86 results about "Control error" patented technology

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 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 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.

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.

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 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 relates to the technical field of steer-by-wire, in particular to a steer-by-wire mechanism two-way encoder redundancy control and mechanical clearance compensation method which is realized based on a steer-by-wire mechanism, and a steering controller executes the following steps: S1, adjusting the steer-by-wire mechanism to a mechanical zero position, and confirming the validity of a two-way encoder signal; s2, zero calibration of the two-way encoder is carried out; and S3, generating two paths of track position variables as steering control references. S4, selecting a smaller value in absolute values of the difference values of the two paths of detection angles and the corresponding track position variables as a mechanical clearance compensation reference; and S5, the steering motor is controlled, so that the mechanical clearance of the steer-by-wire mechanism is eliminated. The method has the advantages that the double-path redundant parallel design is adopted, the double-path SENT encoders correspond to the two paths of track position variables one to one, double verification of angle detection and control variables is achieved, control errors caused by deviation of a single-path component are effectively avoided, and the steering precision is further improved.

Apparatuses and techniques for controlling error reporting for usage-based-disturbance mitigation are described. In an example aspect, an adaptable method of controlling error reporting is provided so as to account for an uncertainty regarding a level of intermittent faults associated with a memory device. An error-reporting control circuit provides a mechanism to control how often errors are reported to a host device. In particular, the error-reporting control circuit can filter unnecessary noise associated with intermittent faults based on a parameter that is settable (e.g., programmable or changeable). In this manner, the error-reporting control circuit can provide further confidence at the host device that reported errors are associated with permanent defects. Furthermore, the parameter can be appropriately set for the given memory device based on its design and / or architecture once silicon data is available.

The application provides a kind of energy storage interval layer monitoring device and modular energy storage station monitoring system, through the state data acquisition of energy storage unit instrument equipment by several slave control modules, and transmission to master control module for summary, the state data after summary is transmitted to host computer module, then combined with the operation instruction given by user, control signal is generated, and is transmitted to master control module, slave control module is controlled by master control module, finally, the operation of instrument equipment is controlled by slave control module.The master control module and slave control module can control the operation of instrument equipment according to their own control logic, can handle the change condition of conventional energy storage unit, compared with the traditional energy storage monitoring system, the application can process data for each energy storage unit, reduce the data processing amount in host computer module, thereby improve the transmission efficiency of data.Through the operation of user, the control error of energy storage unit is reduced, and the control accuracy of energy storage unit is improved.

The application discloses an aircraft brain neural network decoupling control method, which comprises the following steps: acquiring multi-dimensional information of each channel of an aircraft related to different postures; constructing a decoupling neural network, including a feature coding layer, a dynamic decoupling layer and a control action mapping layer, wherein the input is a multi-channel control error fusion signal, and the output is independent control instructions of each channel after decoupling; the feature coding layer firstly outputs a weighted feature vector by using an attention mechanism module as a whole, and then further optimizes the weighted feature vector by using a contrast representation learning module to generate a strengthened feature vector; the decoupling neural network is connected with a controlled object model, and the decoupling neural network is trained online; and a trained aircraft posture decoupling network controller is used for decoupling control of the aircraft. The decoupling process is optimized by using orthogonality and low rank constraints, so that the model complexity is effectively reduced, and the redundant dimension interference is effectively inhibited.

The invention discloses a particle swarm neural network PID parameter optimization system based on an FPGA, and relates to the technical field of automatic control, the system comprises a particle swarm optimization module, a BP neural network module, a PID control module and an FPGA processing module, the particle swarm optimization module carries out global optimization on an initial weight and a threshold value of a BP neural network, and the PID control module carries out PID parameter optimization on the initial weight and the threshold value of the BP neural network; the BP neural network module receives optimized network parameters as initial parameters, performs forward propagation calculation and back propagation training according to control error signals and outputs PID control parameters, the PID control module calculates control output quantity according to system errors, and the FPGA processing module executes operation tasks of all the modules in a hardware parallel mode. According to the method, the particle swarm optimization is combined with the BP neural network, so that the global search capability of the PID control algorithm is enhanced, the problem that the BP neural network falls into a local minimum value is avoided, and the calculation speed and the real-time performance of the system are improved.

The invention provides a man-machine sharing control-oriented dynamic permission allocation system and method. The method comprises the following steps of: establishing a vehicle longitudinal-transverse coupling dynamic model; the robust security constraint is generated through an environment robust control barrier function, and the environment robust control barrier function compensates the influence of sensor measurement uncertainty through a residual error correction term; the performance constraint is generated by controlling a Lyapunov function; based on the robust security constraint and the performance constraint, the change rate of the control permission of the automatic system is used as a core decision variable, an optimization problem is constructed, the robust security constraint is set as a hard constraint, the performance constraint is set as a soft constraint, and the optimization objective is to minimize the permission fluctuation degree and the control error at the same time; and solving the optimization problem to obtain a current control authority weight, and carrying out weighted fusion on the driver control instruction and the automatic system control instruction to generate a final vehicle control instruction.

The invention provides a hemispherical resonator gyroscope phase lag error compensation method and system. The method comprises the following steps: establishing a phase lag error model; sequentially applying virtual rotation control forces with the same amplitude and opposite directions to the hemispherical resonator gyroscope, respectively acquiring actual orthogonal control forces under the two conditions, and calculating the difference between the actual orthogonal control forces under the two conditions; an actual phase lag error is obtained based on the established phase lag error model according to the difference identification of the actual orthogonal control force; when various control forces are applied, the phase of the control force modulation signal is changed according to the actual phase lag error, and compensation of the phase lag error is achieved. By applying the technical scheme provided by the invention, the technical problem that the control error of the gyroscope is influenced by the phase lag error of the hemispherical resonator gyroscope in the prior art is solved.

This invention discloses a dual friction parameter identification and adaptive synchronous control method for a multi-axis electro-hydraulic system, comprising: constructing a dynamic model of a hydraulic leveling system including a LuGre friction model; using a genetic algorithm to perform offline optimization identification of the friction parameters of the LuGre friction model to obtain initial estimated parameters; based on the initial estimated parameters, designing an adaptive parameter adjustment law based on control error to fine-tune the friction parameters in the LuGre friction model online in real time, forming a dual friction parameter identification framework consisting of offline identification and online adaptation; using the dual identification framework, designing an integrated adaptive feedforward integral sliding mode controller, introducing the online fine-tuned friction parameters into the sliding mode control law for feedforward compensation, and ensuring that synchronous control and online estimation of friction parameters are performed synchronously; based on the dynamic model, distributing the virtual torque in the overdrive coupling system, and designing an inner-loop voltage controller to achieve pressure control under constant back pressure.

This invention relates to the field of intelligent control technology for industrial processes, specifically disclosing a reinforcement learning control method for wet leaching processes based on feedback enhancement and operating condition guidance. By defining extended state variables to establish an interactive environment conforming to Markov properties, the theoretical adaptability of the reinforcement learning method is ensured. By introducing control error, control error integral, and control error derivative into the state space to construct a feedback-enhanced state space, cross-scenario generalization control performance is improved. By designing a comprehensive reward function covering steady-state performance, dynamic performance, and dynamic-steady-state balance performance, an effective balance between the dynamic and steady-state performance requirements of the control system is achieved. Through an action range selection mechanism guided by operating condition knowledge, multi-operating-condition control is achieved with a single reinforcement learning agent, avoiding the high training costs and fragmented operating-condition data problems associated with integrating multiple reinforcement learning controllers.

The invention provides a register management method and electronic equipment, and belongs to the technical field of mass flow meters. According to the method, register access feature data of an original register of the mass flow meter in an operation state is collected, so that the access mode deviation degree can be calculated according to the register access feature data, and the access mode deviation degree is used for quantifying the difference degree between the actual access behavior of the register and an expected mode; furthermore, when the access mode deviation degree is greater than a first dynamic threshold value, a synchronization strategy can be triggered and adjusted, that is, for an original register with increased access frequency, the synchronization frequency is increased, and the data volume proportion of incremental synchronization is increased, so that key data can be updated in time, and a control error caused by delay is avoided; for the original register with the reduced access frequency, the synchronization frequency is reduced, and the data volume proportion of increment synchronization is reduced; or the synchronization mode is switched from incremental synchronization to full-amount synchronization, so that the invalid transmission overhead can be reduced, and the bandwidth utilization rate can be optimized.

The invention belongs to the field of power system load frequency control, and relates to a cooperative frequency regulation system and method for a multi-zone-area interconnected power system. The method comprises the following steps: acquiring an area control error of each area according to inter-area tie line power deviation and system frequency deviation; constructing a memory output signal based on the historical data of the transformer area state; based on the memory output signal and the system state, utilizing a preset sliding mode surface function and solving according to a sliding mode control theory to obtain an equivalent control signal; and according to the equivalent control signal and a preset participation weight, generating and issuing a control instruction aiming at the traditional generator set, the electric vehicle cluster and the battery energy storage system so as to cooperatively adjust the system frequency. According to the system, cooperative adjustment of various heterogeneous resources in a multi-zone-area interconnection system is realized, the frequency adjustment effect is improved, the adjustment potential evaluation precision is improved, the accuracy and stability of frequency adjustment can be ensured, and the new energy consumption capability and operation flexibility of a power grid are effectively improved.

This invention relates to an intelligent control method based on PID-DMC and big data modeling. It calculates the control error between the target crude oil temperature and the actual measured crude oil temperature, as well as the rate of change of crude oil temperature. It determines whether the control error and the rate of change of temperature meet the requirements. If so, a recommended control quantity model is established to calculate the recommended control value of the gas flow rate, which is then input into the PID controller to control the crude oil temperature. Otherwise, by analyzing historical data, a model conforming to the step response is found and input into the DMC controller to control the crude oil temperature. The method acquires the temperature curve, determines the temperature inflection point, and establishes the heating start time and temperature control strategy. Control ends when the actual measured crude oil temperature reaches and remains stably at the target crude oil temperature within the required time. This invention solves the problem of untimely temperature control during crude oil transmission, avoids the energy waste and operational risks associated with traditional manual adjustment of the output temperature, and effectively solves the technical problems existing in the temperature control of current heating furnaces.

This invention discloses an intelligent control method for equipment used in chemical production, relating to the field of intelligent control technology. The method includes the following steps: collecting process variable data, automatic control commands, operator intervention behaviors, and equipment health status information; constructing a causal relationship model between operator intervention behaviors and equipment health status based on the collected process variable data; identifying anomalies and removing contamination data by combining sliding time window prediction and dynamic tolerance thresholds; constructing a multivariate causal graph based on process variable deviations to distinguish between control error paths caused by equipment degradation and intervention; deploying multi-version control strategy models in the control system, evaluating and selecting the optimal strategy; guiding the operator to record the purpose and method of intervention on the human-machine interface; continuously evaluating the performance of the control strategy and triggering a model retraining and replacement mechanism; this invention ensures that the control logic always closely approximates the real state.

The invention relates to the technical field of feed processing, and discloses an intelligent control method and system for feed processing, and the method comprises the steps: obtaining the operation state data of a feed in a processing process; constructing a mixing uniformity comparison table by taking the feed mixing uniformity degree as a table head and the current fluctuation degree as a watchband; counting a process deviation value between an expected target parameter of the historical processing case library and the operation state data to obtain a control error parameter; encoding the control error parameter into a control parameter signal, and integrating the control parameter signal and the mixing uniformity comparison table into processing regulation and control data; based on machining operation parameters of similar machining scenes in a historical machining case library, the machining regulation and control data are corrected in a self-adaptive mode, and target control parameters are obtained; transmitting the real-time state data, the operation state data and the target control parameters of the target control parameters in the execution process to a historical processing case library to obtain a processing control report; the feed processing intelligent control efficiency can be improved.

This invention discloses a method and apparatus for predicting and controlling error propagation in computational fluid dynamics (CFD), belonging to the field of CFD numerical simulation technology. Addressing the problems of existing technologies' difficulty in identifying error propagation paths in advance and the lack of proactive control measures, this invention constructs an error propagation state characterization model, representing the propagation behavior of numerical errors under physical mechanisms such as convection, diffusion, and reaction as a combination of multiple error propagation modes; it predicts the error propagation velocity, direction, and propagation length by combining local flow field physical parameters; it constructs an error propagation risk criterion to identify potential error growth regions; and it implements adaptive strategies such as local time step adjustment, adaptive mesh refinement, enhanced numerical scheme stability, and rigid control of the reaction zone. This invention can identify and proactively control unstable regions before significant error amplification, significantly improving the stability and reliability of numerical simulations of complex flows and combustion.

The present invention provides a power converter and a control method for the power converter that can suppress errors in tracking control caused by the transmission characteristics of the output target. [Solution] This power converter 100 comprises a power conversion unit 10 that converts and outputs the input power, and a control unit 11 that controls the power conversion by the power conversion unit 10. The control unit 11 includes a command value generation unit 20, a phase correction unit 40, and a tracking signal output unit 50. The phase correction unit 40 calculates a command value I based on a phase correction amount δ calculated based on at least one of the output current Ia and a transfer function generated based on a model of the AC system 250. * Correct the phase.

The application discloses an underwater wellhead distributed control system and a control method, relates to the technical field of underwater wellhead control, and comprises the following steps: acquiring real-time monitoring data sets of each control node of an underwater wellhead network, wherein the real-time monitoring data sets comprise wellhead pressure time series data, fluid component concentration data and the like; performing multi-source information fusion verification on the data sets to generate a state characteristic matrix; calling a wellhead cooperative control model for analysis and decision-making to generate a cooperative control instruction set; constructing an underwater space-time synchronization network based on underwater acoustic positioning data and the relative position relationship between nodes to distribute the control instructions; and collecting feedback data sets in the execution process, comparing the feedback data sets with preset targets to generate a control error vector, and performing online adaptive adjustment on the model. The method can realize accurate and synchronous distribution of instructions and dynamic optimization of the model, adapt to complex underwater environments, and guarantee stable operation of the wellhead.