83 results about "Fuzzy adaptive control" patented technology

The invention relates to the technical field of temperature control, in particular to a mass concrete water cooling water temperature control method and system. The method comprises the following steps: collecting concrete key parameters through a multi-source sensor, constructing a microenvironment model based on information geometry, and generating a thermosensitive characteristic spectrum; forming a distributed thermodynamic model by utilizing a mutual information routing algorithm and heat-fluid-solid coupling analysis, and calculating an optimal cooling strategy; water flow path and flow optimization is realized in combination with fractional order particle swarm optimization; a pump set and a valve are adjusted through fuzzy self-adaptive control; and a potential instability region is identified through the spectrum energy gradient, and risk early warning is triggered. The precision and reliability of mass concrete cooling temperature control are remarkably improved, and intelligent, efficient and safe temperature control adjustment in the whole process is achieved.

The invention discloses a focusing control method and related equipment, and the method comprises the steps: synchronously collecting the data of a multi-modal sensor, extracting features and parameters, inputting a multi-modal fusion model subjected to joint optimization to generate a motor control instruction, and achieving the focusing through the hierarchical control of rough adjustment, fine adjustment and dynamic tracking. The dynamic weight fusion network deploys the weight of each modal data according to the environmental sensitivity coefficient, and combines the historical memory and trajectory pre-judgment of the alignment trajectory prediction network to reduce invalid calculation and repeated search and greatly improve the focusing speed; the multi-mode sensor works cooperatively to replace a special hardware function, so that the cost and the power consumption are reduced; the dynamic weight fusion network can adjust the modal confidence according to the environment and enhance the anti-interference capability in the complex light environment; the alignment trajectory prediction network and the fuzzy self-adaptive PID controller are in hierarchical cooperation, the problem of erroneous judgment of a complex scene is solved, and the micro-distance and dynamic tracking precision is improved; the model is subjected to pruning, distillation and quantitative optimization, light weight is achieved, and low-cost equipment is adapted.

The invention discloses a mechanical arm control method and system based on fuzzy self-adaptive PID control, relates to the technical field of mechanical arm control, and solves the problem that mechanical arm control is insufficient in flexibility when facing different working conditions. According to the embodiment of the invention, the universe expansion and contraction factor is adjusted through the error, then the close linkage of the quantization factor and universe expansion and contraction is realized through the self-adaptive quantization factor calculation, the fuzzy reasoning technology is combined, the intelligent mapping from the error information to the PID parameter increment is realized, the real-time updating of the PID parameter and the control output calculation are realized, and the real-time updating of the PID parameter is realized. The flexibility and adaptability of the control system are improved; and then output is controlled through incremental PID, so that a complete control link from parameter increment to actuator driving is realized, a complete closed loop is formed by a cascade control architecture, functional modules are tightly coupled, parameters are coordinated, and the whole-process intelligence from error sensing to execution control is realized.

The invention discloses a UPA pressure automatic compensation system and method for mask substrate polishing. The UPA pressure automatic compensation system comprises a pressure execution module, a distributed sensing feedback network and an intelligent control unit. Wherein the pressure execution module is used for directly polishing a mask base plate and comprises a force application shaft, a polishing head, an air source, a UPA air bag and an electric proportional valve, the distributed sensing feedback network comprises an air path pressure sensor and a tail end contact pressure sensor, and the intelligent control unit receives a collection signal of the distributed sensing feedback network and sends the collection signal to the polishing head. On the basis of a current process formula and process parameter coupling model, a data fusion algorithm, a feed-forward compensation algorithm and a fuzzy PID algorithm are operated to obtain a feed-forward compensation amount and a fuzzy PID output amount, the feed-forward compensation amount and the fuzzy PID output amount are superposed, a total control voltage is generated and output to an electric proportional valve, and then the input air pressure of a UPA air bag is adjusted. Therefore, closed-loop control over the polishing pressure of the mask substrate is achieved. Through multi-sensor data fusion, fuzzy PID self-adaptive control and intelligent feed-forward compensation based on a process parameter coupling model, real-time, accurate and constant control of the polishing pressure is realized, so that the surface precision and yield of the mask substrate are improved.

The invention provides a compensation balance system and method of an electrodynamic transducer, and aims to solve the following problems in the prior art: a traditional electrodynamic sound source transducer needs to realize large-volume displacement in a low-frequency application scene, but the flexibility of an elastic suspension support of the traditional electrodynamic sound source transducer is insufficient to bear hydrostatic pressure; meanwhile, traditional passive and active compensation modes have challenges in the aspects of delay and stability. In order to solve the problems, the compensation balance system and method of the electrodynamic transducer mainly comprise an underwater acoustic transducer, a low-pressure middle cabin and a high-pressure gas cylinder, and a system self-checking function, a transducer piston position balance control function and a transducer fuzzy self-adaptive control function are integrated. The defects in the prior art are effectively overcome, and balance between the inner cavity of the transducer and the external hydrostatic pressure can be rapidly achieved. In addition, the control process is further simplified by determining the target control quantity, and the overall performance of the system is improved.

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 relates to the technical field of ship stabilization control, and discloses a rudder fin combined stabilization system cooperative control method comprising the following steps: 1, building a ship motion model; step 2, designing a fuzzy adaptive PID controller; 3, constructing a rudder fin interaction model; and 4, introducing a prediction control algorithm, and optimizing a control input sequence based on a model prediction principle. By establishing a ship motion model, designing a fuzzy self-adaptive PID controller, analyzing rudder fin interaction and introducing a predictive control algorithm, the problems that an existing rudder fin combined stabilization system is insufficient in cooperative control, in the ship navigation process, a ship is affected by external factors such as sea waves and sea wind, the ship is prone to swing motion, and the stability of the ship is poor are solved. Therefore, the stabilization effect and the course stability of the ship are improved.

The invention discloses a harmonic suppression and dynamic reactive compensation control method for an energy router, which is suitable for a micro-grid and a distributed energy access system. According to the method, three-phase current and voltage signals of an alternating current port of the energy router are collected, five-time and seven-time main harmonic components are extracted through fast Fourier transform, harmonic compensation current instructions equal in amplitude and opposite in phase are generated and injected into an inverter to be output, and active counteracting of main harmonics is achieved. Meanwhile, the bus voltage deviation and the change rate thereof are detected, a reactive current instruction is adjusted on line by adopting a fuzzy self-adaptive PI controller, rapid and smooth dynamic reactive compensation is realized, and the bus voltage is stabilized. Decoupling control of active / reactive and harmonic components is realized in combination with Park conversion, and virtual impedance is introduced to enhance system damping and harmonic suppression capabilities. Compared with the prior art, harmonic suppression and voltage support can be simultaneously realized under a single control framework, the response speed is high, the stability is high, and the method is suitable for high-performance grid-connected operation of the multi-port energy router.

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

The invention provides an airway atomization flow intelligent drug delivery control method and system, and relates to the technical field of medical instruments.The method comprises the steps that basic information of the age, the weight and the illness state severity of a patient is obtained, and physiological parameters of the respiratory rate and the tidal volume of the patient are collected in real time through a sensor; inputting the basic information and the physiological parameters into a fuzzy adaptive control algorithm, and initializing an individual administration rule base according to the basic information; based on the real-time change trend of the respiratory rate and the tidal volume, three initial control index values including an atomization flow value, a drug concentration value and a time interval are dynamically generated through a membership function. According to the invention, through the full-process design of data acquisition, intelligent modeling, accurate execution and data closed loop, intelligentization of atomization treatment is realized.

The invention belongs to the technical field of robot control, and discloses a fuzzy adaptive control method and system for uncertain robot system constraints. A dead zone model is integrated into an uncertain robot kinetic equation, a robot system module is established, feedback joint displacement state information is output in real time through the robot system module, and unmeasurable speed and acceleration state information is processed through a filter module, a virtual controller module and a self-adaptive law module. And a virtual control signal and a fuzzy adaptive law parameter are obtained, and dynamic change information is obtained through a fuzzy logic system in a fuzzy adaptive controller module. According to the method, parameter uncertainty caused by unknown robot system parameters or uncertainty and dead zone characteristics caused by non-controlled object factors such as external disturbance are solved, and accurate tracking control of the system state under boundary constraint is realized.

The invention provides a permanent magnet direct drive fan bus voltage oscillation suppression method after power grid fault recovery, and belongs to the technical field of permanent magnet direct drive fans. The technical problem of DC bus voltage oscillation after symmetric fault recovery is solved. According to the technical scheme, the method comprises the following steps that S1, the power difference value P between a computer side and a network side during a fault period and the maximum unbalanced power Pmax absorbed by the rotational inertia of a fan rotor are included, if P is smaller than or equal to Pmax, the step S2 is executed, and if P is larger than Pmax, the step S3 is executed; s2, controlling a direct-current bus voltage outer ring by a grid-side converter, and turning to S5; s3, a DC bus voltage outer ring is controlled by a fuzzy PI adaptive controller; and S4, carrying out fuzzy processing. According to the invention, adaptive adjustment of DC bus voltage outer loop PI parameters is realized, the dynamic response capability is enhanced, voltage oscillation is effectively suppressed, and the stability is improved.

The invention provides an unmanned aerial vehicle flight control method and device based on multi-sensor data fusion, and the method comprises the following steps: carrying out the denoising and clustering weight fusion processing of flight control initial data collected by multiple sensors, and generating fusion data; performing real-time state estimation on the fused data by adopting an extended state observer to obtain real-time flight state parameters; performing deviation compensation based on a deviation value between a preset target state and a real-time flight state parameter, optimizing a fuzzy adaptive PID control algorithm of parameters through a genetic algorithm, and dynamically adjusting the parameters of a control machine in a global optimization mode; and outputting the optimal control quantity to realize accurate flight control of the unmanned aerial vehicle. According to the method, the data confidence is improved through a clustering weighting mechanism, parameter global optimization is carried out in combination with disturbance observation of an extended state observer and a fuzzy adaptive PID control algorithm, and the problem that the flight control precision is insufficient due to the fact that a current unmanned aerial vehicle cannot realize control parameter global optimization is effectively solved.

The invention relates to the technical field of intelligent power grids, and provides a wind turbine generator comprehensive inertia frequency control method based on fuzzy adaptive parameters. In the method, an adaptive parameter method based on frequency and rotating speed is adopted in a frequency support stage of a wind turbine generator, and a comprehensive inertia frequency control parameter is dynamically corrected according to the proportion of residual adjustable kinetic energy of a fan to adjustable total kinetic energy and a first system frequency deviation and according to a first fuzzy adaptive control law; the maximum deviation of the frequency is reduced and the secondary drop of the frequency is relieved; in the rotating speed recovery stage, according to the proportion of the residual adjustable kinetic energy of the fan to the total adjustable kinetic energy and the frequency deviation of the second system, the frequency secondary drop and the rotating speed recovery duration are comprehensively considered, and the output power reference value of the wind turbine generator is dynamically corrected through fuzzy self-adaptive control; the output power reference value dynamically changes along with the steady-state frequency change after the fan frequency is close to the system disturbance, the frequency secondary drop is relieved, the rotating speed recovery time is prevented from being too long, and the system operation stability is improved.

The invention relates to the technical field of industrial automation and intelligent control, in particular to a centrifuge operation data real-time visualization monitoring system which comprises a distributed data acquisition unit, a data analysis and processing unit, a real-time monitoring and visualization unit, a decentralized control system unit and a remote access and support unit. According to the method, on-line incremental learning and hybrid modeling are fused with a physical mechanism and data-driven analysis, so that dynamic prediction, anomaly recognition and causal reasoning of the running state of the centrifugal machine are realized, and lightweight deployment of the edge side is supported; based on the multi-physics coupling real-time simulation and augmented reality technology, multi-phase flow state high-precision dynamic visualization and operation risk intelligent space early warning in the centrifugal process are achieved, and multi-parameter dynamic optimization regulation and control in the centrifugal process are achieved by means of a fuzzy self-adaptive control strategy and a distributed intelligent cooperation mechanism; and the intelligent analysis, real-time monitoring and accurate control capability and the complex working condition adaptability of the system to the operation of the centrifugal machine are obviously improved.

The invention discloses an active vibration absorber control strategy for vibration at the tail end of a flexible arm, and relates to the technical field of mechanical arm control, and the active vibration absorber control strategy comprises the following steps: building a three-dimensional model for a two-degree-of-freedom flexible mechanical arm, describing the joint flexibility of a system by using a linear spring, and representing the rigidity coefficient of a flexible joint by using the elastic coefficient of the linear spring; fuzzy RBF adaptive PID control is trained in combination with a real object, the identification precision of the model is improved, and vibration suppression is realized; in the fuzzy control, input variables are fuzzified by simulating the thinking mode of human beings, reasoning is carried out in combination with expert experience rules, and finally a system of an accurate dynamic model is established. According to the control strategy, the limitation of a traditional control technology based on a model during control is reduced, dynamic hysteresis nonlinearity generated in actual work is effectively corrected, the control precision is improved, fuzzy rule parameters are adjusted through modal analysis in combination with system characteristics, and the control precision is improved. Therefore, the vibration suppression force synthesis in different scenes is more accurate, and the vibration suppression effect is better.

The present application belongs to the technical field of seawater desalination equipment control, and relates to a hydraulic fan driven high-pressure water pump speed control method based on fuzzy adaptive PID, which comprises: S1, obtaining the high-pressure water pump speed error of the hydraulic fan, and determining the input variable of the fuzzy adaptive PID controller; S2, analyzing the stability of the hydraulic fan, establishing a high-pressure water pump variable motor swing angle control model, and determining the output variable of the fuzzy adaptive PID controller; S3, determining the fuzzy control set of the fuzzy adaptive PID controller of the hydraulic fan; S4, using the Mamdani method to perform fuzzy reasoning on the fuzzy reasoning table, and calculating the fuzzy control subset by the barycenter method; and S5, controlling the swing angle adjusting variable motor displacement of the high-pressure water pump variable motor according to the output value of the fuzzy control subset. The present application is based on the hydraulic fan driven high-pressure water pump passing through the reverse osmosis membrane to produce fresh water, and uses fuzzy control and PID control theory to improve the rapidity, accuracy and stability of the water pump speed control.

The invention relates to the technical field of terminal sterilization and filtration in a radiopharmaceutical preparation process, in particular to a filter membrane sterilization and filtration device and system. Comprising the steps that a state sensing module collects liquid medicine radioactive activity data and filter membrane structure strain data and generates real-time data flow; the twinborn modeling module constructs a filter membrane digital twinborn body, and outputs an accumulated radiation damage prediction result through data assimilation; the prediction decision module generates a filtering parameter optimization sequence by using a deep reinforcement learning agent; the control execution module adjusts the flow velocity of the liquid medicine through fuzzy self-adaptive PID control and feed-forward compensation; and the verification feedback module performs an integrity test and transmits data back to the twin modeling module to realize model parameter correction. All the modules form a closed-loop control system through a high-speed data bus, so that the damage of radiation to a filter membrane material is effectively inhibited, and the integrity and the sterile guarantee level of a membrane structure in the filtering process are maintained.

The application discloses a numerical control machine tool control system based on big data, a data acquisition module acquires tool wear during production of the numerical control machine tool, load information of the machine tool during machining, machining precision error of workpiece machining and a machining precision error change rate; relates to the technical field of intelligent control, the system comprehensively utilizes big data and a fuzzy self-adaptive control algorithm, the fuzzy self-adaptive control algorithm determines a feed rate adjustment range based on fuzzy information of a machining state, selects a feed rate scheme with the highest fitness function in a scheme set containing all possible feed rates by setting the fitness function, and a feed rate correction adjustment module dynamically adjusts the feed rate according to actual machining precision, so that the difference between the feed rate scheme determined by the fitness function and actual machining is made up. Through the synergistic effect of these technologies, the machining precision and efficiency of the numerical control machine tool are effectively improved.

This application provides a method and system for intelligent detection and precise spraying control of fruit tree canopies, which is used to solve the problem of inaccurate spraying of liquid medicine in the existing technology. The specific steps of the method are: S1 collects image data of the side and top of the fruit tree canopy; S2 calculates the area and density of the side and top of the fruit tree canopy; S3 calculates the ground spraying decision coefficient K based on the area and density of the side and top of the fruit tree canopy g and aerial application decision coefficient K s , and calculates the ground and aerial application rates separately based on the application decision coefficient; S4 uses an ESO-based fuzzy adaptive control algorithm to accurately control the pipeline pressure and flow of ground and aerial application equipment. This application constructs an application rate calculation model by calculating the area and density of the sides and tops of fruit tree canopies, reducing the amount of pesticide applied to each fruit tree. At the same time, it uses an ESO-based fuzzy adaptive control algorithm to accurately control the pipeline dosage, improving the utilization rate of variable spray solution.

The application belongs to the technical field of power equipment maintenance and intelligent control, and discloses a handcart switch unlocking and closing device, a closed-loop regulating system is constructed through a fuzzy PID adaptive control unit and a multi-dimensional data acquisition unit, the mechanical characteristics of different models of handcart switches can be dynamically adapted, the closing / unlocking success rate is improved, the problems of mis-closing and jamming are effectively avoided; meanwhile, relying on a lightweight LSTM edge fault diagnosis unit, the device operation time sequence data can be analyzed in real time, the trend faults such as battery aging and poor relay contact can be identified in advance, predictive maintenance is realized, the device sudden failure rate is greatly reduced, the unplanned maintenance is reduced, and the service life of the device and the matched handcart switch is prolonged; the bidirectional energy recovery power supply unit realizes the conversion and recharge of mechanical energy into electrical energy through a four-switch Buck-Boost structure, the energy recovery efficiency is improved, and the device endurance time is prolonged.

The invention discloses an accurate and rapid fuzzy control method for a maritime lightering device, and the method comprises the following steps: S1, building a maritime lightering device kinematics equation based on kinematics analysis; s2, obtaining a basic driving signal of each execution mechanism by solving an inverse solution equation; s3, constructing a self-adaptive terminal sliding mode surface of the maritime lightering device; s4, combining the fuzzy controller with PID control to form a fuzzy adaptive PID control structure; and S5, forming a mixed architecture of an inverse solution feedforward channel and a layered terminal sliding mode feedback channel. According to the control method, the adaptive capacity is excellent, the fuzzy controller has the online self-adjusting capacity, the control strategy can be automatically adjusted according to different sea conditions, load changes and ship characteristics, the fuzzy controller and PID control are combined, a fuzzy adaptive PID control structure is formed, and intelligent control according to ship conditions and intelligent control according to sea conditions are achieved.

The invention relates to the field of intelligent manufacturing, in particular to an intelligent diluent concentration adjusting method and system. The intelligent diluent concentration blending system comprises a parameter setting and self-learning module, a controller initialization module, an intelligent control module and a closed-loop control module. According to the invention, by introducing deep fusion of a material characteristic self-learning mechanism and fuzzy adaptive PID control, full-automatic intelligent setting and optimization of control parameters are realized; the system can automatically trigger a self-learning process when mother liquor varieties are replaced, key dynamic characteristic parameters such as lag time, response time constants and steady-state gain coefficients of the system are identified in real time by analyzing concentration response data, and a fuzzy control rule base is automatically corrected based on the parameters; and the controller has the capability of adapting to different material characteristics.

The present application belongs to the technical field of robot control, and discloses a fuzzy adaptive control method and system for constraints of an uncertain robot system. A dead zone model is integrated into a robot dynamics equation, a robot system module is established, real-time output of joint displacement state information can be fed back through the robot system module, unmeasurable speed and acceleration state information is processed through a filter module, a virtual controller module and an adaptive law module, and a virtual control signal and a fuzzy adaptive law parameter are obtained, and dynamic change information is obtained through a fuzzy logic system in a fuzzy adaptive controller module. The present application solves the parameter uncertainty caused by unknown parameters of a robot system or the uncertainty caused by external disturbances and dead zone characteristics of non-controlled objects, and realizes accurate tracking control of system state under boundary constraints.

The method comprises the following steps: extracting frequency information of power grid voltage based on a characteristic harmonic compensation method of an improved STATCOM to obtain a reference phase and frequency of h-order harmonic; the load current is converted into a d-q coordinate system through a Cabc-dqh change matrix module and decomposed into a load active current component and a load reactive current component, and then a reference current instruction value is obtained through low-pass filtering; performing low-pass filtering on the active current component and the reactive current component to obtain a system side current component as a reference value; comparing each separated harmonic current component with a reference value, and inputting the current components to a fuzzy PI controller to obtain voltage compensation instructions of the characteristic harmonic waves under a d axis and a q axis respectively; and superposing each compensation instruction and the original reactive compensation and direct-current voltage control instruction of the STATCOM, and then inputting PWM (Pulse Width Modulation) to enable the STATCOM to output current containing a characteristic harmonic compensation component. According to the method, multi-synchronous rotating coordinate system detection and fuzzy PI adaptive control are introduced, and dynamic and accurate compensation of multiple characteristic harmonics is realized on the premise of not changing the main circuit topology of the STATCOM.

The present application relates to the field of robot control, especially to a preset performance tracking control method for quadruped collaborative robots based on Nash game, comprising the following steps: establishing a dynamic model of a six-axis robot arm system, and introducing fuzzy set theory to deal with bounded uncertainty in the system; using cycloid curve to generate a smooth desired trajectory, and proposing a preset performance optimal tracking control method to express the desired trajectory in the form of an equality servo constraint; obtaining the control parameters of the system according to the constraint, and establishing a fuzzy adaptive controller according to the control parameters to realize accurate tracking of the error; converting the design of the control parameters into a two-person non-cooperative game, and balancing the system indicators through the Nash equilibrium method to obtain the optimal control parameters. The present application improves the system by adding the Nash game method on the basis of accurate tracking control, improves the fuzzy-based performance through optimal design, and seeks the optimal value of the control parameters by constructing a two-player Nash game.

The invention belongs to the technical field of trajectory tracking control of multiple unmanned aerial vehicles, and discloses a fuzzy adaptive control method and system for preset performance and preset time of multiple unmanned aerial vehicles. A kinetic model of each follower considering external interference and unknown parameters of a system model is given and simplified, and then trajectory tracking errors of the unmanned aerial vehicle formation are constrained within a preset performance function boundary range through a preset performance control and error conversion method. The uncertainty in the multi-unmanned aerial vehicle system is processed by adopting a fuzzy adaptive technology, and a preset time fuzzy adaptive controller based on preset performance is constructed by using an instruction filtering backstepping design method and a preset time control theory, so that trajectory tracking control of the multi-unmanned aerial vehicle system is realized. According to the control method provided by the invention, the unmanned aerial vehicle can track the preset expected signal within the preset time, and the preset time of all the signals in the closed-loop system is bounded.

The invention discloses a fuzzy adaptive control method for reduced order output of an arm support electro-hydraulic execution system, and belongs to the technical field of arm support electro-hydraulic execution system control. The method comprises the steps that firstly, a full-state model of a system is established for a boom electro-hydraulic execution system, secondly, order reduction processing is carried out based on the singular perturbation theory, and an order reduction model is obtained; then, designing a state observer based on the reduced-order model, and constructing a fuzzy adaptive state observer in combination with a fuzzy adaptive strategy; and finally, designing a fuzzy adaptive sliding mode controller based on the reduced-order model, and combining the fuzzy adaptive sliding mode controller with a fuzzy adaptive state observer to obtain a reduced-order output fuzzy adaptive controller. According to the method, the system state is observed through the fuzzy adaptive state observer, and system adaptive control is carried out, so that the robustness of the system can be effectively improved.

This invention belongs to the field of badminton racket stringing technology and discloses a tension closed-loop control and trajectory planning method and system for automatic badminton racket stringing. It constructs a nonlinear coupled mechanical model of string tension, racket frame deformation, and the coupling of horizontal and vertical strings, and combines finite element simulation to quantify various tension losses, generating a globally differentiated tension distribution strategy. Simultaneously, it relies on multi-source sensors to collect full-dimensional data, and uses a fuzzy PID adaptive control algorithm to achieve tension closed-loop compensation. It also sets up a racket frame deformation early warning protection mechanism to compensate for tension deviations caused by friction and deformation. Based on the racket's three-dimensional point cloud data obtained from binocular vision and line laser scanning, it generates a string hole topology correlation matrix, and combines a multi-axis kinematic model with an improved A* algorithm to complete multi-objective optimized trajectory planning. It can not only adaptively plan stringing paths according to rackets with different frame types, materials, and hole distributions, but also design a symmetrical stringing topology sequence and a phased S-shaped motion curve.

The invention relates to a fuzzy PID adaptive control method for an auxiliary fan in a fan performance test system, and belongs to the technical field of fan performance test and control. In the performance test system, the target air volume and the actual air volume are collected in real time, the air volume deviation and the change rate of the air volume deviation serve as fuzzy reasoning input, PID parameters are set on line, and the PID parameters are used for driving an auxiliary fan to achieve self-adaptive adjustment. According to the method, proportion, integral and differential coefficients are dynamically corrected by using fuzzy logic rules, so that the auxiliary fan can quickly adjust and stabilize the air volume under the condition of working condition change or disturbance, fluctuation and deviation are reduced, and the air volume is ensured to be accurate and controllable in the test process. Therefore, the efficiency and consistency of working condition establishment of the fan performance test system are remarkably improved, and a reliable control means is provided for high-precision measurement of performance parameters and automation of the test process.