320 results about "Setpoint" patented technology

The invention relates to the technical field of environment regulation and control, and discloses a multi-parameter environment closed-loop regulation and control method and system based on an FPGA, and the method comprises the steps: unifying monitoring data to a same space coordinate system through the FPGA according to the monitoring data collected by a sensor network disposed in an environment, analyzing the coupling relation between different parameter data, and obtaining fusion data; based on the fusion data, analyzing the environment crop state through a preset crop state recognition model, simulating the influence condition of different parameter adjustment processes on the crop state, and generating a first control instruction set; analyzing the degree of deviation between the environmental parameters in the fused data and a preset target value, calculating the priority of each control instruction in the first control instruction set, and merging and optimizing the control instructions; the environmental parameters are corrected; according to the application, the regulation and control efficiency can be improved, the environmental disturbance and the crop demand change can be quickly responded through FPGA parallel processing, and the environmental regulation and control efficiency and the regulation and control effect are improved.

The invention discloses an AGV navigation system used in a storage dynamic environment, relates to the technical field of storage robot control, and is used for solving the problem of poor stable navigation in dynamic storage. According to the method, a closed-loop navigation link of state convergence, confidence evaluation, corridors, setting generation and cooperative gating is constructed, state frames are packaged in unified time, and positioning confidence and availability marks are generated based on the consistency of pose predictive quantity and pose observed quantity, so that credible hierarchical control is realized in shielding, light reflection and personnel forklift passing scenes, and the positioning accuracy is improved. A time sequence occupation constraint set is formed through obstacle tracking, a forbidden area mark is fused, a passable corridor boundary is generated, a speed set value sequence and a curvature set value sequence are generated in a corridor, acceleration change rate constraint and steering change rate constraint are applied, and stable driving control and steering control instructions are output. And the way-giving sequence and the recovery sequence are matched, and the execution state and the occupation state are written back, so that the navigation safety, the stability and the operation continuity are improved.

The present application relates to the technical field of thermal power plants, and discloses an intelligent operation method and system for a thermal power plant, a device, and a storage medium. The method comprises: measuring operation parameters of a thermal power plant in real time by means of advanced detection and soft sensing techniques; establishing a fault monitoring model to monitor the operation process of the thermal power plant on the basis of the real-time detected operation parameters, and generating an early warning signal when a fault occurs; performing fault diagnosis, and generating a fault self-healing instruction; and evaluating the operation status of the thermal power plant by means of a performance evaluation model, and then obtaining optimal operation modes and optimal operation parameter setpoints by means of a multi-objective optimization algorithm. The present application achieves high-performance real-time intelligent closed-loop control, improving the adaptability to complex working conditions and the disturbance rejection capability of a thermal power plant production process control system, and the overall performance of a thermal power plant production process; and efficient human-computer interaction is achieved by means of artificial intelligence-based early warning diagnosis and closed-loop self-healing.

The invention relates to the technical field of machine learning, and discloses a learning path generation method based on reinforcement learning, and the method comprises the steps: obtaining the state deviation data of a target object relative to a target value, and constructing an evolution trajectory; extracting a first-order derivative and a second-order gradient of the state deviation to determine a response phase acceleration vector; when the acceleration vector exceeds a threshold value, judging a response phase sudden change and locking a time point; triggering asymmetric sampling space reconstruction of the empirical pool, and executing sampling probability attenuation on historical samples before the time point by using an exponential attenuation operator; according to the method, the logic viscosity of the strategy network to the old response phase is forcibly relieved by using an asymmetric sampling mechanism, the strategy updating lag generated by high-reward historical experience is eliminated, and the strategy remodeling efficiency of the system under the working condition of phase migration is improved.

The invention provides an excitation amplitude automatic closed-loop control system and method for a DLCP test. The system comprises a multi-frequency alternating current excitation and direct current bias voltage applying module which is used for automatically applying alternating current excitation signals and direct current bias voltage signals according to a preset excitation amplitude sequence under a plurality of test frequencies; the PID closed-loop control module is used for performing closed-loop correction on the applied voltage according to the response of the test port of the tested semiconductor device, so that the condition of the applied voltage at the test port meets a preset requirement; the capacitance measurement module is used for acquiring an equivalent capacitance response signal of the tested semiconductor device; the capacitance test data caching module is used for performing associated marking and classified caching on the capacitance test data according to the test frequency, the excitation level and the bias voltage set point; and the main control module is used for coordinating automatic test processes under the conditions of a plurality of test frequencies and different excitation amplitudes and outputting corresponding capacitance test data so as to improve the consistency, stability and automation degree of the DLCP test.

The invention relates to the technical field of automatic control theory application and data center thermal management, and discloses a data center emergency refrigeration control method based on state space model prediction, which comprises the following steps: establishing a thermodynamic cycle state space equation; the refrigeration flow set value change rate and the water return side pressure change amount are obtained, and the dynamic heat exchange sensitivity is determined; monitoring feedback current characteristics of the driving unit, inverting cooling path resistance distribution and updating gain matrix elements; calculating a state prediction information sequence according to the temperature vector prediction deviation, and adjusting a time domain step length; the dynamic heat exchange sensitivity is introduced into prediction logic, controlled variable dynamic pressure retardation is compensated, an emergency refrigeration frequency control instruction is output, fluid fluctuation is converted into sensitivity compensation of a logic level, non-linear dynamic pressure damping of the emergency working condition is effectively impacted, the problems of control overshoot and logic instability caused by physical lag are solved, and the reliability of the system is improved. And the refrigeration response precision at the emergency conversion moment is improved.

A computing system for functional integration with a distributed power grid having one or more inverters. The computing system includes processing device(s) to control operation of an inverter by executing a feedback controller with a grid-tied line, of the distributed power grid, treated as a plant and including a unified algebraic control system operating based on a pair of closed-loop variables carried in closed-loop signals of the unified algebraic control system. The processing device(s) control, using the feedback controller, transitions of the inverter between a plurality of operating modes by adjusting a magnitude of the closed-loop signals, which correspond to the pair of closed-loop variables, towards a setpoint of a plurality of setpoints. Each setpoint corresponds to a different operating mode of the plurality of operating modes.

An intelligent valve self-adaptive sealing method based on working condition perception belongs to the technical field of industrial process control and fluid transportation, and comprises the following steps: step 1, multi-dimensional working condition perception and data preprocessing; 2, modeling based on a sealing requirement of a physical model and data driving; 3, optimizing a sealing force set point based on an intelligent decision-making strategy; fourthly, closed-loop control and execution are carried out; and 5, carrying out sealing state health diagnosis and early warning. According to the intelligent valve self-adaptive sealing method achieved through the intelligent valve self-adaptive sealing system, a working condition sensing and preprocessing module, a sealing requirement calculation module, a sealing setting optimization module, an intelligent control and precision execution module and a state diagnosis and early warning module are included. According to the method, the sealing force set point can be adjusted in advance according to the predicted working condition change, and dynamic and real-time self-adaptive setting, prediction and closed-loop optimization of the valve sealing force are achieved; and meanwhile, real-time transparency and diagnosis early warning of the sealing state of the valve are achieved.

The invention relates to the technical field of flow metering, in particular to a temperature return control method of an intelligent control valve. The method comprises the following steps: receiving a temperature return control strategy sent by a user and a set value of specified temperature return, and judging an initial control parameter set according to the temperature return control strategy; real-time operation data are collected from the temperature sensing module and the flowmeter according to the sampling period, the return water temperature and flow at the current moment are obtained, and the return temperature deviation is calculated; calculating a control target value of the intelligent control valve by utilizing a model fuzzy logic predictive control algorithm based on the temperature return deviation; and the valve opening degree is adjusted based on the control target value, and the control parameters are dynamically corrected through the temperature return control strategy for self-adaptive temperature return control. According to the method, through a composite algorithm of fusing fuzzy logic control and model predictive control (MPC), on the basis that preliminary control signals are generated through fuzzy reasoning, a nonlinear system model is combined to carry out multi-step prediction on the future return water temperature change trend.

The invention relates to the technical field of industrial automation and energy management, in particular to a smoke waste heat utilization monitoring system based on big data. Comprising a multi-dimensional time sequence data acquisition and fusion module used for generating a unified data set after cleaning and time alignment processing; the heat source-load prediction module based on time sequence deep learning is used for generating a predicted supply and demand curve in a future scheduling period; the multi-target dynamic optimization and scheduling decision module is used for generating an optimal heat energy distribution scheduling scheme; and the real-time feedback and self-adaptive control module takes the optimal heat energy distribution scheduling scheme as a dynamic set value of the control system, and returns the actual distribution power measured and calculated in real time to the multi-dimensional time sequence data acquisition and fusion module for periodically retraining the attention mechanism long and short term memory network model. According to the system, the precision of supply and demand matching is remarkably improved, and the problem of supply and demand mismatching caused by untimely response in a traditional mode is effectively solved.

The invention discloses an embedded hardware sensing dynamic feedback and data acquisition control method, and particularly relates to the field of general control or regulation systems, which comprises the following steps of: establishing a complete sensing acquisition framework by initializing a hardware control parameter set, synchronously acquiring original physical data and environmental parameters of a sensor, and preprocessing the original physical data and environmental parameters into available measurement values; comprehensively evaluating the deviation between actual output and a target value based on a quantitative difference model, and realizing dynamic correction and real-time feedback optimization of software level control parameters through a gradient optimization algorithm; when the parameter optimization effect does not reach the expectation, a hardware-level calibration process is automatically triggered, and temperature drift, power supply fluctuation and other physical characteristic drifts are compensated; a closed-loop control link is formed through multiple rounds of acquisition verification, it is ensured that the data precision is stable and reaches the standard, the sensing precision stability and control reliability of an embedded system in a complex environment are remarkably improved, and the method is suitable for high-precision embedded sensing application scenes.

In variants, an ML-based control system for an industrial facility can include a remote control plane, a client, a set of agents, data processing modules, an optional data store, a telemetry module, a learning module, and / or any other suitable components. In variants, the set of agents can include a facility agent, a set of local cooling agents, and / or any other suitable agents. In variants, the set of agents can include system state approximators and decision models learned via reinforcement learning. The ML-based control system can control components of the industrial facility by predicting and / or determining component setpoints based on power load and / or heat load of the industrial facility.

This invention relates to a PID closed-loop fast allocation control method based on virtual measurement, belonging to the field of automatic control technology. The method involves: receiving a general command from the upper level as the setpoint for PID allocation; the controlled variable is the final value of the sum of the output values ​​of all allocated devices after filtering; and adaptively allocating the proportional gain of the PID controller. K P and integration time T i The value is the number of devices put into automatic operation. A , Lock-up BI , Lockdown BD The calculation function value of the set is used to ensure consistent speed, stability, and quality of the allocation control under different conditions of increasing or decreasing input and lockout. The allocation PID control output is expressed as a percentage. After each device receives the control output value of the allocation PID, it is multiplied by its respective allocated rated capacity value to convert it into an engineering quantity value. Then, a manually set bias value is added to obtain the final control output value of each device, achieving flexible increase or decrease while meeting the overall command requirements. This invention has the advantages of simple, fast, flexible, and reliable allocation control.

This invention discloses a control method and monitoring system based on a planned curve. The method includes: the monitoring system periodically issuing control commands to the controlled system based on the planned curve; the monitoring system receiving and collecting feedback from the controlled system in real time of the control rate, control target value, and actual electrical quantity values; the monitoring system monitoring in real time whether the planned curve is updated and whether the automatic control mode is engaged; in response to the planned curve update or the engagement of the automatic control mode, calculating the remaining adjustment time window and determining whether it is permissible to issue a temporary control command; in response to the permission to issue a temporary control command, determining whether there is an expected adjustment deviation; and in response to the existence of the expected adjustment deviation, determining the temporary control command and issuing it to the controlled system. This invention achieves immediate response of the control system after the planned curve is updated, automatic following after the planned curve is engaged, and post-adjustment of deviations in the control process, thereby improving the real-time performance and reliability of the planned curve control system's adjustment response.

This invention relates to a cascaded fuzzy feedforward direct-drive speed control method and system based on LC filtering. The method includes: obtaining a speed error and error rate of change from a reference speed and a real-time measured speed; inputting the speed error and error rate of change into a Mamdani fuzzy inference engine to output a power setpoint coefficient; inputting the power setpoint coefficient and a normalized measured speed signal into a two-dimensional torque-current reference lookup table, performing bilinear interpolation calculation, and outputting an electromagnetic torque command; converting the electromagnetic torque command into a quadrature-axis command current, generating a PWM signal through current closed-loop regulation and SVPWM modulation, and driving a two-level voltage-type inverter; the output of the two-level voltage-type inverter drives a low-voltage permanent magnet synchronous motor through an LC sine wave filter with an RC damping branch. Compared with the prior art, this invention has advantages such as low computational load, improved dynamic response, and enhanced disturbance rejection.

This invention belongs to the field of solid oxide fuel cell control and optimization technology, and particularly to a dual-input model predictive control method for solid oxide fuel cells with carbon deposition constraints. It includes the following steps: S1: Constructing a DIR-SOFC controlled object model with 11 state variables; S2: Solving for the optimal control increment through sequential quadratic programming; S3: Constructing a constraint system; S4: Introducing an adaptive setpoint for the output voltage that adjusts linearly with the load current density. The model dimension of this invention is expanded from the existing 3 states to 11 states, fully covering six gas phase partial pressures, bilinear coupling of carbon accumulation and catalyst activity, and three-zone thermal dynamics. The ODE white-box prediction engine accurately reflects MSR nonlinearity and positive feedback of carbon deposition. Compared with ARMAX black-box prediction, the error is reduced from approximately 3% to less than 0.8%, with an average single-step calculation time of 133ms, meeting the real-time constraint of a 1s sampling time.

This application relates to a control method, system, and related equipment for compressor demagnetization current protection. The method includes: acquiring the current value of the compressor during operation; converting the current value to obtain the stator resistance value of the compressor; acquiring the compressor temperature corresponding to the stator resistance value; determining the demagnetization current value corresponding to the temperature range where the compressor temperature is located as the demagnetization current protection setpoint according to a pre-established correspondence between demagnetization current values ​​and temperature ranges; and performing protection control on the compressor based on the demagnetization current protection setpoint. This application solves the technical problems in the prior art where compressor demagnetization current protection uses a fixed current protection value and an adjustment method based on ambient temperature, resulting in inaccurate protection and untimely response. It achieves more accurate and timely protection, while optimizing the compressor's performance at different temperatures, improving safety and the range of motor applications.

The invention provides a distributed series compensator control mode switching method and device. And outputting an active power target value of a line where the distributed series compensator is located and a first voltage target value of the distributed series compensator according to the control mode of the distributed series compensator. And outputting an enable signal and a second voltage target value of the distributed series compensator according to the control mode. And switching the control mode of the distributed series compensator according to the active power target value, the first voltage target value, the second voltage target value and the enable signal of the line. According to the invention, smooth change of the power flow is realized when the distributed series compensator is switched among different control modes, impact on the power grid caused by sudden change of the power flow of the power grid due to switching of the control modes is effectively avoided, and stable operation of the power grid is ensured.

To condition an operating medium in a test object circuit (PK) of a test object (P) on a test bench to a desired temperature as quickly as possible, the invention proposes providing a mixing unit (3), there being provided, in the mixing unit (3), a mixing region (28) in which operating medium of the test object circuit (PK) can be mixed with preconditioned operating medium from a conditioning circuit (KK) in order to condition the operating medium in the test object circuit (PK) to the predefined setpoint temperature (T_SOLL), there being provided, on the mixing unit (3), for the fluidic integration of the mixing unit (3) in the test object circuit (PK), at least one test object circuit supply connection (26a) and at least one test object circuit outlet connection (26b) which are fluidically connected to one another via the mixing region (28) in order to form a part of the test object circuit (PK), there being provided, on the mixing unit (3), for connection of the mixing unit (3) to a conditioning unit (2) of the conditioning system (1), at least one conditioning unit supply connection (27a) and at least one conditioning unit return connection (27b) which are fluidically connected to one another via the mixing region (28) in order to form a part of the conditioning circuit (KK) for the operating medium.

The invention relates to the field of metal heat treatment and automatic control, and discloses a steel wire annealing quality on-line monitoring and closed-loop control method which comprises the following steps: applying pseudo-random binary sequence thermal excitation to an operating steel wire, collecting temperature response, identifying a channelized thermal response function, and determining the annealing quality of the operating steel wire according to the channelized thermal response function; the effective thermal diffusion coefficient representing the microstructure is obtained through inversion and serves as an agent parameter, the agent parameter is input into a Gaussian process regression model, a predicted mechanical property index and predicted uncertainty are obtained, during control, the deviation between the predicted index and a target value serves as a feedback signal, and the gain of a feedback controller is dynamically adjusted according to the predicted uncertainty; and meanwhile, the agent parameter change rate serves as a feedforward signal, and annealing process parameters are adjusted in combination with feedback and feedforward control quantities. According to the method, self-adaptive adjustment of the control system is realized by utilizing the prediction uncertainty, online and high-robustness closed-loop control can be carried out on the mechanical properties of the steel wire in combination with feed-forward compensation, and the consistency of product quality is improved.

This invention relates to the field of servo control algorithm design for aircraft airborne pods, providing a method and system for calculating external disturbance reactance based on servo motor current feedback. The method acquires three-phase currents Ia, Ib, and Ic, and electrical angle θ, and obtains two-phase currents Id and Iq through Clarke and Park transforms, defining Iq as the external disturbance reactance estimate IQ. A setpoint r(t) and deviation e(t) = r(t) - IQ are established, and the PID controller output u(t) is calculated. u(t) is used as the q-axis control reference and written to the driver. In voltage control mode, it is mapped to Vq and Vd for inverse transformation and PWM modulation; in current control mode, it is mapped to the q-axis current reference Iqref and converted into corresponding voltage commands by the driver's current loop, driving the servo motor to generate electromagnetic torque to cancel out disturbance reactance. This invention eliminates the need for complex motor models and high-end inertial sensors, has low computational complexity, and can quickly and accurately estimate disturbances, ensuring stable control performance of the aircraft pod in complex flight environments.

A building control system for at least one building. The building control system has a system controller and at least one building operation device with a respective operation controller. Electrical supply power is fed from a supply grid to the at least one building operation device. The system controller is set up to temporarily adapt a setpoint value for each building operation parameter in order to adapt the electrical supply power to the state of the supply grid and to stabilize the supply grid. The direct specification of a temporarily modified setpoint value for a building operation parameter is a simple and efficient option of integrating the subordinate open-loop or closed-loop controllers of the building operation devices into the building control system. This measure also makes simple retrofitting possible.

A generator and a method for controlling the generator are disclosed. The method comprises receiving a power sequence comprising a plurality of power states, creating a dynamic reference-time response within each state, and determining a dynamic average-delivered-power value within each state. An error signal is calculated within each state, and a controller output is produced using the error signal. An internal setpoint is produced based upon the error signal, and a power amplifier is controlled using the internal setpoint to control output power.

Variants of the system includes an industrial facility, job scheduling services, and a set of control agents configured to manage power and cooling resources. The method includes determining a current system state, optionally predicting a future system state, determining control setpoints, and controlling the facility based on the setpoints. In a data center implementation, the control agents receive physical state information from infrastructure components and job data from the job scheduling services. Using this information, the control agents predict future power and heat loads associated with scheduled compute jobs. The predictions are generated by a state approximator. Based on the predicted resource demands, the control agents determine control setpoints for cooling and power infrastructure components. The job scheduling services can allocate, delay, or reschedule jobs based on predicted thermal and power constraints.

This invention discloses a hierarchical coordinated control method and system for gas-cooled microreactors using deep reinforcement learning, belonging to the field of nuclear reactor control technology. The method constructs a three-layer architecture comprising an intelligent decision-making layer, a signal processing layer, and a PID controller layer. It utilizes a SAC (Smart Agent Control) agent to optimize the incremental output of electrical power and outlet temperature based on the gas-cooled microreactor's thermal parameters. The signal processing layer performs rate limiting and low-pass filtering on the optimized incremental output to generate a smooth setpoint command. The bottom-layer PID controller then outputs turbine speed and airflow signals to perform load tracking. This invention integrates the advantages of deep reinforcement learning in handling strong coupling and nonlinear dynamics with the robustness of traditional PID control. Through a physical constraint layer, it effectively suppresses oscillations and overshoot of the AI ​​control signal, achieving fast, smooth, and safe decoupled control of the gas-cooled microreactor across the entire power range, significantly improving load tracking performance and extending actuator life.

This invention provides a serpentine control device that can control the flow of raw material while maintaining wedge quality and achieving stable plate passage even when a continuous rolling mill is continuously rolling raw material. The serpentine control device includes: a prediction calculation unit that predicts the serpentine amount and wedge amount of the raw material tail end after passing through each of the multiple mill stands based on a rolling model for each of the multiple mill stands, the rolling model using an initial serpentine amount, a final serpentine amount, a reduction leveling setpoint for each of the multiple mill stands, and rolling information; a setpoint update unit that updates the reduction leveling setpoint for each of the multiple mill stands based on the serpentine amount and wedge amount of the raw material tail end predicted by the prediction calculation unit; and multiple reduction leveling control units that control the reduction leveling of the multiple mill stands based on the updated reduction leveling setpoints from the setpoint update unit.

A method for controlling actuators acting on vehicle wheels of a motor vehicle comprisesascertaining a force to be brought about on a reference point of the motor vehicle on the basis of driver specifications,ascertaining wheel forces to be brought about on the vehicle wheels to implement the force to be brought about on the reference point of the motor vehicle by means of a first dynamic allocation by model-based predictive control (MPC),ascertaining setpoint values for wheel parameters from the ascertained wheel forces, andactuating the actuators of the motor vehicle so as to implement the setpoint values of the wheel parameters.

The invention relates to a control system for a motor operating a roof assembly, including a speed controller, a current controller, a motor and a performance feed forward module. The control system is configured for receiving a speed setpoint as input, feeding back a speed signal, and determining a speed error. Providing a current setpoint and feeding back a current output to the current setpoint, and providing a current error signal to the current controller. Feeding back a relative position signal and feeding forward, based on at least one performance profile determined from an absolute position signal, a current feed forward signal to the current setpoint. And generating, based on the current error signal and the relative position signal, a drive signal for driving a motor. The current error signal is based on the current setpoint, the current feedback and the current feed forward.

This invention discloses an intelligent scheduling and energy-saving optimization method for industrial air conditioning based on multi-source information fitting, comprising: 1. Receiving temperature and humidity target attainment instructions and simultaneously setting target times; 2. Collecting temperature and humidity data inside and outside the production operation area, future weather forecast data, and past operation record data to construct a multi-source dataset; 3. Using a time-series prediction model and optimization algorithm to fit the multi-source information and calculate the optimal temperature and humidity control trajectory that satisfies the time window constraint; 4. Based on the control trajectory, combined with the space and equipment configuration of the production operation area, executing a dynamic setpoint energy-saving control strategy in a single-area single-air conditioning mode, or executing an equipment group control start-stop strategy in a single-area multi-air conditioning mode, automatically executing equipment start-up and shutdown and parameter modulation. This invention enables high-precision, rapid, and intelligent automatic control of industrial air conditioning, significantly reducing system energy consumption, thereby improving the stability of the optimized production area environment and the level of intelligent energy management.

This invention provides an industrial control method and system based on anthropomorphic thinking chain and hybrid expert model, including: Step S1: Based on the operator's thinking decision points and decision chain when controlling the equipment, construct a decision model based on anthropomorphic thinking chain and multi-layer hybrid expert model for each process segment in the complete production chain; Step S2: Train the decision model of each process segment using historical data to obtain the trained decision model; Step S3: Use the trained decision model to obtain the control setpoint based on the real-time sensing data of the corresponding process segment.