57 results about "Fuzzy adaptive control" patented technology

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 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 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 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.

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 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.

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 present application relates to the field of intelligent manufacturing, and particularly relates to a diluent concentration intelligent matching method and system.A diluent concentration intelligent matching system comprises a parameter setting and self-learning module, a controller initialization module, an intelligent control module and a closed-loop control module.The present application realizes full-automatic intelligent setting and optimization of control parameters by introducing deep fusion of a material characteristic self-learning mechanism and fuzzy adaptive PID control;the system can automatically trigger a self-learning process when a mother liquor variety is replaced, identify key dynamic characteristic parameters such as lag time, response time constant and steady-state gain coefficient of the system in real time by analyzing concentration response data, and automatically correct a fuzzy control rule base based on these parameters, so that the controller has the ability to adapt to different material characteristics.

The permanent magnet synchronous motor parameter identification method based on fuzzy adaptive control has the following steps: inputting a frequency value f0, an amplitude value, and a bias value of a d-axis current signal at a set angle, performing PI setting of a current loop, and obtaining K p , K i of the current loop; giving a d-axis step current instruction, adopting a mean value and difference method to obtain an identification resistance; giving a d-axis sinusoidal current instruction, and utilizing a least square method with a forgetting factor to obtain an identification d-axis inductance; giving a q-axis sinusoidal current instruction, and obtaining an identification q-axis inductance according to a q-axis voltage, a q-axis feedback current and the identification resistance; and giving a q-axis step current instruction, and obtaining a flux according to the q-axis voltage, the q-axis feedback current, a motor rotating angular velocity and the identification resistance. The method is a closed-loop offline parameter identification method, and has the advantages of simple and reliable identification process, universality, controllable current, high safety, no motor rotation except for identification of the flux, and applicability to different occasions.

This invention discloses a rail grinding system and method for a rapid rail grinding vehicle, belonging to the field of rail maintenance equipment control technology. The system includes a grinding control module and a winding subsystem, a pressure loading subsystem, a travel coordination subsystem, a host computer interaction subsystem, and a remote control subsystem, all communicatively connected to the grinding control module. This invention achieves high-precision, highly disturbance-resistant decoupled control of the abrasive belt speed and tension by integrating fuzzy adaptive LQR control with a Luneburger observer, effectively suppressing the influence of winding diameter changes and external disturbances. The pressure loading system employs a self-disturbance-resistant fuzzy variable impedance algorithm to dynamically compensate for pressure fluctuations caused by rail irregularities, ensuring constant force grinding and significantly improving the consistency of the grinding profile and surface quality. It features both host computer and remote control operation modes, exhibiting a high degree of intelligence. This invention solves the industry problems of low operating efficiency, over-cutting, or under-grinding of existing grinding vehicles, achieving efficient, high-quality, and rapid rail grinding and maintenance.

The invention discloses a multi-six-rotor unmanned aerial vehicle attitude consistency dynamic triggering safety control system, and belongs to the technical field of unmanned aerial vehicle control. Comprising a multi-six-rotor attitude model, a delay funnel performance module, a fuzzy system module, an adaptive law module, an intermediate controller module, an event trigger mechanism module, an attitude consistency control module and an FDI attack module. The delay funnel performance module is designed, and the problems of initial feasibility condition limitation and performance self-adjustment are solved through an offset transfer function and a self-adjustment performance function; then processing nonlinearity, external interference and random FDI attacks based on backstepping design and fuzzy adaptive control in combination with a second-order tracking differentiator, and avoiding'calculation explosion '; and finally, designing a dynamic event triggering mechanism to reduce communication and calculation overhead. According to the method provided by the invention, the communication frequency is greatly reduced on the premise of ensuring the attitude tracking precision, and the method is superior to a traditional attitude control method.

The invention discloses a verification method for a waist rehabilitation robot control system based on multi-platform collaborative simulation, belongs to the field of rehabilitation robots, is suitable for a 3-RPS parallel mechanism waist rehabilitation robot, and can also be popularized to a complex parallel system. According to the method, a SOLIDWORKS-ADAMS-MATLAB-OPENSIM co-simulation platform is constructed, after three-dimensional modeling and dynamics and inverse kinematics analysis are carried out, an ADAMS and MATLAB / SIMULINK co-simulation interface is constructed, a fuzzy PID self-adaptive control system is designed, and real-time interaction of a control algorithm and a mechanical system is achieved; and in combination with typical rehabilitation action simulation, human body motion data and OPENSIM man-machine coupling analysis, effectiveness and robustness of a control strategy are verified. The method can reduce the model machine cost, shorten the research and development period, improve the trajectory tracking precision and the anti-interference capability, and guarantee the safety and reliability of rehabilitation training.

This invention discloses a motion control method and system for an underwater pipeline inspection robot. The method includes: calculating the robot's current actual motion state based on original observation data; using inspection quality indicators as input, substituting them into an observation model, and inversely solving to obtain the optimal inspection pose that satisfies the inspection quality indicators, thereby obtaining the ideal motion state; comparing the ideal motion state with the actual motion state to calculate multi-dimensional control errors; inputting the multi-dimensional control errors into a PID controller to generate fuzzy adaptive control variables for different actuators, and inputting them into a PMAC multi-axis motion controller; combining dynamic constraint models and geometric constraints to perform multi-axis interpolation planning and generate servo control pulses; collecting observation data from sensor modules in real time driven by the servo control pulses and updating the current actual motion state. This invention improves the reliability and inspection accuracy of underwater pipeline inspections in complex environments.

The present application relates to the technical field of smart grid, and provides a wind turbine comprehensive inertia frequency control method based on fuzzy adaptive parameters. In the method, the adaptive parameter method based on frequency and speed is adopted in the wind turbine frequency support stage, the first fuzzy adaptive control law is used to dynamically correct the comprehensive inertia frequency control parameters according to the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the first system frequency deviation, the maximum frequency deviation is reduced, and the frequency secondary drop is relieved; in the speed recovery stage, the wind turbine output power reference value is dynamically corrected by the fuzzy adaptive control according to the proportion of the remaining adjustable kinetic energy of the wind turbine to the total adjustable kinetic energy and the second system frequency deviation, the frequency secondary drop and the speed recovery time are comprehensively considered, the output power reference value dynamically changes with the wind turbine frequency approaching the steady-state frequency after the system disturbance, the frequency secondary drop is relieved, the speed recovery time is avoided to be too long, and the system operation stability is improved.

The invention provides a fuzzy self-adaptive Q learning control method and system for sewage treatment, and the method comprises the steps: obtaining the dissolved oxygen concentration as a system state, and constructing a nonlinear optimization problem; performing dynamic adjustment on proportion, integral and differential coefficients by adopting a Mamdani type fuzzy inference rule in combination with fuzzy logic, performing defuzzification through a centroid method to obtain adaptive PID parameters, and forming a fuzzy adaptive control strategy; an online Q learning framework is further constructed, a Q function is approximated by using the evaluation network, a network generation strategy adjustment amount is executed, and network weight is optimized based on a Bellman equation and a particle swarm algorithm; finally, fuzzy control and Q learning output are fused, control input is generated through a coupling coefficient to adjust an oxygen transfer coefficient, and tracking control over the dissolved oxygen concentration is achieved. According to the invention, the tracking control precision of the dissolved oxygen concentration and the system operation stability can be improved.

The application discloses a linkage control method for a water energy recovery unit and a flow-regulating pressure-regulating valve, belongs to the technical field of hydropower generation control systems, and comprises the following steps: obtaining water energy recovery unit operation parameters and flow-regulating pressure-regulating valve state parameters; identifying a current system working condition type based on the operation parameters; calculating dynamic coupling model parameters according to the system working condition type and the state parameters; performing fuzzy adaptive control based on the dynamic coupling model parameters and a flow pressure deviation; and selecting and executing a corresponding multi-modal control strategy according to control parameters and the system working condition type, establishing a dynamic coupling model of a flow-regulating valve flow characteristic-guide vane opening-response delay, realizing fuzzy rule-driven PID parameter adaptive adjustment, and constructing a multi-modal collaborative control mechanism for the unit and the valve. The application effectively solves the flow-pressure dynamic imbalance problem in the water plant unit operation conversion and load rejection working conditions.

The invention belongs to the cross technical field of environmental protection and ocean engineering, particularly relates to a seawater desulfurization floating liquid level self-adaptive aeration method, and aims to solve the problems of low gas-liquid mass transfer efficiency and high energy consumption of a traditional aeration system under the interference of liquid level fluctuation and foam. According to the method, the liquid level and the foam thickness are monitored in real time through multiple groups of ultrasonic and infrared sensors, flue gas concentration and seawater salinity data are combined to be input into a multivariable fuzzy self-adaptive control model, and optimal aeration pore diameter and flow distribution instructions of each region are dynamically output; a piezoelectric driving diaphragm is utilized to realize millisecond-level adjustment of the aperture of 0.5-3.0 mm, a center area, a transition area and an edge area are divided to implement gradient air supply, and micro-pore pulse jet is synchronously triggered to inhibit foam. According to the method, the gas-liquid contact uniformity and the desulfurization stability are remarkably improved, the blind area is reduced by 90%, the efficiency is stabilized at 98% or above, the energy consumption is reduced by 18-22%, the service life of equipment is prolonged by 30%, and the method is suitable for long-term intelligent operation in a high-salt and high-turbidity marine environment.