2211 results about "Control objective" patented technology

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention relates to the technical field of sewage treatment, in particular to a coupling control system and method for deep denitrification of sewage, and the system comprises a real-time water quality monitoring module, a microorganism twinborn modeling module, a real-time prediction module, an intelligent decision module, an execution mechanism module and a prediction regulation and control module. Compared with the prior art that a passive feedback control strategy based on an effluent quality index is generally adopted, the hysteresis quality is high, and violent fluctuation of an inflow load cannot be coped with; according to the method, a digital twinborn body capable of reflecting the functional state of a microbial community in real time is constructed, and a control target is improved from a traditional process parameter set point to direct optimization of microbial ecological functions; according to the invention, the method achieves the fundamental crossing from the control of technological parameters to the regulation and control of microbial ecology, can carry out intervention from the root of the reaction process, enables the system to have the active health management capability, and remarkably improves the stability of the treatment efficiency and the intelligent level of coping with complex working conditions.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention provides an unmanned aerial vehicle consistency control method based on a Stackelberg-Nash game, and relates to the technical field of multi-agent consistency and game optimization control. The method comprises the following steps: establishing a motion attitude nonlinear dynamic model of each unmanned aerial vehicle in a multi-unmanned aerial vehicle system, and defining a communication network topological relation; constructing a consistency error and a performance index function; the method comprises the following steps: establishing a layered Stackelberg-Nash game mechanism with a plurality of participants; utilizing a Bellman optimality principle to construct a coupling HJB equation to solve an optimization control strategy of the leader and the follower; and constructing a single evaluation network to estimate the optimization control strategy of the leader and the follower in each execution so as to realize an optimization control target. A favorable tool is provided for analyzing a series of control problems of master-slave consistency of multiple unmanned aerial vehicles in the control field, and the reliability of a control system can be enhanced to a certain extent.

The invention discloses a phase modifier unit state monitoring and load control method and system for reactive power compensation of a power grid, belongs to the technical field of automatic control of power systems, and mainly solves the problem that service life loss is aggravated or a control target is single due to the fact that the health state of equipment is not fully considered when an existing phase modifier unit responds to reactive power requirements of the power grid. The method comprises the following steps of: calculating reactive vacancy and extracting vibration frequency band energy characteristics by acquiring voltage and frequency of a power grid as well as vibration, temperature and exciting current signals of a unit in real time, and constructing a health state index HSI in combination with self-adaptive wavelet basis selection; taking minimum equipment life loss and optimal power grid voltage stability as multiple objectives, dynamically generating a load control instruction by adopting a particle swarm algorithm, realizing weight adaptive adjustment based on HSI, and introducing an online life loss model as a constraint condition; finally, reactive power output is adjusted through an excitation system, and cooperative control of power grid stability and equipment life extension is achieved.

The invention discloses a light passenger car chassis steering stability self-adaptive control method, which comprises the following steps of: collecting multi-dimensional working condition parameters in real time, and covering 12 types of key parameters such as a steering wheel rotating angle and a wheel speed; after preprocessing and abnormity diagnosis, double models are adopted to estimate the dynamic state of the vehicle in parallel; dynamically setting a control target, and realizing personalized control in combination with driver style recognition; the control quantity is optimized through multi-mode strategy dynamic switching and steering and braking system cooperative control; and actuator dynamic compensation, scenarized self-learning and multi-stage fault handling mechanisms are introduced. According to the method, the complex working condition adaptability is improved, the response speed is controlled, the subsystem cooperation efficiency is improved, the system robustness and the long-term use performance are enhanced, and the driving safety of the light bus is effectively guaranteed.

The invention discloses a method and a system for adjusting the internal environment of a mobile laboratory cabin, particularly relates to the technical field of environment control, and aims to solve the problem that the existing environment adjusting method adopts a fixed control target and cannot adapt to differentiated environment requirements in a multi-task scene of a mobile laboratory. The method comprises the following steps: acquiring a current task identifier of a mobile laboratory, identifying a corresponding experiment task execution stage, and dynamically generating an inter-parameter collaborative constraint relation matrix based on a fuzzy inference rule base; a regulation and control demand vector is generated by collecting a multi-dimensional environmental parameter measurement value and combining a pre-stored environmental parameter expected value; calculating a regulation and control conflict risk entropy value based on the matrix and the vector, and performing risk judgment; when the risk tolerance is exceeded, a roundabout regulation and control strategy is generated as an optimization guide parameter; finally, an environment control instruction is generated in a coordinated mode according to the optimized guiding parameters and sent to an executing mechanism; the dynamic adaptation of the environmental parameter regulation and control strategy and the experiment task is realized, and the precision and reliability of multi-parameter cooperative control are improved.

The invention provides a satellite orbital transfer control method and device, electronic equipment and a storage medium, and relates to the technical field of satellite control. The satellite orbital transfer control method comprises the following steps: constructing a three-dimensional centralized monitoring area corresponding to a target satellite based on acquired first operation information of the target satellite; based on the three-dimensional centralized monitoring area, generating a monitoring instruction, and based on the monitoring instruction, controlling a monitoring terminal corresponding to the target satellite to perform centralized monitoring on the three-dimensional centralized monitoring area; when it is monitored that the target terminal exists in the three-dimensional centralized monitoring area, second operation information corresponding to the target terminal in a unit time period is obtained, and a control mode corresponding to the target satellite is determined based on the first operation information and the second operation information; and based on the control mode, controlling the target satellite to perform orbital transfer operation. According to the invention, the orbital transfer capability of the target satellite can be improved and the collision probability can be reduced.

The invention discloses a hydroelectric generating set speed regulation control method considering excitation coordination. The method comprises the steps that 1, system state information is collected and preprocessed; step 2, on-line identification of excitation speed regulation coupling characteristics; 3, decomposing a multi-time-scale control target; step 4, designing a self-adaptive multivariable controller; 5, working condition self-adaptive parameter optimization; step 6, intelligently inhibiting the water attack effect; 7, multi-machine cooperative reactive power optimization control is carried out; 8, optimizing and executing a control strategy in real time; the technical problems that excitation speed regulation dynamic coupling, multi-time scale response mismatch, multi-machine cooperative control and the like in a hydroelectric generating set control system cannot be comprehensively solved in the prior art are solved, and the regulation response speed and the system operation safety are improved while the frequency and voltage stability is ensured.

A vehicle trajectory is planned. An initial reference trajectory of a target vehicle within a target planning duration is acquired. The initial reference trajectory includes an initial state variable and an initial control variable of the target vehicle at at least one position point. A reference lane trajectory is acquired. A trajectory cost of the target vehicle is determined according to a geometric constraint and a dynamics constraint by using the reference lane trajectory and the initial state variable and the initial control variable of the target vehicle at the at least one position point. A target vehicle is controlled to adjust from the initial reference trajectory of the target vehicle to a target travelling trajectory according to the trajectory cost of the target vehicle.

The invention discloses a force-position hybrid control method and device for a rope traction parallel robot and a medium, and belongs to the field of rope traction parallel robot control, and the method comprises the steps: 1, building a kinematics and dynamics model of the robot; step 2, dividing hierarchical priorities of contact force tracking, trajectory tracking and rope tension distribution subtasks according to operation task constraints of rich contact; 3, in the contact force tracking subtask, constructing an admittance controller based on a disturbance observer according to the deformation model, and tracking the contact force; 4, in the trajectory tracking subtask, according to a control target in a sliding mode surface form, constructing a sliding mode controller based on a disturbance observer and tracking a trajectory; step 5, constructing a motor speed controller according to the controller output quantity in the step 3 and the step 4 in combination with an artificial potential field-based rope tension distribution algorithm of the rope tension distribution subtask; and step 6, performing robot force-position hybrid control by using a motor speed controller to complete an operation task of rich contact.

The invention discloses a modeling and optimization control method for a gas supply system of a proton exchange membrane fuel cell, and belongs to the technical field of hydrogen energy power. The method comprises the following steps: establishing a nonlinear state space model of a proton exchange membrane fuel cell gas supply system; determining the optimal oxygen excess ratio of the system under different load currents through experiments, and fitting the optimal oxygen excess ratio into a reference function about the load currents; based on the nonlinear state space model, a model prediction control problem with tracking of the optimal oxygen excess ratio and minimization of the cathode and anode pressure difference as control targets is constructed and expressed as a constrained quadratic programming problem; and decomposing and iteratively solving the quadratic programming problem by adopting an alternating direction multiplier method to obtain the optimal control input of the current control period and act on the system. The method effectively solves the problem that traditional model predictive control is difficult to deploy in real time in a vehicle-mounted controller due to large calculated amount, so that efficient and accurate cooperative control of the proton exchange membrane fuel cell gas supply system is realized.

The invention belongs to a food and beverage sterile filling technology, and discloses a sterile nitrogen filling pressure-flow cooperative control system for particle-containing high-viscosity orange juice. Comprising a multi-source signal processing module used for synchronously obtaining a pressure numerical sequence, a temperature numerical sequence and a voiceprint spectrum vector of orange juice in a pipeline; the rheological state identification module is used for determining a particle density index and a particle size distribution probability of the orange juice flowing through the valve port area at present, and inverting an apparent viscosity value and a thixotropic state parameter of the orange juice so as to form a comprehensive state signature; the prediction collaborative decision module is used for generating a control target instruction set according to the maximum shear stress and further generating a pressure setting curve instruction and a displacement travel curve instruction; and the execution closed-loop control module is used for responding to the pressure setting curve instruction to adjust the pressure of the sterile nitrogen and driving the liquid filling valve to execute opening operation based on the displacement stroke curve instruction, so that dual-objective optimization of high-efficiency circulation and low-damage filling of orange juice is realized.

The invention discloses a vehicle control method, device and equipment, a medium and a product, and relates to the field of new energy automobiles. A discrete scheme matrix corresponding to the target vehicle is obtained, and the total equivalent fuel consumption corresponding to the discrete scheme matrix is calculated based on the state information of the target vehicle; determining a target total equivalent fuel consumption from the at least one total equivalent fuel consumption based on Hamiltonian calculation; and controlling the target vehicle based on the state information corresponding to the target total equivalent fuel consumption. According to the technical scheme, at least one total equivalent fuel consumption is determined through the constructed discrete scheme matrix, the optimal value is determined to control the target vehicle, the optimal fuel consumption value can be rapidly calculated in real time based on the state information of the vehicle, and effective management control over vehicle energy is achieved.

The invention provides a semi-active suspension vibration control method and system based on preview fusion and game decision, and the method comprises the following steps: obtaining pavement preview information through a forward sensor, carrying out the signal processing, and generating a future pavement preview sequence in real time; establishing a two-degree-of-freedom 1 / 4 whole vehicle vertical dynamical model; under a model prediction control framework, participants of a non-cooperative Nash game model are defined, a corresponding cost function is designed, a damping coefficient is discretized by adopting mixed integer programming, Nash equilibrium is solved by adopting a branch and bound method, and an optimal damping coefficient is obtained; mapping the optimal damping coefficient into optimal damping force meeting constraint conditions; hysteresis force compensation is conducted through a hysteresis model, and target control current is obtained through damping force obtained after hysteresis compensation according to the mapping relation between the damping force of the magnetorheological damper and the control current; and outputting the target damping force according to the instruction of the target control current. According to the invention, autonomous balance adjustment of multiple control targets is realized.

The invention discloses a weight coefficient-free model prediction control system and method for a permanent magnet synchronous motor, and particularly belongs to the field of permanent magnet synchronous motor control. The control system comprises a direct current voltage source, a three-phase converter, a permanent magnet synchronous motor, a rotating speed outer ring PI control module, an MTPA flux linkage calculation module and a model prediction control module. The control method comprises the following implementation steps: firstly, establishing a torque and flux linkage prediction model of the permanent magnet synchronous motor; then, a reference torque amplitude value and a reference flux linkage amplitude value are obtained through a rotating speed outer ring PI controller and a maximum torque per ampere (MTPA) calculation module respectively; secondly, constructing an independent torque cost function and a flux linkage cost function according to the torque error and the flux linkage error, and forming a cascade form; evaluating the switching states by using a cost function, selecting two vectors from the eight switching states of the converter by using the independent cost function of the torque (or flux linkage), and selecting an optimal vector from the two vectors by using the independent cost function of the flux linkage (or torque); and finally, the execution sequence of the torque and flux linkage cost function is alternated in each subsequent period. Compared with the prior art, the method avoids complex weight coefficient design through a cost function cascading mode, alternates the execution sequence of the cost functions in each period, dynamically balances the weights of the two control targets, and reduces the number of calculation times.

The invention provides a micro-grid control method based on edge side AI control equipment and a program product, and relates to the field of power supply and consumption system control. The micro-grid control method comprises the following steps: deploying a lightweight large model on edge side control equipment, wherein the lightweight large model is obtained through compression based on a pre-selected universal large model; micro-grid operation demand information input in a natural language form is acquired by the lightweight large model, and a control target is analyzed from the micro-grid operation demand information; generating a control strategy according to the control target; and controlling the operation state of the micro-grid according to the control strategy. According to the scheme of the invention, the lightweight large model is deployed at the edge side control equipment, the microgrid operation demand information is obtained through a natural language, the convenience and flexibility of control are improved, and the control demand is better met. And a light-weight large model accurately generates a control target and a strategy, so that more accurate operation control is realized, the adaptive capability is enhanced, and intelligent autonomous control is realized.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention discloses an MPC-based emergency power supply optimal start-stop and power distribution method and system, and the method comprises the following steps: collecting the operation parameters of various emergency power supplies, and obtaining the key load demands of a future time period; constructing an optimization control model; constructing a control objective function based on a model prediction control method; adding scheduling constraints; rolling prediction optimization is carried out according to the control objective function and the constraint condition, and the optimal start-stop state and key load prediction value of various emergency power supplies in the future control period are calculated; issuing the output optimal starting and stopping states of various emergency power supplies and the key load prediction value to field power supply equipment to realize dynamic scheduling control; through the model prediction control method, cooperative operation and intelligent scheduling among multiple power supplies are realized during disasters, the power supply stability and the energy use efficiency of the system are improved, and the method is suitable for emergency power supply systems in multiple disaster scenes such as earthquakes, typhoons and floods.

The application discloses a kind of distributed drive electric vehicle stability control method and system, desired centroid side slip angle and yaw angular velocity are calculated as control target according to vehicle dynamics model, actual centroid side slip angle and yaw angular velocity are estimated in real time by Kalman filtering.Further through stability judging law, vehicle state is identified using phase plane method.According to vehicle state, it is divided into: stable domain, coordination domain and non-stable domain.Further control target value and actual value are input into model reference adaptive controller, and the size of additional yaw moment and front wheel angle is respectively solved.The weight size of the two is calculated by the vehicle stability region determined in stability law.Additional front wheel angle can be directly input into vehicle actuator, if direct torque control is needed, additional yaw moment size needs to be input into torque distribution module for real-time solution, and finally left and right wheel torque value of front axle is output to vehicle actuator.

The invention discloses an adhesive tape binding self-adaptive control method based on harness size real-time feedback, relates to the technical field of automatic control management, and is used for solving the problem of insufficient stability in an adhesive tape binding process. According to the method, a closed-loop control flow of multichannel data acquisition, state identification and control adjustment is constructed, accurate identification and response of size sudden change, edge offset and tension abnormity in the binding process are realized, a control target is generated through gray box prediction and scene identification, and time delay compensation and phase alignment are completed by adopting equivalent advance, so that the accuracy of the binding process is improved. Under the condition that the bandwidth is limited or the time delay fluctuates, stable parameter issuing is ensured through event-driven communication and instruction beat calibration, meanwhile, hysteresis and minimum retention time mechanisms are introduced, so that a control strategy has the anti-disturbance capability, and finally, a control closed-loop result is used for dynamic optimization of a material and environment parameter library and strategy rule entries. And a continuous self-learning mechanism is constructed, so that the binding quality stability and the equipment adjustment accuracy are improved.

The invention discloses air conditioning equipment, a control method of the air conditioning equipment and a computer readable storage medium, and relates to the technical field of the air conditioning equipment, and the control method of the air conditioning equipment comprises the steps that multiple candidate parameter values of control parameters of the air conditioning equipment in the next operation period are obtained, and the multiple candidate parameter values of the control parameters of the air conditioning equipment in the next operation period are obtained based on a feedforward control strategy and a feedback control strategy; predicting a control deviation value of each control target of the air conditioning equipment under each candidate parameter value of the control parameters; based on the target weight value of each control target, performing weighted fusion processing on the control deviation value of each control target under each candidate parameter value to obtain a comprehensive deviation value of each candidate parameter value; and the candidate parameter value with the minimum comprehensive deviation value in all the candidate parameter values serves as a target parameter value, and the air conditioning equipment is controlled to operate according to the target parameter value of the control parameter in the next operation period. The control effect and the control adaptability of the air conditioning equipment can be improved at the same time.

The invention discloses a vehicle control method and device based on multi-source information fusion and actuator health state monitoring. The vehicle control method comprises the steps that driver state data, vehicle dynamics data and environment information data are obtained; sensing and fusing the multi-source data to generate comprehensive sensing data; performing scene classification according to the comprehensive perception data, identifying and judging a current driving scene type, and determining a control demand of current driving; health state monitoring is conducted on vehicle actuators, and the working performance and reliability of all the actuators are evaluated in real time; according to a scene judgment result and an actuator health state monitoring result, dynamically adjusting weight distribution of each control target in a decision making process; and based on the adjusted weight distribution, executing multi-target collaborative optimization, generating an optimal control strategy, converting the optimal control strategy into a control instruction, and issuing the control instruction to a vehicle execution mechanism. And the technical bottlenecks of the existing system in complex scene adaptability, actuator collaborative optimization, fault handling capacity and the like can be solved.

The invention discloses an equipment control method and device based on multivariate data and a storage medium, and relates to the technical field of smart home / smart home, and the equipment control method based on multivariate data comprises the steps: obtaining historical application data collected in a target environment; continuous device operations of the user are extracted from the historical application data, a device operation sequence is obtained, and the continuous device operations are device operations of which the time interval between the operation time and the operation time of adjacent device operations is smaller than a time threshold value; generating an equipment operation scene corresponding to the equipment operation sequence according to the environment characteristics of the target environment when the equipment operation sequence is executed in the historical application data; according to the technical scheme, the equipment operation scene is pushed to the user, the intelligent home equipment in the target environment is controlled to execute the execution command under the condition that the user executes the selection operation on the equipment operation scene and the trigger condition is detected, and by adopting the technical scheme, the problems that in the related technology, the control efficiency of the intelligent home equipment is low and the like are solved.

The invention discloses a storage tank pressurization control method and system based on LNG gas station fuzzy self-adaptive PID control, and aims to solve the technical problems that a traditional pressurization control method of an LNG storage tank is low in control precision, poor in stability and rapid in equipment abrasion. According to the control method, dynamic compensation for the target pressure is introduced, so that the system can aim at a more accurate control target in the initial stage, the steady-state control deviation is reduced, and the control precision is improved. And static pressure head change disturbance caused by liquid level change is overcome through dynamic compensation, the adjusting burden of the controller is reduced, the controller can focus on processing other dynamic disturbance, and therefore the control process is more stable, and overshoot is smaller. Through the design of a dynamic compensation mechanism and a special fuzzy rule base, the system can automatically adapt to various complex working conditions, and fast, stable and overshoot-free pressure control is always kept. Therefore, frequent and large-amplitude action and surge phenomena of the valve are avoided, and the energy consumption of an executing mechanism is reduced.

The invention discloses an intelligent layering and guiding device for highway tunnel fire smoke and a control method, and relates to the technical field of tunnel fire safety, a smoke flow Froude number is introduced as a direct control target, the parameter represents the ratio of smoke flow inertia force to buoyancy force, the numerical value directly determines the stability of a layering interface, and the layering interface can be directly controlled. The Froude number of each subarea of the tunnel is calculated in real time through a sensing network and is compared with a critical value calibrated through an experiment, when a real-time value is lower than the critical value, it is indicated that inertia force is relatively insufficient, the buoyancy effect is dominant, and instability and entrainment are likely to happen to an interface between a smoke layer and lower cold air, and then according to a control algorithm, the smoke layer and the lower cold air are controlled. The micro jet fan applies directional inertia force to a smoke layer by adjusting the angle and the speed, the intelligent induction smoke outlet optimizes local pressure difference and flow by adjusting the opening degree and assists in maintaining flowing, the smoke flowing state is actively locked in a stable layered flow interval, and mixing and sedimentation caused by flow state instability are prevented.

The invention discloses a motor coil press fitting process parameter self-adaptive regulation and control method and device, and relates to the technical field of motor vehicle motor manufacturing. The method comprises the steps that press fitting target parameters including a target termination position and target pressing force are obtained, press fitting is started, actual pressure and displacement values are collected in real time, a process state deduction model is constructed, whether double targets can be met at the same time when press fitting is finished in a current control mode or not is predicted, and an analysis result is output. If the conditions cannot be met at the same time, the predicted pressure deviation value and the displacement deviation value are taken as optimization targets, a process state compensation value is introduced, a multi-target optimization function is constructed to solve a control target weighting coefficient, a press fitting tail-section hybrid controller is reconstructed, output instructions of a position ring and a force ring are fused, and press fitting is executed. According to the invention, synchronous high-precision control of the pressing force and the termination position under material fluctuation and environmental interference is realized, and the adaptability and stability of coil press-fitting processes of various motors such as a new energy automobile driving motor and a household appliance motor are improved.

The invention relates to the technical field of servo drivers, in particular to a servo driver control method, and the technical scheme comprises the steps: carrying out the online identification of load inertia and external disturbance through a self-adaptive observer, generating a dynamic compensation parameter, constructing a multi-model parallel self-adaptive observer group, and carrying out the online identification of the load inertia and external disturbance through the self-adaptive observer group, a dynamic confidence evaluation mechanism is designed, disturbance dynamic compensation is realized, parameter sudden change and slow change scenes can be taken into consideration, the real-time performance of dynamic response can be ensured, compensation parameters are input into a fuzzy neural network controller, an optimal control quantity is generated in combination with a preset control target, a multi-source compensation parameter input channel is constructed, and the optimal control quantity is generated. A dynamic input weighting module is designed, fuzzy rule confidence is updated through an online strategy gradient algorithm to generate an optimized control quantity, the working condition adaptability can be enhanced, a dynamic target can be dealt with, finally, a sliding mode variable structure algorithm is adopted to carry out high-frequency buffeting suppression on the control quantity, and a final driving signal is output to a power module. And the control precision of the servo driver is improved.

The invention relates to a kitchen waste sewage accurate aeration control method, system and device based on fuzzy PID and deep learning and a medium, and the method comprises the steps: collecting parameters, and processing the parameters to obtain preprocessed data; predicting the oxygen demand through a deep learning time sequence prediction model, and generating a dissolved oxygen set value interval in combination with multi-target reinforcement learning; based on the deviation and the deviation change rate of a dissolved oxygen set value interval and a real-time measured value, an evolutionary fuzzy reasoning system is used for adjusting gain parameters of an aeration controller, PID control quantity is output and converted into an aeration equipment driving signal, and multi-area aeration intensity dynamic distribution is implemented in combination with a hydrodynamic mapping relation and preprocessed data. Through the synergistic effect of the technologies, the core defects of single sensing dimension, prediction lag, control target conflict, extensive execution and the like of a traditional method are systematically solved, and collaborative optimization of degradation efficiency, energy consumption economy and equipment operation life in the kitchen waste sewage treatment process is realized.