control logic is always close to the real state.

The invention discloses an improved active-disturbance-rejection control method based on error and input delay compensation, relates to the technical field of industrial control, and aims to solve the problem of insufficient input delay and control precision of a high-order inertial system. The method comprises the following steps of: modeling a controlled object into a dynamic system with inertia characteristics, constructing an input delay compensation link and an error-based active-disturbance-rejection link, inputting a control error and a compensation value into an extended state observer (ESO), estimating an error related state quantity and a disturbance tracking value in real time, calculating a control quantity through a state error feedback control law, and compensating the control quantity; and closed-loop accurate adjustment is realized. The invention further comprises an improved second-order active-disturbance-rejection control scheme to adapt to higher precision requirements. The control structure is simplified, control signals and system output are synchronously controlled, the anti-interference capability and the tracking performance are considered, and the method can be widely applied to high-order inertial industrial system control in the fields of chemical engineering, thermal power generation and the like.

The application provides a fluid pulsation control method, device and system based on a time delay parameter model. The method comprises the following steps: obtaining a hysteresis phase parameter of a control valve at a target frequency of fluid pulsation based on the target frequency; determining a model parameter of a secondary channel estimation model based on the hysteresis phase parameter of the control valve at the target frequency and a basic delay of the secondary channel; obtaining a reference signal based on the target frequency of the fluid pulsation, inputting the reference signal into the secondary channel estimation model, and obtaining a reference signal filtered by the secondary channel estimation model; and adjusting an adaptive parameter based on a reference control error of the fluid pulsation and the reference signal filtered by the secondary channel estimation model, and realizing active control. The method can ensure that an adaptive fluid pulsation control algorithm is applicable to stable control in a time-varying scene.

The invention discloses an energy storage cluster cooperative control method and apparatus, a terminal device and a storage medium. The method comprises the steps of obtaining operation information of a power system to be subjected to frequency modulation; wherein the operation information comprises frequency deviation data; according to the operation information, generating a regional control error signal of the power system to be subjected to frequency modulation; calculating frequency modulation total power based on the regional control error signal and the frequency deviation data; performing primary power distribution on each energy storage cluster in the power system to be subjected to frequency modulation according to the frequency modulation total power to obtain single energy storage cluster frequency modulation power of each energy storage cluster; and performing power secondary distribution in each energy storage cluster based on the single energy storage cluster frequency modulation power corresponding to each energy storage cluster to obtain a power adjustment scheme of the power system to be subjected to frequency modulation. According to the invention, the control precision of the to-be-frequency-modulated power system in automatic power generation control is improved.

The present application discloses a control method and a power conversion system for a power converter, wherein the control method for the power converter comprises: obtaining an output voltage reference value of the power converter; obtaining an actual output voltage value of the power converter at the current moment; determining the voltage interval in which the actual output voltage value is located according to a preset voltage interval set; obtaining a control error of the current control cycle according to a preset algorithm, an output voltage reference value, and an actual output voltage value corresponding to the voltage interval; controlling the power converter using a PID control algorithm according to the control error; and returning to the step of obtaining the actual output voltage value of the power converter at the current moment until the actual output voltage value meets the preset accuracy requirement. The present application can make the output voltage of the power converter smoothly transition to its reference value when the PID controller parameter configuration is poor, which is beneficial to reducing the voltage spike of the switch tube inside the power converter and improving the robustness of the power converter control system.

The application provides a power system frequency anti-interference regulation method and device under certainty network multiple attacks, and the method comprises the following steps: obtaining multiple groups of frequency regulation related parameters of a controlled area; inputting regional frequency deviation data and regional power deviation data in each group of frequency regulation related parameters into an adaptive network attack detection system to obtain estimated regional control error data; the adaptive network attack detection system utilizes sample data under multiple network attacks collected in advance to train a neural fuzzy system physical model; according to actual regional control error data and estimated regional control error data of the multiple groups of frequency regulation related parameters, root mean square error is calculated; according to the root mean square error and a preset range, a relieved regional control error is determined from the actual regional control error and the estimated regional error. The application comprehensively considers multiple attacks, can quickly detect new attacks, and improves power adjustment efficiency.

This application belongs to the field of control and relates to a control method and system for recovering waste gas from resin production. It addresses the technical problems in existing technologies, such as the inability of operating condition division to match the true state of a multivariable system and the need for PID parameters to balance multiple control objectives. The control method includes the following steps: S1, dividing neurons into stable operating condition neurons, transitional operating condition neurons, and disturbance operating condition neurons; S2, obtaining a Pareto optimal solution set composed of PID parameter groups, including the ITAE optimal solution, the control output rate of change optimal solution, and the equilibrium solution; S3, determining the best matching unit for the latent eigenvectors in a self-organizing map network; S4, when the best matching unit is a stable operating condition neuron, selecting the ITAE optimal solution when the absolute value of the current control error is greater than a high threshold; selecting the control output rate of change optimal solution when the absolute value of the current control error is less than a low threshold; and selecting the equilibrium solution when it is between the two. This achieves accurate identification and division of the operating state of the resin production waste gas recovery process.

The invention relates to the technical field of knock control, and discloses an intelligent control system and method for engine knock and preignition, and the method comprises the following steps: collecting engine knock signal data and preignition signal data, and carrying out the signal processing of the knock signal data and the preignition signal data, the method comprises the following steps: generating knock intensity data and preignition intensity data, and during engine knock and preignition identification, setting multi-stage identification thresholds including a basic threshold, a dynamic self-learning threshold and a temperature compensation threshold, and updating the thresholds in real time by utilizing a self-learning algorithm, so that the engine knock and preignition identification accuracy is improved. The method can effectively adapt to sensor signal characteristic change caused by engine wear, fuel quality difference and environment change, solves the problems of misjudgment and missed judgment caused by a fixed recognition threshold value, ensures the accuracy and reliability of knock and preignition recognition, reduces control errors, and improves the reliability of knock and preignition recognition. And the dynamic property, the economical efficiency and the driving smoothness of the engine are effectively improved.

The invention discloses an automatic rail transfer control system and method for a knitting machine, and belongs to the field of automatic control of textile machinery. Comprising an orbital transfer unit arranged between an outer ring drive plate track and an inner ring drive plate track and a control module for controlling the orbital transfer unit to work. The method comprises the following steps: coarsely adjusting to be near a target position through a servo motor incremental encoder, and switching a dial encoder for fine adjustment after stopping; after the driving plate is finely adjusted in place, the conversion plate is driven to exchange the spindle seat; and based on positioning module encoders of two adjacent orbital transfer units in the same circle, sensor null drift influence is identified. Multi-mode weaving can be achieved, the orbital transfer control error is smaller than or equal to + / -0.05 degrees, and the method is suitable for intelligent production of the safety emergency rope of the fire-fighting rope.