174 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 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 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 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 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 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 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.

The application relates to the field of multi-machine operation control systems, and discloses a multi-machine operation energy-saving intelligent control system of a blower, which comprises the following modules: a data acquisition and fusion module for acquiring and fusing operation parameters and environmental parameters of the blower; a prediction modeling module for constructing a system load prediction model based on historical data; a multi-agent decision module for generating a control strategy according to a current state; a collaborative optimization module for coordinating non-cooperative strategies among multiple blowers; and a parameter updating module for updating the prediction model and the control strategy through control errors and training feedback. The modules work collaboratively to realize efficient energy-saving control and dynamic optimization of the system. The application adopts a multi-agent collaborative optimization technology, thereby guaranteeing the overall energy efficiency of the system, minimizing the load fluctuation of a single blower, prolonging the service life of the equipment, solving the problem of disordered scheduling of the traditional control method under large load fluctuation, and greatly reducing energy consumption.

The invention relates to the technical field of image communication, in particular to an active tracking control method of a high-precision PTZ camera. The method comprises the following steps: acquiring a Rocker Cmd object; the speed values of all the shafts are judged based on a Rocker Cmd object, and corrected rocker input values are obtained; performing protocol mapping on the corrected rocker input value, and caching the corrected rocker input value to obtain an instruction queue; removing invalid stop instructions in the instruction queue to obtain an optimized continuous control instruction sequence; and performing acquisition time and direction judgment on the optimized continuous control instruction sequence to obtain a corrected control instruction set. Through intelligent processing, protocol mapping and caching, logic analysis and state feedback of rocker input signals, high-precision, smooth, reliable and durable closed-loop control of the PTZ camera is realized, and the control error-tolerant rate is remarkably improved.

This invention discloses an intelligent constant temperature and pressure control system for a cleanroom, comprising: a data acquisition module for deploying sensors to collect data and transmit it to a control unit; a data processing module for calibrating, filtering, and synchronizing data, and inputting it into an MLP network to extract features; a coupling prediction module for establishing a prediction model, calculating deviations, and generating a control error vector; a process control module for executing control strategies based on the error vector and determining equipment operating parameters; an execution control module for transmitting adjustment commands and driving synchronous adjustment of the equipment; and a closed-loop feedback module for collecting adjusted data and feeding it back to the control unit to achieve closed-loop regulation. This invention, through an intelligent control system, adjusts the temperature, pressure, and airflow of the cleanroom in real time, improving environmental control accuracy, energy efficiency, and stability, and ensuring the efficient and reliable operation of the experimental environment.

The useful solution refers to a pump control circuit in water tank systems or water filtration devices with water tanks, including a power supply block, a logic processing block, and an output block, arranged on a compact printed circuit board. It uses relay elements to switch the load, and also includes a power supply protection block, noise immunity, and processing logic to limit control errors when the water level signal or water request signal is fluctuating or constantly changing.

The invention discloses a grid-connected converter system impedance control method based on impedance compensation and a related device. The method comprises the following steps: acquiring a port voltage signal, an output current signal and a capacitance current signal of a hardware filter circuit; respectively inputting the port voltage signal into a virtual impedance controller and a voltage feed-forward compensator to obtain an impedance current instruction signal and a voltage feed-forward compensation signal; inputting the impedance current instruction signal into a current feed-forward compensator to obtain a current feed-forward compensation signal; superposing the voltage feed-forward compensation signal and the current feed-forward compensation signal to obtain an impedance feed-forward compensation signal; superposing the impedance current instruction signal and the external current instruction signal, subtracting the output current signal to obtain a current control error signal, and inputting the current control error signal into a current controller to obtain a modulation voltage instruction signal; inputting the capacitance current signal into a damping controller to obtain a damping feedback signal; superposing the impedance feedforward compensation signal, the modulation voltage instruction signal and the damping feedback signal to obtain a superposed signal; and modulating the superposed signal to obtain a switch driving signal for controlling a switch tube device in the hardware switch network. According to the invention, the influence of the equivalent impedance of the hardware filter circuit and the time delay introduced in the sampling and modulation process on the impedance control effect can be effectively eliminated, so that the frequency range of impedance control is expanded, and the precision of impedance control is improved.

The application discloses a double-magnetic-guide control method and system of an AGV, the AGV, equipment and a medium, which takes the center of two magnetic sensors as a control target point, constructs a positive kinematics equation of each wheel and the control target point, solves the current speed of the vehicle body and the current speed deviation angle of the two control target points through inverse kinematics, combines the trajectory deviation measurement result of the magnetic sensor to calculate the predicted correction angle of each control target point, and finally solves the target speed and the target speed deviation angle of each wheel through positive kinematics. The method takes the positions of the two magnetic sensors as control targets, adopts the double-control-target-point angle correction mode, can well consider the front and rear control points, can not only realize fast correction of the AGV, greatly improves the correction response speed, but also can be applied to complex and changeable paths, can well follow various changeable paths, improves the path following precision, and greatly reduces the control error.

The application provides a multi-region power system load frequency control method, system and storage medium, and belongs to the technical field of automatic control of power systems. The method comprises the following steps: for the case that the key state of the power system is not measurable, reconstructing each regional system through a state observer and collecting the output operation parameters of each region in real time; calculating the load frequency control error of each region based on the estimated value of the unmeasurable state and the output operation parameters, and constructing an event triggering mechanism to determine whether the change amount of the error relative to the last triggering time is greater than a set threshold value, and triggering the control only when the condition is met; and the controller calculates the output adjustment amount of each regional unit according to the constraints of the unit adjustment capacity and the adjustment rate characteristics, and then issues and executes the output adjustment amount. The application reduces the communication burden through the event triggering mechanism, solves the problem of unmeasurable key state through state estimation, and ensures the executability of the instruction through the adjustment characteristic constraint, so that high-precision and high-reliability load frequency stability control is realized under the premise of saving resources.

This invention discloses a high-precision continuous interval logic control method supporting independent and controllable intelligent manufacturing, belonging to the fields of intelligent manufacturing industrial control, precision machining, domestically produced industrial chips, and AI intelligent control technology. Based on the mathematically fundamental continuous interval theory and STE industrial control-specific coding, this invention integrates domestically produced industrial control chips and precision control models to construct a full-process industrial control system, setting high-specification performance thresholds such as nanometer-level control precision, zero error accumulation, and low latency. By establishing a continuous interval mathematical model, calibrating fundamental parameters, replacing discrete control with continuous logic, issuing STE-coded instructions, and implementing real-time error correction, the method eliminates the accumulation of discrete control errors, achieving high-precision control in high-end precision manufacturing that is entirely domestically produced and free from reliance on foreign technologies. It is adaptable to various precision manufacturing scenarios, contributing to the independent upgrading of intelligent manufacturing and filling gaps in related domestic underlying control technologies.

The embodiment of the invention provides an excavator auxiliary pipeline control method and device, equipment, a medium and a product. The method comprises the steps that engine load parameters and hydraulic system feedback pressure parameters of the excavator are obtained, fuzzy calculation is carried out based on the engine load parameters and the hydraulic system feedback pressure parameters, a target current value is generated, and current output of an auxiliary pipeline proportional electromagnetic valve of an auxiliary pipeline is adjusted according to the target current value. The method is used for achieving the effect of reducing flow loss caused by control errors.

The application provides a vehicle control interface inspection method and device, electronic equipment and a storage medium; applied to an automatic driving vehicle, the automatic driving vehicle comprises an automatic driving system, the automatic driving system comprises at least one sub-control system, each sub-control system corresponds to a control interface, and the method comprises the following steps: at least one control request of each sub-control system is sent to an automatic driving vehicle to be inspected, and a response result corresponding to each control request is received, each control request comprises a target control result, and the response result is used for indicating the result of the sub-control system responding to the control request and controlling through the corresponding control interface; according to the target control result in each control request and the corresponding response result, the control error corresponding to each control request is calculated, and the target control error corresponding to each control request is obtained; whether all control interfaces are normal is determined according to the target control errors corresponding to all control requests.

This invention discloses a method for generating sickle bend control strategies based on deep reinforcement learning, comprising: collecting intermediate slab data during the hot roughing and rolling process of strip steel as sample data and performing data preprocessing; establishing and training a sickle bend amount prediction model based on the XGBoost algorithm to build an intelligent agent environment for generating the sickle bend control strategy model; establishing a sickle bend control strategy generation model based on a deep deterministic policy gradient algorithm; training the sickle bend control strategy generation model and saving the model parameters; inputting the intermediate slab data from the hot roughing and rolling process of strip steel into the trained sickle bend control strategy generation model, and outputting the intermediate slab sickle bend adjustment strategy. This invention, through automatic control, can effectively avoid control errors caused by current reliance on human experience, reduce manual intervention, and lower labor intensity. Simultaneously, controlling the straightness of the roughing slab provides a strong guarantee for the stability of finishing rolling production.

The invention discloses a neural network adaptive feedforward method and system for realizing cable constant tension control, and relates to the field of industrial control, and the method comprises the steps: S1, obtaining the latest historical tension data and real-time tension data of a cable; s2, a pre-trained NARX neural network model outputs a cable tension prediction value at a future moment; s3, taking the cable tension prediction value as a feedforward compensation amount; s4, inputting the system control error into a BP neural network to obtain a self-adaptively set PID control parameter; s5, calculating to obtain a feedback control quantity by adopting the self-adaptively set PID control parameters; s6, superposing the feed-forward compensation quantity and the feedback control quantity; according to the method, future tension changes are accurately predicted through the NARX neural network and serve as feed-forward signals, advanced suppression of disturbance is achieved, PID parameters are adjusted online through the BP neural network, and a controller can automatically adapt to changes of complex working conditions such as different sea conditions and loads.