255 results about "Control vector" patented technology

The invention discloses a multi-parameter coupling intelligent regulation and control method and system in a vanadium ore smelting process, relates to the technical field of non-ferrous metal smelting automatic control, and solves the problems of feeding deviation and resource chain scission caused by slow response of an existing heat balance model to ore impurity fluctuation, online sensing lag and process island. The impurity concentration and the dielectric constant are obtained in real time through an LIBS / XRF combined probe and a microwave dielectric spectrum, rheological disturbance is dynamically corrected through extended Kalman filtering, a dynamic coupling response model is input, a thermodynamic interference factor is calculated, and a leaching and precipitation strategy is generated; performing mother liquor tail gas material flow collaborative optimization in parallel, constructing a multi-dimensional control vector U, performing iterative updating through near-end strategy optimization in a digital twin and issuing the multi-dimensional control vector U to an edge PLC (Programmable Logic Controller) in a distributed manner, and finally correcting a model weight through online XRD (X-Ray Diffraction) feedback; according to the method, the adaptive capacity to complex vanadium ore component disturbance, the response precision of online control and the inter-process resource circulation efficiency are remarkably improved.

The invention discloses a frequency conversion vector control method for a digital energy-saving pump station, which belongs to the technical field of vector control and comprises the following steps: constructing a coupling vector model, simulating the running states of a pump unit under different working conditions through a virtual test signal, and automatically correcting the coupling vector model; operating data of a pump set are collected in real time, an operating situation feature set is constructed, predictive vector control is carried out, an optimal voltage vector sequence is generated, and meanwhile load fluctuation of a pipe network is predicted; demand pre-judgment is carried out on the starting and stopping time and the action amplitude of the anti-backflow device, a bidirectional disturbance suppression mechanism is constructed, current vector parameters are pre-adjusted, and calculus correction is carried out based on deviation judgment; when multiple pumps operate in parallel, the instantaneous operation efficiency point of the pump set is calculated in real time, the efficiency change trend is predicted, a load distribution scheme is generated, the comprehensive energy efficiency is evaluated, energy efficiency self-optimization is carried out, the comprehensive energy efficiency of the system is improved, and control experience reuse is achieved.

The invention relates to the technical field of industrial automation, and discloses a self-adaptive dynamic pressure regulation energy-saving system and method based on pneumatic equipment, and the system comprises a state monitoring module which is used for constructing an operation state matrix of the pneumatic equipment; the pressure control module is used for generating a dynamic pressure control vector of the pneumatic equipment; the risk judgment module is used for calculating a real-time operation risk coefficient of the pneumatic equipment; the hierarchical regulation and control module is used for establishing a hierarchical regulation and control strategy set of the pneumatic equipment; the control feedback module is used for generating a self-adaptive control parameter sequence of the dynamic voltage regulation instruction and the state early warning instruction; and the scheme output module is used for integrating the dynamic pressure regulating instruction, the state early warning instruction, the hierarchical regulation and control strategy set and the self-adaptive control parameter sequence and outputting a self-adaptive dynamic pressure regulating and energy saving scheme of the pneumatic equipment. The energy utilization efficiency and the operation stability of the pneumatic equipment can be improved.

The invention relates to the technical field of energy meteorology and intelligent power grids, in particular to a multi-extreme meteorological high-risk scene set generation method, system and equipment based on a joint training generative adversarial network. The method comprises the following steps: constructing a physical information generative adversarial network framework comprising a generator, a discriminator, a predictor and a physical constraint module; designing a multi-objective loss function fusing adversarial loss, prediction loss, physical consistency loss and task performance loss; adopting a training strategy combining meta-learning initialization and incremental learning to jointly optimize parameters of the generator and the discriminator in stages; extreme risk scene data of specified disaster types, seasons and intensity grades are generated through condition vector control, and the extreme risk scene data are stored in a high-risk scene library after physical consistency verification. Through the method, a multi-extreme-weather high-risk scene set with statistical authenticity, physical rationality and task correlation can be directly generated, and the risk identification, scheduling optimization and toughness evaluation capabilities of the clean energy base under extreme weather conditions are remarkably improved; the problems of sample scarcity, model overfitting and lack of physical constraints in scene generation in the prior art are solved, efficient and automatic generation of a high-risk scene is realized, and reliable data support is provided for power grid toughness evaluation and scheduling decision of a clean energy base.

The invention relates to the technical field of solid waste incineration automatic control, and discloses a solid waste incineration equipment automatic control method and system.The method comprises the steps that real-time data of solid waste components in a hearth are collected, and an initial multi-dimensional parameter set is obtained through preprocessing; calculating a balance point of the heat value and the water content through fuzzy reasoning; when the balance point exceeds a threshold value, parameter interaction influence is predicted, and a multi-dimensional control vector is generated; performing data fusion and historical data matching in combination with real-time incineration process indexes to obtain an enhanced load distribution adjustment factor; carrying out weight comparison based on the adjustment factor and the balance point, judging a load balance influence index, and carrying out matching correction if the index exceeds a compatible threshold value; updating a load distribution scheme according to a correction result, and generating a final operation strategy; and a control instruction is sent through a feedback cycle mechanism and is continuously optimized, so that closed-loop control is realized. The device can adaptively treat dynamic changes of solid waste components, and the incineration efficiency and stability are improved.

The invention discloses a converter steelmaking endpoint forecasting method and device and a storage medium, and the method comprises the steps: carrying out the multi-scale importance modeling and graph attention mechanism modeling of the multi-dimensional process characteristics of the current heat, and generating the importance weight and the structure perception weight of each dimension of process characteristics; fusing the importance weight and the structure sensing weight, and multiplying the fused weight with the corresponding dimension process characteristics to generate a characteristic regulation and control vector of the current heat; performing multi-scale convolution operation on the feature regulation and control vector, extracting local mode features under different receptive fields, and outputting a spatial feature vector after the local mode features are enhanced by a self-attention mechanism; a plurality of groups of Transformer encoders are adopted to respectively process the furnace feature sequences with different historical window lengths, and cross-furnace time sequence feature vectors are extracted; and carrying out dimension alignment on the spatial feature vector and the time sequence feature vector, carrying out dimension-by-dimension weighted fusion through a learnable fusion weight, and outputting a steelmaking end point state prediction value of the current heat through a regression prediction network.

The invention relates to the technical field of robot control, and discloses a robot cerebellum reinforcement learning method and system based on adaptive environment change, and the method comprises the steps: collecting multi-modal sensing data in the operation process of a robot; based on the multi-modal sensing data, determining an environment sudden change increment and an environment characteristic variable quantity, and obtaining a main strategy network according to the environment sudden change increment and the environment characteristic variable quantity; outputting a basic action vector of the robot based on the main strategy network, and performing value evaluation on the state of the robot to obtain a value evaluation quantity; performing updating calculation on the basic action vector based on the value evaluation quantity to obtain a compensation action vector; and performing vector superposition fusion on the basic action vector and the compensation action vector to obtain a final control vector, and taking the final control vector as a cerebellum learning action instruction of the robot, thereby realizing dynamic adaptation of a reinforcement learning strategy and cerebellum rhythm control. And the motion stability and the learning convergence efficiency of the robot in the unstructured environment are optimized.

The invention belongs to the technical field of power system operation and motors, and particularly relates to an energy storage type phase modifier control method which comprises the steps that a rotating shaft of a double-fed motor is connected with a flywheel to serve as an energy storage type phase modifier; the rotor winding is connected to a power grid through an excitation system and a transformer in sequence, and the excitation system is electrically connected by a reactor, a back-to-back converter and a filter in sequence; the stator winding is directly connected to a power grid; the control system decouples reactive power and active power, vector control is carried out on exciting current to absorb or release power, and reactive power-voltage support and active power-frequency support of the power grid are completed. The method overcomes the defects that a traditional direct-current excitation phase modulation mode can only modulate phase and cannot modulate frequency, or is cumbersome and slow in response. The system can meet the requirement of wide-range variable-speed operation, quickly store / release a large amount of rotor kinetic energy, inhibit the voltage and frequency fluctuation of a power grid, and is especially suitable for stations with high new energy occupation ratio. The invention also provides a non-transitory readable recording medium storing the program of the method and a system comprising the medium, and the program can be called through a processing circuit to execute the method.

The invention discloses an aspheric surface processing intelligent feedback control system based on digital twinning, and the system comprises a digital twinning modeling and baseline calibration module which is used for building an aspheric surface processing digital twinning body and generating a baseline; the twinborn update and virtual-real residual calculation module is used for calculating virtual-real residual update digital twinborn bodies; the reinforcement learning candidate strategy generation module is used for generating reinforcement learning candidate strategies through the reinforcement learning strategy network; the model predictive control optimization module is used for establishing a model predictive control optimization problem and performing rolling prediction; the double-strategy fusion control module is used for executing virtual processing simulation and generating a control vector; and the virtual-real closed-loop adaptive convergence execution module is used for executing aspheric surface processing to realize closed-loop adaptive convergence control. According to the method, digital twinning is combined with reinforcement learning and model prediction control, intelligent optimization of aspheric surface machining is achieved, and the method has the advantages of being high in precision and stability and rapid in convergence.

The invention relates to the technical field of drainage control, in particular to a liquid level sensing drainage control method and system based on electronic and floating ball cooperative detection, and the method comprises the steps: fusing the angular displacement, capacitance value and acceleration disturbance data of a floating ball, and constructing a liquid level state observer; a dynamic compensation factor is introduced to strip liquid shaking interference, and an accurate liquid level reference value is obtained; a composite control vector is constructed based on the liquid level deviation and the change rate, and a drainage instruction is generated through analysis to control a valve to act; meanwhile, a self-adaptive feedback adjustment mechanism is established, observer parameters are dynamically calibrated through liquid level response residual errors, and the drifting error of an actuator is offset; according to the technical scheme, the robustness of drainage control can be remarkably improved.

The invention discloses a car following control method and a related product. According to the scheme, multi-source data are acquired, and the multi-source data are fused to obtain a fusion result; based on the fusion result, performing multi-modal trajectory prediction on the driving behavior of the preceding vehicle to obtain a prediction result of the driving behavior of the preceding vehicle; the multi-modal trajectory prediction comprises longitudinal motion prediction and transverse motion prediction; quantifying a car following risk by using a multi-dimensional risk index to obtain a risk level; constructing a multi-target cost function based on the fusion result, the front vehicle driving behavior prediction result and a control vector, and solving the multi-target cost function by using a preset constraint condition to obtain an initial control parameter of the vehicle; and on the basis of the risk level, initial control parameters of the vehicle are adjusted, and target control parameters of the vehicle are obtained. Compared with the problem of response lag in adaptive cruise control in the prior art, the adaptive cruise control method has obvious advantages.

The invention belongs to the field of non-electrical variable control, and particularly discloses a temperature and humidity control system for vermicelli drying, which comprises an environment feature extraction module used for collecting real-time temperature and humidity data in a dry environment through a distributed sensor network and extracting environment feature vectors in combination with external meteorological data; the equipment adjusting module is used for carrying out mapping processing on the operation parameters of the drying equipment according to the environment feature vector to generate an equipment adjusting instruction sequence; the process fusion module is used for extracting a process parameter change trend from a historical drying record and fusing the process parameters and the current environment feature vector by adopting a time sequence analysis method to obtain a comprehensive regulation and control vector; the interference evaluation module is used for calling a real-time data interface through an edge computing node to obtain external environment interference factors; the invention aims to solve the problems of inaccurate temperature and humidity regulation and control, data response lag and insufficient linkage between environment variables and equipment in the vermicelli drying process.

The present application relates to the technical field of data processing, and more particularly to a building energy consumption optimization method based on BIM. The content includes: obtaining the original data of the target building and preprocessing, mapping the preprocessed original data to the BIM model, obtaining the dynamic BIM data model, and constructing the five-dimensional space-time feature tensor; the five-dimensional space-time feature tensor is processed by dimension reduction compression transformation, the three-dimensional tensor after compression transformation is used to construct the perturbation response function; based on the perturbation response function, the control inversion processing is carried out to obtain the preliminary control vector, the control path evolution mechanism is introduced to optimize and adjust the preliminary control vector; the optimized and adjusted control vector is converted into a control signal to realize building energy consumption optimization. The traditional building energy consumption optimization method in building energy consumption management has the problems of difficult spatio-temporal fusion of multi-source data, difficult accurate modeling of dynamic coupling relationship and lack of real-time adaptive ability of control vector.

The invention discloses a multi-target cooperative control method for a production system, and the method comprises the steps: collecting and preprocessing multi-source data of wire harness production, and constructing a multi-dimensional structure containing processes, materials, carriers and time phases; establishing a time-phase mapping and dynamic evolution mechanism based on the multi-dimensional structure, and generating a target time-phase association set; takt, material in-place and carrier circulation signals are extracted, a multi-frequency phase set is constructed, and a comprehensive deviation signal is formed; constructing a multi-frequency phase-locked group control module, and generating three types of multi-target control instruction sets of takt, material and carrier; inputting the target association set and the control instruction set into an improved Longform model to generate a prediction control vector of the next period; and according to the predictive control vector output parameter, dynamically updating the plan and path configuration and rolling and correcting the phase locking parameter. According to the invention, through multi-dimensional time phase modeling and the improved Longform model, cooperative control optimization of the wire harness production takt, the material and the carrier is realized.

The invention is suitable for the technical field of medical instruments, and provides a mechanical arm control method, computer equipment and a storage medium. The method comprises the steps that first motion data and first dynamic data of all joints of the mechanical arm at the current moment and second motion data and second dynamic data of all joints at at least one historical moment are obtained; determining acting force data of the end tool at the current moment according to the first motion data and the first dynamic data; determining target rigidity of the target object according to the first motion data, the first dynamic data, the second motion data and the second dynamic data; determining a force control law of the end tool at the current moment according to the acting force data, the target rigidity and the expected acting force of the end tool relative to the target object at the current moment; and according to the force control law and the expected pose data, determining the force-position hybrid control vector at the next moment, thereby reducing the damage to the target object while improving the pose control precision of the end tool.

The invention relates to the technical field of air quality monitoring, and provides an air quality sensor based on the Internet of Things and a remote control method thereof, and the air quality sensor comprises a multi-source heterogeneous sensing fusion system, a time-space domain edge gateway system and a dynamic response collaborative central system. The remote control method comprises the steps that a purification device linkage control matrix is processed through a heterogeneous instruction fusion technology to obtain a space optimization control vector set, and the space optimization control vector set forms a man-machine interaction entropy value mechanism; the man-machine interaction entropy value model is processed through an entropy weight dynamic distribution strategy to obtain a space state sensing vector, and the space state sensing vector forms a cross-medium alarm triggering channel; the cross-medium alarm trigger channel is processed by the bidirectional response coupling engine to obtain an equipment state feature topology, and the equipment state feature topology forms a closed-loop control entropy correction instruction. According to the invention, pollution of a physical space is dynamically converted into operable equipment control parameters, and a complete Internet of Things closed-loop system from environment monitoring to purification execution is established.

The application discloses a greenhouse environment control method based on adaptive control vector parameterization, comprising the following steps: constructing a nonlinear optimal control problem of a type of greenhouse environment dynamic constraint optimization based on constraint conditions in a greenhouse environment, and converting the problem into a nonlinear programming NLP problem of a type of limited dimension parameters; based on a control vector parameterization method, solving the NLP problem by fusing engineering constraint rules and a penalty function method aiming at different constraint conditions on fixed time grid nodes of a "coarse grid"; adaptively subdividing the fixed time nodes of the "coarse grid", and constructing new "refined" time grid nodes; based on the new "refined" time grid nodes, re-solving the NLP problem until a termination condition is met to stop the solving, so that the best solution result of the NLP problem is obtained. The application solves the problem of low calculation efficiency in the solving process of the control vector parameterization method for the dynamic optimal control problem in the prior art.

The invention discloses a hub motor vector control method based on an improved grey wolf particle swarm algorithm, and the method comprises the steps: building a mathematical model of a hub permanent magnet synchronous motor, building a double-closed-loop vector control simulation model containing a speed loop and a current loop in Simulink based on the mathematical model, and selecting a speed loop PID controller as a to-be-set object; improving the original grey wolf particle swarm algorithm to obtain an improved grey wolf particle swarm hybrid algorithm; and performing iterative optimization on the three parameters Kp, Ki and Kd of the speed loop PID controller through the improved grey wolf particle swarm hybrid algorithm, and outputting the finally obtained global optimal solution q as the optimal values of the three parameters Kp, Ki and Kd of the speed loop PID controller to complete the hub permanent magnet synchronous motor vector control.

This invention relates to the field of dexterous hands in humanoid robots, and more particularly to a method, device, and medium for controlling a robot dexterous hand based on interferometric identification. The method includes: Step S1: Obtaining a hand shape sequence based on task information parsing; Step S2: Generating corresponding joint states for each hand shape in the hand shape sequence, obtaining a joint state vector for each hand shape; Step S3: Forming a hand shape group by grouping two adjacent hand shapes in the hand shape sequence; Step S4: Using the joint state vectors of the two hand shapes in each hand shape group as boundary constraints, and filtering them using an interferometric identification model, generating multiple joint transition state vectors; Step S5: Sequentially concatenating all joint state vectors and joint transition state vectors to obtain a control vector for the dexterous hand; Step S6: Controlling the joint movements of the dexterous hand based on the obtained control vector. Compared with existing technologies, this invention has advantages such as eliminating the need for complex spatial state calculations.

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 provides a robot variable impedance control method and system based on model predictive control and control equipment, and the method comprises the steps: constructing an iteration vector based on a current state vector and a control vector, and executing the iteration of the iteration vector under a plurality of set constraint conditions through employing an optimization model based on a non-linear objective function and the iteration vector, the plurality of constraint conditions comprise impedance parameters, upper and lower bound constraints of rigidity parameters, change rate constraints and passivity constraints. And extracting a control vector in the iteration vector, and obtaining an impedance matrix and a stiffness matrix based on the extracted control vector. And inputting the impedance matrix and the stiffness matrix into an impedance controller, and predicting to obtain a torque for executing robot control. According to the scheme, follow-up behaviors of the system can be predicted in a rolling mode in each control period, control input is optimized under the constraint condition, a systematic optimization strategy is provided for parameter adjustment, multi-target performance indexes and hardware constraints can be considered, and control robustness is improved.

The invention discloses an intelligent control method of a numerical control dotting and ruling machine for mold machining, and particularly relates to the field of numerical control machining control. The method comprises the following steps: establishing a three-dimensional point cloud model containing surface microscopic curvature distribution and a local stress-strain relationship by acquiring a space geometric model and material physical parameters of a target mold workpiece; performing grid division on the point cloud model, extracting curvature features and local elastic modulus of each grid unit, and constructing a parameter matrix; in combination with the initial track of the preset tool path, modulating by adopting a self-adaptive control algorithm to generate a corrected path; in the machining process, a control vector is constructed based on the corrected path and the dotting depth and the scribing force which are fed back in real time, an execution mechanism is driven to conduct closed-loop control, and dynamic compensation of each dotting and scribing action is achieved; the method can effectively solve the problems of uneven dotting depth, lineation deformation and quick tool abrasion in the machining process of complex curved surfaces or high-strength materials, and the machining precision and stability are remarkably improved.

The invention discloses a flight control device, an aircraft control method and a vertical take-off and landing aircraft, and relates to the technical field of aircraft control. A main manipulator configured to receive manipulation information input by a pilot; the backup manipulator and the main manipulator are independently arranged relatively, and the backup manipulator is configured to receive manipulation information input by a pilot; the control system is configured to identify a target manipulator from the main manipulator and the backup manipulator, generate a vector control instruction mapped with the manipulation information by using a signal corresponding to the manipulation information received from the target manipulator and combining a preset manipulation mapping relation, and control the target manipulator according to the vector control instruction. And controlling the aircraft to perform vector motion according to the vector control instruction. According to the scheme, the complexity of aircraft control can be effectively reduced, and the safety of aircraft control is improved.

The invention belongs to the technical field of automation control, and particularly relates to a control parameter optimization method and system for a multistage reaction kettle, and the method comprises the steps: collecting key operation data of a series multistage reaction kettle at each moment in real time, and constructing an initial temperature sequence and an initial valve opening sequence after standardization and adaptive filtering denoising; indexes such as temperature fluctuation standard deviation are calculated based on the initial temperature sequence, the temperature deviation degree is obtained through normalized weighted fusion, and parameters such as the Arrhenius law and the actually measured temperature are combined to construct a reaction nonlinear strength index; on the basis of the index coupling initial valve opening degree sequence change rate, a model vector self-adaptive correction coefficient is obtained in combination with parameters such as a smooth factor; and correcting a basic step response vector controlled by the dynamic matrix by using the coefficient, solving an optimal valve opening adjustment amount, calculating an execution value, and outputting the execution value to an execution mechanism to complete regulation and control, thereby realizing full-cycle optimization of control parameters. According to the invention, the control accuracy of the multi-stage reaction kettle is improved.

This invention discloses a method for predicting and protecting the overheating risk of asynchronous motors, specifically relating to the field of motor control and protection technology. Based on an intelligent fusion model, it estimates temperature and thermal stress field, calculates the rate of change of thermal stress, non-uniformity, and hotspot trends, and calculates a dynamic health index using historical data. These parameters are then input into a multi-objective reinforcement learning controller to optimize long-term health and short-term performance, generating a thermal shaping control vector to regulate the motor. This invention combines physical mechanisms with data-driven approaches through an intelligent fusion model, utilizing a graph neural network to learn the structure of the heat conduction graph and verify physical laws, thereby improving the accuracy of thermal state estimation. It achieves a multi-dimensional risk characterization combining transient impact and cumulative effects through the rate of change of thermal stress, non-uniformity, hotspot trends, and dynamic health index. By optimizing long-term health and short-term performance losses, a thermal shaping control vector is generated to achieve regulation from passive protection to active prevention, extending the motor's service life.

The application relates to the technical field of drainage control, in particular to a liquid level sensing drainage control method and system based on cooperation of an electronic device and a float ball, which comprises the following steps: fusing float ball angular displacement, capacitance value and acceleration disturbance data to construct a liquid level state observer; introducing a dynamic compensation factor to strip liquid sloshing interference and obtain an accurate liquid level reference value; constructing a composite control vector based on liquid level deviation and change rate to analyze and generate a drainage instruction to control a valve action; and simultaneously establishing an adaptive feedback adjustment mechanism to dynamically calibrate observer parameters by using liquid level response residual error and offset actuator drift error; and the technical scheme can significantly improve the robustness of drainage control.

The application belongs to the technical field of industrial control and relates to a purification plate glue brushing control method and system. The method comprises the following steps: obtaining an inlet plate state and corresponding nozzle channel execution state, conveying state and post-spraying pattern reading state; performing time delay compensation according to the conveying state to obtain a plate running coordinate and build a coordinate state chain; reading an effective source set and an effective state amount, generating a robust baseline residual in a neighboring stable window; generating a cross-source conflict degree, obtaining a quality gate confidence and determining a gate state; generating a control vector and outputting a control amount when the control condition is met, and writing a review-confirmed effective control result into a batch process archive. The technical scheme of the application realizes closed-loop control of the continuous glue brushing process of the purification plate through multi-source state alignment, robust residual analysis, cross-source conflict gating and result rewriting, can improve control basis consistency and control amount adjustment stability, and meets the reviewable regulation and control demand of continuous batch glue brushing.

The invention relates to the technical field of power system automation, in particular to a new energy box-type substation state estimation, regulation and control method, which comprises the following steps of: injecting a multi-frequency electrical excitation signal into a box-type substation, acquiring a voltage signal and a current signal, establishing a digital twin model based on primary wiring topology and grounding grid topology, performing combined state estimation and parameter estimation to obtain a comprehensive feature vector representing a degradation state, and inputting the comprehensive feature vector into a fault diagnosis and life evaluation model to obtain a risk index; and constructing a risk weighting objective function taking active output, reactive output and current as independent variables, and solving to obtain a control quantity vector for adjusting power output, reactive compensation and an electric protection device action constant value, thereby reducing the operation risk and prolonging the service life of equipment while ensuring the grid-connected requirement.

The application discloses a motor driving control system, an electric driving joint and a control method thereof. The control system comprises a first control module and a second control unit. The first control module comprises a first control unit, a signal gating control unit and a motor unit connected in sequence. The first control unit is configured to generate a first control signal according to the operation feedback signal of the motor unit through a vector control algorithm. The second control unit is electrically connected with the signal gating control unit. The second control unit is configured to generate a second control signal according to the operation feedback signal of the motor unit through a reinforcement learning algorithm. The signal gating control unit is configured to select the first control signal or the second control signal as a target control signal to control the operation of the motor unit according to the operation feedback signal of the motor unit and the second control signal. The application can solve the problem that the electric driving joint cannot quickly and safely and reliably adapt to different humanoid robots and has poor adaptability.

This application provides a method, apparatus, electronic device, and storage medium for determining responses to questions, belonging to the technical field of question response determination, and applicable to scenarios in financial, medical, health, and elderly care insurance application systems. The method includes: responding to a question input by a user, performing semantic and compliance constraints on the question, and generating a structured control vector corresponding to the question; inputting the structured control vector into multiple perturbation decoding heads, and generating candidate response sequences corresponding to the business semantic distribution of the question and each perturbation decoding head based on the structured control vector; wherein the business semantic distributions of the multiple perturbation decoding heads are different; and determining the target response sequence for the question based on the candidate response sequences. This application improves the diversity and accuracy of responses by constructing a structured control vector through pre-generation semantic and compliance constraints, and by generating candidate responses in parallel using perturbation decoding heads with differentiated business semantic distributions.