3076results about "Controllers with particular characteristics" patented technology

The invention relates to the technical field of thermal power plant flue gas denitration, in particular to a thermal power plant APC advanced process control system and a denitration optimization method.The system comprises a data acquisition module for acquiring boiler combustion parameters, flue gas emission data and the operation state of a denitration system in real time; the multivariable predictive control module is used for optimizing the combustion efficiency and the NOx generation amount based on a dynamic matrix control algorithm; the denitration optimization decision module is used for dynamically adjusting the ammonia spraying amount through an ammonia escape feedback model; the intelligent coordination module integrates a unit load instruction and an environmental protection constraint condition and is used for cooperative control of combustion-denitration; according to the system, boiler combustion parameters, flue gas emission data and the operation state of the denitration system are obtained in real time through the data acquisition module, a dynamic matrix control algorithm of the multivariable prediction control module is combined, the NOx generation trend can be predicted in advance, the combustion efficiency can be optimized, and therefore the problem that adjustment is lagged when loads fluctuate in traditional PID control is effectively solved.

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention discloses an equipment temperature adjusting method fusing a long short-term memory network, and particularly relates to the technical field of temperature control, which comprises the following steps: determining the deployment position of a sensor through thermal simulation, collecting multi-source heterogeneous data, dynamically adjusting the sampling frequency in combination with the temperature and the load current change rate, and adjusting the temperature of the sensor; after data preprocessing, an attention mechanism enhanced LSTM prediction model is constructed, a time sequence sample data set is divided according to equipment thermal response characteristics, training is carried out, and an optimal model is obtained through early stop mechanism optimization; predictive feedback double-closed-loop regulation and control is achieved based on the optimal model, outer loop PI control is combined with an integral separation mechanism to generate a basic control quantity, an inner loop outputs a correction quantity through fuzzification, reasoning and defuzzification, an actuator is driven after superposition, and safety linkage is triggered synchronously; constructing an incremental data buffer pool to screen effective samples, and adaptively updating model parameters by adopting a layered fine tuning strategy; the temperature regulation and control precision and the long-term self-adaptive capability are obviously improved, and the over-temperature risk of equipment is reduced.

The invention relates to the technical field of electrical cabinet monitoring, in particular to an electrical cabinet active anti-condensation method and system based on a condensation mechanism mathematical model. According to the invention, a mathematical model based on a condensation formation mechanism is constructed, and environmental meteorological parameters, station room environmental parameters, electrical cabinet internal microenvironment parameters and cable trench state parameters are monitored through a four-stage monitoring system; and fusing the multi-source heterogeneous data, inputting a condensation mechanism mathematical model to calculate a condensation risk index, and adopting a hierarchical response mechanism to cooperatively control an execution mechanism according to the obtained condensation risk index value to realize active defense of condensation. According to the method, the condensation phenomenon with the space-time dynamic characteristic is quantitatively described through a mass transfer and heat transfer coupling equation, a grading prevention and control strategy and a cooperative control algorithm are developed by calculating the condensation risk index, setting the threshold condition and judging the condensation formation risk level, the prevention and control intensity is dynamically adjusted according to the risk level, and the control accuracy is improved. And the risk response speed and the resource utilization efficiency under the complex working condition are obviously improved.

The invention discloses a welding robot system based on body sensing and an intelligent route generation method, and belongs to the technical field of robot welding, the welding robot system comprises a body sensing module, a fusion processing module, an intelligent route generation module and an execution feedback module, the body sensing module collects multi-modal data in parallel through multiple sensing units and preprocesses the multi-modal data; the fusion processing module combines a three-dimensional attention mechanism and digital twinning mapping to realize deep feature fusion, the intelligent route generation module outputs an adaptive path through planning-optimization-layered correction, and the execution feedback module constructs a full-closed-loop dynamic adjustment mechanism. By the adoption of the system and method, the problems that physical and virtual scenes are not synchronous, dynamic interference response is insufficient and path adaptability is poor in complex welding seam welding are accurately solved, and the welding path fitting degree and quality stability are remarkably improved.

The invention discloses an unmanned agricultural machine path navigation method based on farmland environment perception judgment, and relates to the technical field of path navigation, and the method comprises the steps: continuously perceiving farmland topographic relief features, gradient changes, and agricultural machine current attitude and wheel speed information before and during the operation of an agricultural machine; identifying a terrain disturbance potential risk area based on the sensing data, and predicting the offset and rollover risk value of the terrain fluctuation to the agricultural machine trajectory; according to the method, a pre-correction model fusing terrain prediction data and agricultural machinery dynamic characteristics is called, a pre-correction path containing the terrain compensation amount is generated, and navigation control parameters are adjusted in advance, so that the pre-correction path containing the terrain compensation amount is actively generated by sensing terrain fluctuation and predicting risk and offset in advance; the problem of causal malposition of risk prior to control is avoided from the source.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention relates to the technical field of non-electrical variable control or regulation systems, and discloses a temperature rising blanket temperature closed-loop control method and system based on human body position recognition feedback, and the method comprises the steps: calculating a form compactness coefficient representing a heat dissipation boundary based on pressure data, and calling a mapping function to convert the form compactness coefficient into an equivalent thermal impedance estimated value; introducing the estimated value and dynamically constraining the forward channel gain of the temperature closed-loop controller according to a negative correlation strategy; according to the method, a real-time mapping mechanism of the contact topological characteristics and the control gain is constructed, so that the problem of model mismatch caused by body position change of the controlled object is solved; when the high-impedance contact state is detected, energy input is automatically reduced, the local heat accumulation effect is restrained, and the convergence and safety of the thermodynamic control process under the variable boundary condition are ensured.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention discloses a PID temperature control system and method for a semiconductor etching machine, and relates to the field of temperature control, and the system comprises a coupling relation construction module, a temperature sensing confidence module, a decoupling compensation module, a coupling relation dynamic updating module, and a transient temperature change control module. According to the invention, by constructing the coupling relation matrix and the inverse matrix thereof, decoupling control of multi-area temperature control is realized, cross interference is effectively suppressed, and the temperature control precision is improved. And a multi-point temperature measurement fusion and anti-interference algorithm is introduced, so that the temperature measurement accuracy and the system robustness are enhanced. A response residual vector is associated with a control vector, and a coupling matrix is dynamically optimized, so that the system has self-learning and model self-adaption capabilities and adapts to working condition changes. And a transient temperature change quick response mechanism is set, so that the control priority can be improved and the PID parameters can be adjusted when the temperature changes suddenly, the response speed and the control stability of the system to abnormal temperature change are remarkably improved, and the thermal uniformity and the process consistency in the etching process are ensured.

The invention discloses a method and a system for adjusting a tea leaf fixation process and a medium, and aims to realize cooperative regulation and control of enzyme activity and aroma substances in a fixation process. The method comprises the following steps: collecting multi-modal sensing information in a fixation process, and carrying out feature extraction on the multi-modal sensing information to obtain multi-dimensional features; dynamically constructing an LSTM prediction model based on the multi-dimensional features, and outputting an enzyme activity prediction value sequence; the enzyme activity predicted value sequence is input into a fuzzy PID controller for reinforcement learning optimization, a temperature-rotating speed adjusting instruction is generated, and adjusting parameters of the controller are dynamically updated through a reward function of the chlorophyll retention rate and the energy consumption ratio; according to the temperature-rotating speed adjusting instruction, adjusting the hot air temperature of each temperature area of the enzyme deactivation chamber, the rotating speed of the enzyme deactivation cylinder and the axial segmentation wind speed; and collecting fixation end point judgment information, and terminating fixation when a preset condition is met. According to the invention, the problems of unstable quality and overhigh energy consumption caused by parameter solidification in the traditional enzyme deactivation process are solved, and dynamic optimization and accurate control of the enzyme deactivation process are realized.

The invention relates to a deformation online measurement and control method for multi-robot collaborative assembly. The method comprises the steps that multi-view surface images of workpieces in the assembly process are collected in real time through distributed robots and cameras arranged at the tail ends of the distributed robots; each view angle surface image is input into a deep learning model trained based on a digital image related technology, the optimal pixel displacement corresponding to each view angle surface image is output, and the optimal pixel displacement corresponding to each view angle surface image is converted into a spatial displacement label; the deep learning model takes an encoder-decoder as a trunk network; fusing the spatial displacement labels corresponding to the view angle surface images to obtain a fused displacement field; based on the fusion displacement field and the nominal path planning point, the tail end pose of the distributed robot is determined; the assembly error is calculated based on the reference target positioning and the tail end pose, and the PID controller adjusts the joint space of the distributed robot based on the assembly error. According to the method, the calculation overhead is remarkably reduced, and the measurement precision and robustness are improved.

The invention belongs to the technical field of intelligent home control, and particularly relates to an electric blanket intelligent temperature control system, which analyzes the matching relationship between the heating power and the temperature response in the historical operation cycle of a plurality of independent subareas of an electric blanket, fuses the thermal coupling effect between the subareas, and independently quantifies the dynamic thermal inertia parameter of each subarea. Based on the independent temperature change trend and the real-time temperature deviation of each partition, a control state including a steady state, a transient state or a strong compensation state is decided and maintained for each partition, the dynamic thermal inertia parameter, the real-time temperature deviation and the current control state are integrated, differentiated feed-forward compensation amounts are generated and superposed to the output of a PID controller of the corresponding partition, and the control state of the corresponding partition is calculated. A final power control instruction is generated after amplitude limiting and smooth filtering, temperature response lag caused by thermal inertia is actively predicted and counteracted, precise control is achieved in combination with partition state recognition, and the system response speed and control stability are remarkably improved.

The invention discloses a fishery pond water temperature control optimization method based on a swarm intelligence algorithm, and belongs to the technical field of control optimization, and the method comprises the following steps: S1, constructing a fishery pond water temperature PID control system introducing an improved single-corner whale optimization algorithm module; s2, introducing an improved single-corner whale optimization algorithm, and constructing an improved single-corner whale optimization algorithm module; s3, setting and optimizing the pond water temperature PID control parameters by using an improved single-corner whale optimization algorithm to obtain optimal control parameters; and S4, setting an optimal control parameter obtained through optimization by using the improved single-corner whale optimization algorithm as a parameter of a pool water temperature PID controller, and optimizing a pool water temperature regulation control effect. According to the method, a swarm intelligent optimization algorithm, namely an improved unicorn whale optimization algorithm, is introduced, high-performance control over the pond water temperature is achieved, and reliable control technical support is provided for efficient, stable and safe operation of aquaculture.

The invention relates to the technical field of industrial process automation control, and discloses an intelligent adjusting method for an industrial waste liquid low-temperature evaporation process, which comprises the following steps of: superposing pressure step test excitation to a vacuum adjusting valve, synchronously acquiring temperature response data flow, calculating absolute error integral and SIAE and reply time characteristics of a response curve, and calculating the temperature response data flow according to the absolute error integral and SIAE and reply time characteristics of the response curve; substituting into an inverse model to solve an equivalent process gain and a time constant of the controlled object; based on the internal model control principle, PID controller parameters are dynamically calculated and updated through the calculated gain and the time constant, a control law is reconstructed to generate an execution instruction, and the problem of control divergence of a non-linear time-varying object under model mismatch is solved through active energy feature identification and a pole assignment mechanism; decoupling control and self-adaptive adjustment of fluid viscosity sudden change and equipment scaling are achieved, and the stability and control precision of the system are improved.

The invention discloses a decoupling control method for motion coupling between a track and a propeller, relates to the technical field of robot control, and realizes accurate and intelligent regulation and control of motion of a sea-land amphibious robot by constructing a complete closed-loop strategy from sensing, recognition, prediction to control. And the motion stability of the robot in a complex mixed environment is obviously improved. According to the method, the undesirable coupling effect between the crawler belt and the propeller can be anticipated and quantified in a prospective mode, suppression is conducted actively through self-adaptive control, impact, vibration and attitude instability caused by power conflicts in land and water transition or rugged terrain are effectively avoided, and stability and safety of a robot platform and loads of the robot platform are guaranteed. The energy utilization efficiency of the robot is greatly improved. And it is ensured that the two power systems work in the optimal cooperative state at any moment, and invalid energy internal consumption and redundant output are avoided.

The invention relates to the technical field of control of manufacturing units, in particular to a composite material rod piece winding tension dynamic compensation control method and system. The method comprises the following steps of: calculating an instantaneous linear velocity pulsation quantity caused by the change of a non-circular section contour of a core mold at each moment; according to the dynamic elastic modulus and the equivalent viscosity coefficient, calculating a phase correction factor for representing the viscoelastic response lag of the prepreg tape at each moment; based on the instantaneous linear velocity pulsation quantity and the phase correction factor at each moment, a dynamic compensation torque instruction is obtained, and the dynamic compensation torque instruction is in negative correlation with the phase correction factor; and the dynamic compensation torque instruction is output to the tension unwinding motor, so that the adjustment of winding tension is realized, the accuracy of tension dynamic compensation control is effectively improved, and the product quality is effectively improved.

The invention discloses a Taylor space external fixation support PID control method based on an improved South goose algorithm, and belongs to the technical field of PID control, and the method specifically comprises the steps: 1, building a Taylor space external fixation support control model based on a PID controller; step 2, improving a South goose algorithm, including introducing boundary excitation and a chaotic elite strategy to initialize a population so as to give consideration to global and boundary exploration; an elitist retention and worst reset mechanism is adopted to ensure excellent solution inheritance and diversity; the global search efficiency is improved by utilizing a golden proportion reverse exploration method; multi-directional Levy flight is introduced to enhance the local optimal jumping capability; step 3, optimizing PID controller parameters by using an improved South goose algorithm to obtain optimal Kp, Ki and Kd; 4, simulating a PID (Proportion Integration Differentiation) controller of the control model by adopting MATLAB (Matrix Laboratory) and Simulink; the improved Swane algorithm can improve the adaptive control capability of the system, and enhance the stability and control precision.

The invention relates to the technical field of environmental engineering water treatment automatic control, and discloses a landfill leachate treatment control method based on cooperative control. The method comprises the following steps: acquiring real-time monitored water quality parameter data streams including a pretreatment stage pH value, a main treatment stage COD concentration, a deep treatment stage membrane pressure difference and the like in a landfill leachate treatment system, inputting the data streams into a cooperative control framework, and dynamically correcting PID controller parameters through a nonlinear gain function to generate a primary adjustment instruction; evaluating and generating a strategy optimization instruction based on the reinforcement learning strategy network; inputting the two into a grey prediction model and outputting a cooperative control signal containing a feedforward compensation amount; and finally, dynamically adjusting the coagulant dosage, the aeration intensity and the working pressure of the membrane component according to the signal to form a closed-loop control loop. The method can master the water quality change in real time, accurately regulate and control the treatment process, improve the stability and reliability of the treatment system, and effectively improve the landfill leachate treatment effect.

The invention relates to the technical field of automatic control, and discloses a PID (Proportion Integration Differentiation) intelligent pressure reducer controller, which comprises the following steps of: judging whether an environment disturbance or a load sudden change event exists according to a system load change rate and sensor feedback stability, constructing a master control-controlled coupling characteristic matrix by adopting a dual-channel data fusion method, performing attribution splitting on each disturbance channel, and obtaining a PID (Proportion Integration Differentiation) intelligent pressure reducer. PID parameters are dynamically adjusted according to the principal component weight of a disturbance channel, a variable parameter PID adjustment matrix is constructed, filtering smoothing is carried out on sampled signals through an extended Kalman filtering algorithm, a feedback closed-loop correction vector based on sampling residual errors is constructed, a historical offset tracking and switching value boundary judgment mechanism is introduced, fine tuning suppression is carried out on control signals of a motor driving mechanism, and the control signals of the motor driving mechanism are output. Judging whether a mechanical limiting protection device needs to be activated or not; and if yes, automatically triggering voltage limiting protection logic and locking the current control quantity. The method has the advantage of improving efficient and stable operation.

The invention relates to the technical field of battery testing and environment control, in particular to a lithium oxygen battery testing environment regulation and control system, and aims to solve the problems that in lithium oxygen battery testing, environment parameter regulation and control response is lagged, precision is insufficient, and multi-parameter collaborative decoupling is poor. The system is a combination of a battery test unit, a gas environment regulation and sensing module and an intelligent cooperative control unit. A multivariable decoupling algorithm is embedded in the intelligent cooperative control unit, and dynamic high-precision, wide-range and quick-response cooperative regulation and control of the oxygen concentration, humidity and pressure of the cavity are achieved. The system improves the precision and stability of environment regulation and control, and provides a reliable test environment for lithium oxygen battery research.

The invention relates to the technical field of robot control, and discloses a mechanical arm trajectory planning control method and system based on a BAFARNN model. The method comprises the steps that a mechanical arm kinematics model is established, and a trajectory tracking problem is converted into a time-varying equation; designing a bounded adaptive function to activate a recurrent neural network model, defining an error function and constructing a dynamic equation; designing a piecewise adaptive coefficient function, and dynamically adjusting the gain according to an error norm and time; setting a Lissajous curve as an expected trajectory, and initializing a simulation environment; the joint speed is solved in real time through an ODE numerical method, and the mechanical arm is driven to move; actual motion data is collected and compared with an instruction, and closed-loop feedback control is triggered when the actual motion data exceed a threshold value. According to the method, rapid convergence is achieved through the piecewise adaptive coefficient function, the bounded activation function and the negative feedback mechanism are adopted to suppress noise, and high-precision and real-time trajectory tracking of the mechanical arm in the dynamic environment is achieved.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention particularly relates to a discharge machining simulation control system for a nuclear power plant in a high-radiation boron-containing water environment, which belongs to the technical field of special maintenance in the high-radiation environment of the nuclear power plant and comprises a man-machine interaction system, a discharge machining control system and subsystems. Each subsystem comprises a pulse power supply system, a servo power system, a washing system, a purification system, a grounding system and a tool system; the man-machine interaction system is used for man-machine interaction and displaying the discharge machining implementation data fed back by the subsystems; the electric discharge machining control system is used for generating an electric discharge machining instruction according to the man-machine interaction information and controlling the subsystems to cooperate to perform electric discharge machining work; and each subsystem is used for implementing electric discharge machining work according to the electric discharge machining instruction and feeding back electric discharge machining implementation data. According to the invention, simulation control of discharge processing in the high-radiation boron-containing water environment of the nuclear power plant is realized.

A gate linkage control method and device and a parallel water supply and power generation system are provided. The method comprises the following steps of: acquiring current gate opening degree information of the parallel water supply and power generation system and a current water level of a third trunk canal, the current gate opening degree information comprising current first gate opening degree information and current second gate opening degree information; determining a target gate linkage control strategy of the parallel water supply and power generation system according to the current gate opening degree information of the parallel water supply and power generation system and the current water level of the third trunk canal; and controlling a gate opening degree of a target gate according to the target gate linkage control strategy of the parallel water supply and power generation system.

The invention discloses an offshore rocket recovery platform attitude stability control method based on particle swarm optimization, and the method comprises the following steps: S1, collecting and preprocessing multi-source attitude data, and generating a time series data set; s2, constructing a fuzzy PID controller, and setting nine three-axis control parameters; s3, performing global optimization by adopting an improved particle swarm algorithm, and outputting an initial parameter solution; s4, introducing a zebra optimization algorithm to carry out local refined optimization; s5, constructing a collaborative optimization architecture, and fusing and outputting optimal control parameters; s6, the optimal parameters are input into a controller, and a servo system is driven to adjust the three-axis attitude; s7, monitoring disturbance amplitude, dynamically triggering a zebra strategy and feeding back the zebra strategy to the particle swarm; and S8, evaluating the control performance, and feeding back iterative optimization if the control performance does not reach the standard. According to the method, the improved particle swarm optimization algorithm and the zebra optimization algorithm are fused, so that self-adaptive optimization and accurate attitude stable control of attitude control parameters of the offshore rocket recovery platform are realized.

The invention discloses a joint motor control method and system based on an angle sensor, a medium and a product, and relates to the field of general control or adjustment systems.The method comprises the steps that real-time angle data and temperature data of the angle sensor are collected, and a real-time data packet is generated; determining a rapid compensation parameter corresponding to the temperature value, performing preliminary compensation on the angle value to obtain a preliminary compensation angle value, and generating a motor control parameter to control a joint motor to operate; storing a plurality of continuous real-time data packets into a compensation cache region; when the number of the data packets in the compensation cache region reaches a preset number threshold value, temperature change features and angle drift features are extracted, and accurate compensation parameters are calculated; and performing secondary compensation on the preliminary compensation angle value based on the accurate compensation parameter to obtain an accurate compensation angle value, and updating the motor control parameter based on the accurate compensation angle value. According to the invention, response delay caused by a compensation algorithm can be reduced.

The invention relates to an intelligent control method and system for extrusion forming of an aluminum profile, and the method comprises the following steps: carrying out acoustic emission sensor array measurement and preprocessing on a mold inlet region of the aluminum profile in an extrusion process, and obtaining a preprocessed acoustic emission signal sequence; performing time-frequency domain energy distribution analysis on the acoustic emission spectrum to generate an acoustic emission energy spectrogram; carrying out material flow stability analysis based on the spectrogram to obtain a flow analysis result; if the flow instability degree exceeds the target stability range, multi-constraint optimization calculation is conducted on the main cylinder pressure and the extrusion speed based on the analysis result, and optimization control parameters are obtained; and finally, closed-loop feedback control is performed on the extruder through a distributed PID controller, so that the material flow stability is kept in a target range, intelligent high-precision forming control is realized, and the problem that in the traditional extrusion process, the material flow stability is often influenced by factors such as the extrusion speed, the main cylinder pressure and the mold design is solved. And a flow instability phenomenon is easy to occur.