254 results about "Self-tuning" patented technology

The invention provides a parameter self-tuning control method for a permanent magnet synchronous motor, and relates to the technical field of synchronous motor control, and the method comprises the steps: firstly, monitoring a dynamic operation data set, including stator current, rotor flux linkage, rotating speed fluctuation quantity and load change characteristic parameters, of the permanent magnet synchronous motor in real time; secondly, feature relevance mapping is carried out based on multiple parameter combinations in the dynamic operation data set, initial parameter adjustment sets are generated, and each initial parameter adjustment combination comprises the mapping relation between the motor operation state and the target response index; dividing a plurality of parameter adjustment subsets according to the parameter correlation degree, configuring a dynamic error range, extracting a positive adjustment parameter from the first subset corresponding to the first operation state, and extracting a negative adjustment parameter from the second subset with the low correlation degree; and finally, training a dynamic parameter adjustment model by utilizing the positive adjustment parameters and the negative adjustment parameters, generating an optimized real-time parameter setting instruction set, realizing intelligent self-setting of the parameters of the permanent magnet synchronous motor, and improving the running performance of the motor.

The invention discloses a servo system speed loop parameter self-tuning method based on digital-analog linkage. The method comprises the following steps: establishing an open-loop transfer function of a speed loop of a servo system; parameters of a speed ring are initialized, a same-frequency and same-amplitude speed instruction is sent to a motor to drive a load to operate, and the total rotation inertia of the motor is calculated; a mapping relation equation is determined, and parameters of a speed PI controller are obtained; discretizing the parameters of the speed PI controller by adopting a bilinear method to obtain a discrete proportional gain and a discrete integral gain; determining constraint conditions of the servo system and calculating an evaluation function value according to the ITAE performance evaluation function; obtaining optimal control parameters by adopting a coordinate rotation method; corresponding discrete proportional gain and discrete integral gain are calculated according to the optimal control parameters to serve as parameters of a speed PI controller to complete parameter setting, and load operation is achieved according to a speed instruction. According to the method, the setting process is more efficient, the calculation parameters are more accurate, the manual debugging time can be greatly shortened, and the universality is good.

The invention discloses an integrated distributed energy storage UPS (Uninterrupted Power Supply) system, which relates to the technical field of distributed energy storage and comprises the steps of S1, data real-time acquisition, S2, parameter normalization processing, S3, dynamic weight calculation, S4, load instruction dynamic distribution, S5, multi-target coordination control execution and S6, state feedback and parameter self-tuning. According to the method, core parameters such as the charge state, the health degree, the temperature and the internal resistance of each energy storage unit are evaluated in real time, a weight function with a time-varying characteristic is constructed, the output proportion of each unit can be dynamically adjusted, and intelligent load distribution matched with the equipment aging state and the environment working condition is achieved; by introducing a health degree attenuation factor, the system can quantify the nonlinear degradation characteristics of the battery pack, preferentially dispatches the unit with an excellent state to bear the base load, and provides an intermittent rest period for the high-loss unit at the same time.

The invention provides a magnesium alloy electric drive main shell mold machining precision control method and system, and belongs to the field of precision numerical control machining. The method comprises the steps that dynamic milling force, key point temperature and position signals of all shafts are synchronously collected through a multi-source sensor; a recursive least square method is adopted to identify the dynamic rigidity and damping coefficient of the tool-workpiece system on line, and the thermal deformation drift distance is calculated in combination with a temperature signal; predicting a three-dimensional deformation error vector in real time based on the dynamic model, thermal deformation and geometric errors, and constructing a space compensation field; macro motion compensation is achieved by modifying numerical control program coordinate points, and meanwhile high-frequency micro-amplitude compensation is conducted through a spindle tail end fast tool servo system. And establishing an adaptive disturbance observer to perform online estimation on unmodeled disturbance and feed-forward compensation, and realizing online self-tuning of model parameters in combination with in-situ measurement feedback. Real-time sensing, full-band dynamic compensation and closed-loop self-adaptive control of multi-source errors are achieved, and the machining precision and long-term stability of the mold are remarkably improved.

The invention relates to the technical field of data processing, and discloses an automatic control platform operation data management method. The method comprises the steps of collecting industrial equipment data through a field bus, storing the industrial equipment data into a database in a classified mode and establishing an index table, extracting a control loop to construct a DCS model, generating a process abnormality diagnosis report, calculating control parameters by using a self-tuning algorithm and outputting a control instruction, optimizing a process formula based on an implementation effect and constructing a recommendation knowledge base. According to the intelligent data management method, the process abnormality is accurately diagnosed, the control parameters are automatically optimized, and the process knowledge is continuously accumulated, so that the control precision, the diagnosis efficiency, the parameter optimization capability and the knowledge reuse level of the industrial production process are improved.

The invention relates to the technical field of controller parameter tuning, and discloses a PID controller parameter self-tuning method based on reinforcement learning, comprising a PID controller parameter self-tuning system, the PID controller parameter self-tuning system comprises an environment module, a reinforcement learning agent module and a controller module, the environment module is in signal connection with the controller module through the reinforcement learning agent module, and the environment module comprises a controlled object, a sensor and an actuator. According to the PID controller parameter self-tuning method based on reinforcement learning, an environment module and a reinforcement learning agent module which are provided with a PID controller parameter self-tuning system are arranged, and a controlled object, a sensor and an actuator of the environment module can be utilized, so that data of the PID controller parameter self-tuning system can be conveniently measured and collected; and by utilizing the state, action and reward of the reinforcement learning agent module, the robustness of the system can be conveniently improved by utilizing reinforcement learning, and the function of improving the parameter self-tuning control performance of the PID controller is achieved.

The invention is suitable for the technical field of control systems, and provides a servo motor speed ring self-tuning method and device, and the method comprises the steps: collecting an error value, a steady-state error and steady-state time in the speed adjustment process of a servo motor in real time, and calculating a fluctuation index, thereby achieving the self-tuning of the speed ring of the servo motor. And the adjustment effect of the PID controller parameters can be comprehensively evaluated. When the volatility index is smaller than a first threshold value, a linear function is adopted to adjust the initial PID parameter, a first target control parameter is obtained, and therefore the adjusting performance is optimized; and when the volatility index does not reach the standard, performing adaptive adjustment by using a fuzzy strategy to obtain a second target control parameter. By dynamically adjusting PID parameters, external interference or load change can be effectively handled, the problems of over-adjustment, under-adjustment and the like are reduced, and the stability and response speed of the system are improved. The manual debugging workload is reduced, especially in a complex application scene, the system efficiency and stability are remarkably improved, the debugging cost is reduced, and the deployment efficiency is improved.

The invention discloses a fuel cell rapid cold start method considering initial water content optimization, and belongs to the technical field of fuel cell automobile optimization control. According to the fuel cell rapid cold start method considering initial water content optimization, a closed-loop model prediction controller is firstly designed to accurately control the current of a galvanic pile, and then the initial film water content is optimized by using a particle swarm optimization algorithm, so that the rapidity of a cold start system is improved. The method can be structurally divided into the following parts: establishment of a cold start model, design of a parameter self-tuning model prediction controller and optimization of the optimal initial membrane water content. In the cold start process, the current output of the electric pile is adjusted in real time according to different environment temperatures and membrane water contents, controller parameters are adjusted in a self-adaptive mode, the freezing rate and the heating rate of water produced by the electric pile can be effectively balanced, and the performance of a cold start system is improved.

The invention provides a robust control automatic setting method for a constant-pressure water supply system, and the method comprises the steps: obtaining a system working condition in real time, and extracting multi-dimensional state parameters including real-time flow, pressure deviation, current deviation and power deviation; the current load working condition is judged according to the pressure deviation, disturbance characteristics are analyzed by integrating all parameters, and the disturbance grade and type are determined. On the basis, robust control parameters are automatically set in combination with real-time working conditions, load conditions and disturbance characteristics of the system. And finally, performing multi-stage cooperative control on the system by using the set parameters, so that the constant-pressure water supply system is automatically set. According to the robust control automatic setting method for the constant-pressure water supply system, load working conditions and disturbance characteristics are recognized based on multi-dimensional state parameters, robust control parameters are self-set in a targeted mode, and automatic setting of the constant-pressure water supply system is achieved through multi-stage cooperative control. Therefore, the intelligent level of the constant-pressure water supply system and the adaptability in different operation environments are effectively improved.

The invention discloses a shimmy suppression method for a four-wheel independent steer-by-wire system. The shimmy suppression method comprises the following steps: establishing a wheel shimmy kinetic model of the four-wheel independent steer-by-wire system; designing a barrier Lyapunov function, and converting the swing amplitude constraint into a state constraint condition; designing a backstepping sliding mode controller parameter self-tuning strategy based on the shimmy frequency; designing a back-stepping sliding mode controller with self-adaptive swing amplitude-frequency characteristics based on the state constraint condition; based on a designed backstepping sliding mode controller, active self-adaptive control is carried out on shimmy phenomena of wheels with different frequencies and amplitudes, and then suppression of the shimmy phenomenon of the four-wheel independent steer-by-wire system is completed. According to the amplitude-frequency characteristics of the shimmy phenomenon occurring in the operation process of the four-wheel independent steer-by-wire system, the shimmy phenomenon of different frequencies and amplitudes can be restrained in a self-adaptive mode, then the reliable operation capacity of the four-wheel independent steer-by-wire system is improved, and vehicle instability caused by excessive shimmy of wheels is avoided.

The invention discloses a method for optimizing the AGC load regulation rate of a power station, and the method comprises the steps: building a digital twinborn body of a unit, building a boiler thermal positive and negative balance model, a fuel heat value prediction model and a self-adaptive control parameter library based on the historical operation data of a target unit, and receiving and processing the operation data of the unit in real time; dynamically predicting a boiler theoretical calorific value demand corresponding to the AGC instruction by using a long-short-term memory recurrent neural network, and calculating a deviation between an actual calorific value and a theoretical calorific value; when the deviation exceeds a preset threshold value, multi-mode cooperative compensation control of feed-forward compensation, parameter self-tuning and energy closed-loop calibration is implemented in parallel; the AGC instruction response time is controlled within 50-55 seconds, and model self-updating is achieved through online tracking. According to the method, the defects that traditional thermal power station fuel calorific value calibration is slow in response, a single control model is difficult to deal with complex problems, AGC response time is long and the like are overcome, the power station AGC load regulation rate and thermal power generating unit frequency modulation performance are remarkably improved, and power grid frequency stability is guaranteed.

The invention relates to the technical field of intelligent gate valve control, in particular to an intelligent induction type automatic adjusting gate valve system.An analysis module of a calibration unit in the intelligent induction type automatic adjusting gate valve system recognizes signal distortion by comparing historical working condition databases, extracts the viscosity change trend and deduces the impurity accumulation rate; a dynamic compensation factor is generated by an offset calculation model subjected to multi-source transfer learning training, the signal amplitude and phase are corrected by a compensation module through dual-channel closed-loop correction, and compensation logic is optimized by combining distortion residual detection and model online incremental learning; a PID controller of the control unit adopts a parameter self-tuning strategy, dynamically adjusts parameters according to the viscosity acceleration, the impurity accumulation rate and the synchronous error, generates a gate valve opening instruction to drive an execution mechanism, counteracts signal distortion caused by viscosity change and impurity accumulation, improves the adjusting precision and guarantees stable and efficient fluid conveying.

The invention discloses an intelligent analysis and management method and system for an integrated controller, relates to the field of integrated control, and aims to complete complex air conditioning system identification model training and off-line optimization of an optimal PID parameter strategy by using historical data at a cloud end with sufficient computing power. And generating an expert strategy library containing various working conditions and corresponding optimal control parameters thereof. Then, a lightweight proxy model with small calculation amount and high reasoning speed is trained based on the strategy library; the proxy model is deployed on a resource-constrained edge controller. Therefore, the edge end does not need to execute complex online optimization calculation, and only needs to input the real-time working condition data into the agent model, so that the current optimal PID parameter can be quickly deduced. According to the cloud training and edge reasoning architecture, online, real-time and intelligent self-tuning of controller parameters is realized, and a gap between an advanced algorithm and industrial landing is effectively filled up.

The invention relates to a servo driver parameter self-tuning oscillation suppression compensation method and related equipment, and the method comprises the following steps: carrying out the multi-channel synchronous collection of an operation state signal of a servo motor, and obtaining a real-time dynamic response data flow; performing time-frequency domain conjoint analysis on the servo motor based on the real-time dynamic response data flow to obtain an oscillation characteristic parameter matrix; performing oscillation source identification on the servo motor based on the oscillation characteristic parameter matrix to obtain an oscillation excitation source classification matrix; performing compensation parameter calculation on the oscillation excitation source classification matrix to obtain a multi-dimensional compensation coefficient set; and performing real-time parameter updating adjustment on the servo driver based on the multi-dimensional compensation coefficient set to obtain a servo control output signal after oscillation suppression, thereby solving the technical problem that a traditional servo driver depends on a fixed parameter setting method, is difficult to adapt to a complex and changeable working condition environment, and cannot effectively suppress an oscillation phenomenon.

A pneumatic control valve control system and a control method thereof belong to the technical field of pneumatic control valve control, the system comprises a signal detection module, an execution module, a PLC control module and an upper computer monitoring module, and the PLC control module is connected with the signal detection module, the execution module and the upper computer monitoring module. The control method comprises the following steps: S1, establishing a mathematical model transfer function of the regulating valve control system; and S2, PID parameters are adjusted on line by using a fuzzy control method, and a PID controller is optimized, so that the PID controller has a function of self-tuning according to system changes. On the basis of a traditional PID algorithm, the pneumatic control valve opening mathematical model and the fuzzy controller are established, the fuzzy self-adaptive PID algorithm is used for controlling the pneumatic control valve in real time, and compared with a traditional PID self-tuning system, a pneumatic control valve control system of the fuzzy PID control algorithm is high in response speed, good in stability and small in overshoot; therefore, the dynamic performance of the pneumatic control valve control system is greatly improved.

The invention discloses a superheat degree control method and system for suppressing superheat degree oscillation. The method comprises the steps that the process identification module identifies dynamic characteristics of the superheat degree control system through a transfer function; the prediction control module constructs a state space model according to the dynamic characteristics, performs state prediction based on a state estimation value and a disturbance estimation value, outputs a state prediction vector and calculates a first objective function; the parameter identification self-tuning module carries out self-tuning on the PID control parameters according to the dynamic characteristics and a second objective function; the PID control module outputs an electronic expansion valve driving signal according to the deviation between a superheat degree predicted value in the state prediction vector and a superheat degree preset target value and the set PID control parameter; and the electronic expansion valve driver drives the electronic expansion valve to act according to the electronic expansion valve driving signal so as to adjust the flow of the refrigerant. According to the technical scheme provided by the embodiment of the invention, superheat degree oscillation caused by low superheat degree operation of the refrigerating system is remarkably inhibited, and the system stability is improved.

The invention discloses a BLDC (brushless direct current) driving parameter self-tuning embedded system and method for multiple equipment types, and the method comprises the steps: constructing a BLDC unified mathematical model containing an electromagnetic torque equation, a back electromotive force equation and a mechanical motion equation, and building a nonlinear mapping relation library of motor type identification codes and a key electrical parameter set; a predefined multi-mode excitation signal sequence is injected into the to-be-driven BLDC motor, dynamic response data are synchronously collected, and the dynamic response data are used for representing the characteristics of the key electrical parameter set; based on collected dynamic response data, equivalent inductance and equivalent resistance in a key electrical parameter set are decoupled and extracted through a frequency domain impedance analysis method, and meanwhile, a counter electromotive force coefficient and rotational inertia in the key electrical parameter set are identified by combining time domain counter electromotive force zero crossing point detection with rotating speed differential operation. Based on a motor fingerprint feature library and a fuzzy adaptive architecture, multi-model debugging-free plug-and-play is realized, and stable control performance is maintained under a dynamic working condition.

The invention discloses a permanent magnet synchronous motor control method based on self-tuning prediction and a neural network, which aims at the inductance parameter mismatch problem in incremental dead-beat prediction control, adopts an inductance mismatch diagnosis and self-tuning mechanism to dynamically adjust model parameters and weight coefficients, and improves the robustness and dynamic response performance of a current loop. A neural network sliding mode control method is introduced into a speed ring, adaptive compensation of nonlinear disturbance and parameter uncertainty is realized by constructing an integral sliding mode surface and adjusting a sliding mode gain on line, and a stable and reliable current control instruction is output; meanwhile, the observer part constructs an improved super-spiral sliding-mode observer, the electrical angle and d-q axis composite disturbance can be estimated in real time with high precision, effective compensation information is provided for current control, and the overall stability and robustness of the system are further enhanced.

The invention discloses a cable extrusion temperature control optimization method based on an adaptive control algorithm, and the method comprises the following steps: S1, collecting and preprocessing multi-source process data, and generating a multivariable temperature control input data set; s2, configuring a self-tuning PID (Proportion Integration Differentiation) controller, and setting an initial PID parameter; s3, performing coevolution optimization on PID parameters of each heating section to obtain an optimal multi-section PID parameter combination; s4, distributing an optimal PID parameter, and adaptively adjusting the parameter in combination with the input data set; s5, implementing closed-loop temperature regulation, monitoring deviation, coordinating section management, and collecting temperature control, energy consumption and process data; and S6, periodically feeding back temperature control and energy consumption data, optimizing parameters, dynamically adjusting the parameters, and resetting the parameters when the parameters are abnormal. According to the invention, intelligent self-adaptive optimization control of multi-section temperature in the cable extrusion process is realized, and the temperature control precision, the energy efficiency and the product quality stability are remarkably improved.

The invention relates to the technical field of reinforcement learning, motor driving and the like, provides a reinforcement learning-based adaptive search self-tuning method for a motor driving algorithm, and realizes an optimal decision of an individual search behavior by performing adaptive switching among various heuristic search operators by introducing a Q learning mechanism. And meanwhile, in combination with a neighborhood search strategy based on differential evolution, the local search capability is enhanced, and the global optimization performance is improved. The method is applied to the setting problem of key parameters of the motor driving system, the I TAE performance index serves as an optimization target, the search behavior is dynamically adjusted through a self-adaptive strategy, and therefore better control performance is obtained. Compared with an existing method, the method has the remarkable advantages in the aspects of convergence speed, self-adaptive capacity and anti-interference performance.

The invention discloses a high-voltage circuit breaker short-circuit breaking direct test loop parameter self-tuning method, and belongs to the technical field of power system high-voltage electrical equipment tests. According to the method, an equivalent test loop is firstly established, mathematical modeling is carried out, in order to facilitate subsequent optimization, test loop elements are subjected to per unit, and the model is revised; and establishing a conversion relation formula between the dimensionless parameters and the dimensional parameters. Taking a dimensionless parameter as a decision variable, taking a differential equation corresponding to the model as a constraint condition, taking the sum of absolute values of differences between an obtained value and a standard value as a value function, and calculating a minimum value of the value function by applying a third-generation genetic algorithm; and finally, performing simulation verification on an optimization result. According to the invention, the working efficiency can be greatly improved, and more circuit breaker test tasks can be carried out within the same time.

The invention relates to an automatic instrument parameter self-tuning method and device based on deep learning. According to the method, real-time operation data of an instrument is collected through a multi-source sensor, time sequence alignment is carried out to form a multi-dimensional data tensor, and then local time sequence features and a long-term dependency relationship are respectively captured by using a convolutional layer and a bidirectional long-short-term memory network in a depth feature extraction network; the two types of features are fused through an attention mechanism to form a depth feature vector, on this basis, the vector is mapped into a working condition membership degree vector by adopting a differential fuzzy clustering method, and finally, the working condition membership degree is non-linearly mapped into a PID parameter adjustment amount through a multi-layer perceptron network, and the PID parameter adjustment amount is superposed to a basic parameter to realize parameter self-tuning. Therefore, under the condition that manual intervention is not needed, an automatic instrument can automatically adapt to complex and changeable operation conditions, the control precision and the system stability are remarkably improved, and the problem of adaptability of traditional PID control in a time-varying nonlinear system is effectively solved.

The invention discloses a ring-down light cavity precision pressure control method and device based on enhanced data-driven PID, and the method comprises the steps: 1) building a ring-down light cavity pressure control device, and integrating a pressure sensor, a proportional valve, a digital multimeter, a DC power supply, a vacuum pump and other key parts; 2) acquiring a proportional valve driving voltage and a pressure feedback signal based on LabVIEW, and constructing a discrete time ARX model of the system; (3) on the basis of the model residual error and the real-time error, an enhanced data driving algorithm is adopted to carry out online self-adaptive updating on PID parameters; 4) filtering high-frequency noise by using a second-order Bessel low-pass filter to improve the signal stability; and 5) realizing closed-loop control under the LabVIEW platform, and monitoring and maintaining the operation performance of the system in real time. The method has the advantages of model self-learning, parameter self-tuning, high robustness, high control precision and the like, is particularly suitable for scenes such as high-precision spectral measurement with extremely high requirements on pressure stability, and has remarkable engineering practical value and popularization and application prospects.

The invention discloses a solid powder intelligent weighing system based on fuzzy adaptive PID. The system comprises a material transfer module, a weighing unit, a material information identification module and an intelligent control unit. The material transfer module realizes automatic transfer and positioning of the weighing container; the weighing unit comprises a weighing container, a high-precision sensor and a discharging module, and the discharging module is provided with a material bin and a feeding valve mechanism; the information identification module is used for automatically identifying and inputting material information; the intelligent control unit is internally provided with a fuzzy self-adaptive PID controller, receives weight and material information, identifies valve-flow model parameters on line based on a recursive least square method and performs self-tuning, dynamically adjusts PID parameters according to a fuzzy rule, and outputs an instruction to realize three-stage control of rapid blanking, fuzzy fine adding and inching compensation. Through a parameter self-tuning and segmentation strategy, the method is adaptive to various material characteristics, the efficiency is improved and the static error is eliminated under the condition of ensuring high precision, and safe and traceable intelligent weighing is realized by relying on full-process automation and information tracing.

The invention discloses a multi-device flexible parameter self-tuning method based on transfer learning, and belongs to the technical field of multi-device parameter tuning, and the method comprises the following steps: S1, data preprocessing and feature screening; s2, aligning the feature space of the target equipment; s3, carrying out transfer learning model initialization and knowledge transfer; s4, performing online adaptive parameter optimization; and S5, multi-device parameter self-tuning circulation and model updating. According to the multi-device flexible parameter self-tuning method based on transfer learning, source device knowledge is reused through transfer learning, data dependence of target devices is reduced, and the cross-device transfer effect is improved; and convergence is accelerated and initial parameter sensitivity is reduced through an optimization strategy, so that efficient self-tuning of multi-device parameters is realized.

The invention relates to the technical field of adaptive control systems, and discloses a time-varying parameter self-tuning multicolor printing synchronous control system, which comprises a data acquisition module, a decision module, a compensation module, a control instruction module and an execution module, wherein the data acquisition module is used for acquiring state parameters of each color cell; the decision module is used for identifying a target color group needing to be compensated and determining the compensation amount of the target color group and a decoupling mode between the color groups; the compensation module is used for dynamically constructing a decoupling compensation matrix and calculating the compensation amount of a non-target color group based on the decoupling compensation matrix; the control instruction module is used for mapping the compensation quantity of each color group into the adjusting quantity of the working parameters and generating a control instruction; and the execution module is used for executing the control instruction and driving the working parameters of each color cell to be adjusted. The synchronization precision and stability of the multi-color printing system are improved, and the problem of coupling interference existing in the industry for a long time is solved.

The invention relates to the technical field of motor control, and discloses a vector control application of a direct drive motor, which comprises the following steps: S1, collecting stator current, rotor speed and bus voltage signals of the motor, and carrying out filtering preprocessing on the collected signals; and S2, constructing a self-adaptive flux linkage observation model, estimating a stator flux linkage in real time based on stator current and bus voltage signals in combination with rotor rotating speed information, and correcting stator resistance and inductance parameters through a model reference self-adaptive algorithm. According to the method, the motor parameters are corrected in real time through the self-adaptive flux linkage observation model, parameter drift caused by factors such as temperature change and magnetic saturation is effectively counteracted, and the accuracy of flux linkage estimation is improved; the improved PI regulator realizes online self-tuning of control parameters through a fuzzy control algorithm, can quickly adapt to changes of dynamic characteristics of the motor, and shortens the regulation response time of flux linkage and torque; and the resistance of the system to external interferences such as load fluctuation, power supply voltage change and the like is enhanced.

The invention relates to a frequency converter dynamic performance optimization control method, and belongs to the technical field of power electronics. The method comprises the steps that frequency converter operation and device characteristic signals and external working condition correlation signals are collected in real time and integrated into a multi-dimensional operation data set, an analysis model is built, and a mapping relation is preset; extracting transient characteristics of a device switch, calculating a dynamic impedance parameter based on a mapping relation, and matching a parameter self-tuning rule base to generate a current loop proportionality coefficient, an integral time constant and a modulation wave duty ratio correction; a rotating speed and current double-loop fusion framework is built, the weight of an outer loop is dynamically adjusted according to rotating speed deviation and load sudden change, an inner loop adopts an adaptive reaching law and disturbance compensation, and a cooperative control instruction is generated through inter-loop interaction; monitoring instruction execution data to form feedback, correcting a model mapping relation and optimizing control parameters. The dynamic response precision of the frequency converter under complex working conditions is improved, the anti-disturbance capability is enhanced, and meanwhile, the device operation loss and the manual debugging cost are reduced.

The invention relates to the technical field of compressed air system intelligent control, and discloses an intelligent control and air supply distribution method and system for a nuclear facility compressed air system pipe network. Carrying out numerical simulation on the distributed arrangement and operation conditions of different air compression stations and the flow and pressure characteristics of the compressed air pipe network under the intermittent load of different process equipment; on the basis of data generated by numerical simulation, the pipe network tail end pressure of the compressed air pipe network system is adjusted through an intelligent fuzzy self-tuning PID control method, and the operation state of an air compressor unit of the compressed air pipe network system is optimized on the basis of a flow matching strategy; pipe network operation parameters of the compressed air pipe network system are collected in real time, and the control strategy is fed back and adjusted. The whole compressed air system can operate efficiently and stably, the requirements of all links of nuclear facilities for compressed air are guaranteed, meanwhile, the operation intelligence and reliability of the system are improved, and adverse effects caused by unstable pressure and the like are reduced.

The invention relates to a valve positioner parameter self-tuning method. The method comprises the steps of positioner control parameter calculation, PID parameter calculation and maximum overshoot calculation. For different valves or executing mechanisms, valve position information is collected in real time, and needed valve position control values are determined to serve as positioner control parameters; pID parameters are calculated through a dynamic step setting method according to the positioner control parameters; in the setting process, a valve position feedback value is obtained in real time, the overshoot amount is calculated, the valve positioner controls the valve to be stabilized within the preset precision range through the PID algorithm and the maximum overshoot amount, control parameters, PID parameters and the maximum overshoot amount needed by the positioner can be rapidly calculated, the parameters calculated through the method are substituted into the algorithm, and therefore the control precision of the valve positioner is improved. And the system can be automatically adjusted and controlled when parameters change or external disturbance influences, so that the stability and the control precision of the system are ensured.