211 results about "Fuzzy adaptive" patented technology

The invention belongs to the technical field of ship path tracking, and discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID, and the method comprises the steps: presetting a path point, and obtaining a real-time coordinate; obtaining an actual expected heading angle # imgabs0 # and a ship observation heading angle # imgabs1 #, subtracting the actual expected heading angle # imgabs0 # and the ship observation heading angle # imgabs1 # to obtain a heading deviation # imgabs2 #, and calculating a heading deviation rate # imgabs3 #; the course deviation and the course deviation rate are input into a fuzzy self-adaptive PID motion control module, and gain parameters are output through a fuzzy control algorithm; the gain parameter and # imgabs4 # are input into a PID controller to calculate a ship rudder angle # imgabs5 #; inputting # imgabs6 # into the motion mathematical model to update the motion state, and forming a closed-loop updated motion state to enable the ship to approach a preset path point; and judging a ship path after the state information is updated. The expected course of the ship is calculated in real time and the sideslip angle of the ship and the control parameters of the ship are adjusted in real time through self-adaptive sight guidance, and the control parameters are automatically adjusted in real time by means of the fuzzy self-adaptive PID motion control module to achieve accurate motion control, so that the ship travels according to a preset planned path.

The invention discloses a cable inspection robot attitude control method based on MPC and ADRC fusion, and the method comprises the steps: firstly, building a model prediction control (MPC) model, carrying out the prediction of the future state of a robot, solving an optimal control problem, and obtaining initial control input for achieving the internal dynamic compensation; secondly, an extended state observer (ESO) in an auto-disturbance rejection control (ADRC) model is adopted, and external disturbance is estimated in real time and used for compensating control input; thirdly, a fuzzy self-adaptive module based on fuzzy PID is introduced, the attitude error, the attitude error change rate and regeneration disturbance intensity generated based on estimation disturbance are collected in real time, the MPC weight and the ESO gain are dynamically adjusted, and the response speed and robustness of the system under variable working conditions are improved. According to the hierarchical control scheme, the defect that an existing single control method is difficult to keep the optimal control effect under variable working conditions is effectively overcome, and technical guarantee is provided for stable operation of the cable inspection robot in a high-risk and high-complexity environment.

The invention provides an elevator energy recovery application method and system based on a super capacitor, and relates to the technical field of energy recovery, and the method comprises the steps: collecting elevator operation data and super capacitor state data, constructing a state space and an action space, and constructing a reward function according to peak-valley electricity price economic benefits, super capacitor life loss and energy utilization efficiency; training a deep reinforcement learning model based on the double-Q network to obtain an energy recovery optimization strategy; when the elevator is braked, regenerative braking energy is stored in the super capacitor; when the elevator is driven, the stored energy is released for traction; the optimal storage allocation scheme of the multiple elevator rooms is calculated through the virtual energy storage unit, hierarchical control is implemented based on an optimization strategy and the allocation scheme, the upper layer adopts fuzzy self-adaptive weight to determine the energy storage priority, and the lower layer adopts a distributed algorithm to calculate the energy allocation coefficient and the target power; and the charge-discharge rate is updated according to the health state of the supercapacitor.

The invention belongs to the technical field of power systems and automation thereof, and relates to a virtual inertia fuzzy self-adaptive new energy inverter frequency active support method. Aiming at the problem of power grid frequency stability caused by new energy power generation intermittency and volatility, the method comprises the following steps: simulating inertia characteristics of a synchronous generator through a direct current side capacitance control strategy; detecting the frequency of the power grid by using a power synchronous ring, and converting a power difference value into a frequency deviation; designing a fuzzy control module, and adjusting the coefficient of the virtual inertia controller in real time according to the frequency deviation to optimize the frequency response; an inverter current given value is adjusted according to the output of the fuzzy control module, and active and reactive power output is calculated and controlled; relevant parameters are monitored in real time, the performance of the self-adaptive control module is evaluated, and parameter optimization response of the fuzzy controller is adjusted. The frequency stability and reliability of the power grid can be effectively improved, the control flexibility and precision of the inverter are improved, and new energy efficient grid-connected power generation is promoted.

The invention relates to the technical field of robot intelligent control, in particular to a concrete construction digital intelligent robot control system. Comprising a concrete data acquisition module, a construction path planning module, a robot control module, a quality monitoring module and an interactive feedback module. The data acquisition module acquires information such as concrete height and humidity of a construction area through a sensor; the path planning module generates an optimal construction path by using an artificial bee colony algorithm; the robot control module accurately executes a path instruction through sliding mode control and a fuzzy adaptive algorithm; the quality monitoring module adopts a support vector machine to evaluate construction quality and generate a report; and the interaction feedback module realizes path adjustment and information interaction through a visual page. Through combination of digital intelligence and algorithm optimization, integration of intelligent planning of a construction path, precise control of the robot and quality monitoring and evaluation is realized, and the intelligent level and engineering quality of concrete construction are remarkably improved.

The invention discloses an extruder control system for cable protection pipe production, and relates to the technical field of cable protection pipe processing. Comprising a sensor module used for collecting operation parameters of the extruder, the operation parameters comprise the charging barrel temperature, the machine head temperature, the extrusion pressure, the screw speed and the traction speed, and a control module connected with the sensor module and used for receiving the operation parameters and generating a control instruction based on preset control logic and algorithm. A fuzzy self-adaptive PID algorithm is adopted for temperature control, nonlinearity and time-varying properties of extrusion temperature control can be adapted, the temperature control precision is improved, and the influence of temperature fluctuation on material extrusion is reduced, so that the quality of a cable protection pipe is improved; interference factors such as material characteristic changes can be effectively handled, stable control over extrusion pressure is achieved, and the production quality and production efficiency of the cable protection pipe are guaranteed.

The invention discloses a photodynamic inactivation parameter adjusting method based on hybrid fuzzy-PID control, relates to the technical field of parameter fuzzy control, and aims at the problem that photodynamic inactivation efficiency and aquatic organism safety in aquaculture are difficult to consider at the same time through multi-sensor fusion and environment prediction. Then, a fuzzy-PID self-adaptive putting technology is adopted to be coupled with an illumination strategy, and accurate matching of the photosensitizer dosage and the light intensity is achieved; water body fluctuation and pathogen diffusion are dealt with through real-time closed-loop and multi-scene collaborative scheduling; multi-dimensional index evaluation is utilized, reinforcement learning is introduced to carry out self-learning optimization, and putting and illumination parameters are continuously improved; and finally, the method is expanded to large-scale distributed control and cloud big data training, and efficient inactivation and flexible deployment can be kept in different breeding scenes. Pathogens can be effectively prevented from being rapidly diffused in an adverse environment, damage to aquatic animals caused by excessive accumulation of reactive oxygen species (ROS) is reduced, and meanwhile energy consumption and resource waste are reduced.

The invention belongs to the technical field of position tracking control of rigid multi-joint mechanical arms, and discloses a fuzzy self-adaptive visual servo control method and system for a multi-joint mechanical arm system for placing bobbins in a textile workshop, and the method comprises the steps: constructing an image error model according to the positions of feature points on an image plane; the fuzzy logic system is used for processing uncertain items in the system, the instruction filtering and fuzzy self-adaption technology is used for achieving tracking control over the expected trajectory of the system, the problem of calculation complexity is solved, and adverse effects caused by filtering errors are eliminated. In the process of changing the bobbins, the bobbins are placed on a creel through a multi-degree-of-freedom robotic arm, a clamping jaw at the tail end of the robotic arm is required to accurately grab the bobbins for feeding and discharging, and the requirement for accurate and rapid control of the robotic arm is met, so that research on visual servo control of the robotic arm is of great significance. The method has a good control effect, the problem of calculation complexity in the design process of the controller is solved, the trajectory tracking error is smaller, and the system robustness is higher.

The invention provides a path planning method and system based on four-AGV cooperative carrying, and relates to the technical field of automatic logistics transportation. The method comprises the steps that a high-precision grid map is constructed, a global path is generated in combination with a simulation improved A * algorithm, dynamic weight factors are adaptively adjusted according to the obstacle density, and the path search efficiency is optimized; a time window constraint method is adopted to divide path segments and dynamically allocate time windows; real-time sensing of dynamic obstacles is fused, and a local path is adjusted based on Kalman filtering and a dynamic priority scheduling algorithm; a one-master and three-slave cooperative control architecture is adopted, a master AGV tracks a simulation path, and slave AGVs dynamically adjust control parameters through a fuzzy adaptive PID algorithm; the synchronous jacking operation of the four AGVs feeds back the state to the central server in real time through the Ethernet, so that the synchronism and safety of collaborative carrying are ensured. According to the invention, the technical problems of high path conflict rate, low trajectory tracking precision and slow obstacle avoidance response of a multi-AGV system in a complex environment are solved.

The invention belongs to the technical field of force / position hybrid control of mechanical arm systems, and particularly discloses a finite-time self-adaptive impedance control method and system for a flexible joint mechanical arm system. In order to solve the problems that the control effect of a mechanical arm system is affected by nonlinear and unknown system model parameters at present and derivation is complex in backstepping design, the fuzzy self-adaptive finite time impedance controller is designed based on the impedance control technology so as to achieve hybrid control over the track and force of the tail end of the mechanical arm. In the design process of the controller, a real control law is constructed to realize finite time stability of the system; and meanwhile, command filtering and fuzzy self-adaptive technologies are introduced to solve the problems of calculation explosion in backstepping design and unmodeled dynamics of a mechanical arm system. By means of the control method, the influence caused by system uncertainty can be effectively overcome, the position tracking error of the mechanical arm system is reduced, and meanwhile it can be guaranteed that all signals in a closed-loop system are bounded within finite time.

The invention discloses an air pressure-boiling point self-adaptive regulation and control system and method of an energy storage immersed phase change liquid cooling system, and relates to the technical field of energy storage systems. The method comprises the following steps: calculating a thermal load of a system in real time, performing multi-objective optimization grading, and dynamically determining an optimal boiling point set value under a current working condition; resolving and obtaining a target air pressure set value by using the air pressure-boiling point relation mapping library; an improved composite control algorithm integrating feedforward control, fuzzy adaptive PID, integral saturation resistance and change rate limitation is adopted to drive an air pressure regulation execution mechanism, and regulation and control of the air pressure in the system are achieved. The system and the method have the advantages of high control precision, high response speed, good stability, high adaptability and the like.

The invention relates to the technical field of voltage control, and discloses an ESC voltage regulator voltage control method and system based on fuzzy adaptive PID, and the method comprises the steps: collecting the output voltage of an ESC voltage regulator and a preset target voltage in real time, calculating a voltage error and an error change rate after filtering, and carrying out the calculation of the error change rate; dividing fast tracking, precise adjustment and anti-interference stability maintenance stages according to errors and change rates and generating control signals, calling corresponding fuzzy rule subsets to deduce proportional, integral and differential gain adjustment amounts, generating real-time gains in combination with initial PID parameters, calculating control amounts by using position type PID based on the real-time gains, converting the control amounts into signals to drive power switch tubes, and controlling the power switch tubes. And closed-loop adjustment is formed. According to the method, the problem that a traditional control method is poor in performance under multiple working conditions is solved, cooperative control over rapid tracking, precise adjustment and anti-interference stability maintenance of the output voltage of the ESC voltage regulator is achieved, it is ensured that the ESC voltage regulator can output stable voltage under the working conditions of starting, steady-state operation and load sudden change, and the requirement for high voltage control precision and dynamic response is met.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

The invention relates to the technical field of unmanned aerial vehicle measurement, in particular to an unmanned aerial vehicle measurement system based on low-altitude meteorological high-precision positioning, which comprises a low-altitude meteorological sensing unit, a positioning fusion calculation unit, a cooperative calibration control unit and a self-adaptive measurement unit. The positioning fusion calculation unit uses an atmospheric delay correction model and fuzzy adaptive Kalman filtering to compensate an atmospheric delay error and adjust a positioning noise matrix to output accurate track coordinates, and cooperates with the calibration control unit to establish a relation between a humidity gradient and a positioning height error, obtain an optimal compensation coefficient and convert the optimal compensation coefficient into a flight control instruction, and finally control the flight control instruction. And the adaptive measurement unit controls the multispectral imager to switch wavebands and adjust the scanning angle of the laser radar according to the flight path and the meteorological parameters, so that the aerosol vertical profile synchronous inversion is realized, and the positioning accuracy of the unmanned aerial vehicle and the reliability of measurement data under the complex meteorological condition are improved.

A pneumatic control valve control system and a control method thereof belong to the technical field of pneumatic control valve control, the system comprises a signal detection module, an execution module, a PLC control module and an upper computer monitoring module, and the PLC control module is connected with the signal detection module, the execution module and the upper computer monitoring module. The control method comprises the following steps: S1, establishing a mathematical model transfer function of the regulating valve control system; and S2, PID parameters are adjusted on line by using a fuzzy control method, and a PID controller is optimized, so that the PID controller has a function of self-tuning according to system changes. On the basis of a traditional PID algorithm, the pneumatic control valve opening mathematical model and the fuzzy controller are established, the fuzzy self-adaptive PID algorithm is used for controlling the pneumatic control valve in real time, and compared with a traditional PID self-tuning system, a pneumatic control valve control system of the fuzzy PID control algorithm is high in response speed, good in stability and small in overshoot; therefore, the dynamic performance of the pneumatic control valve control system is greatly improved.

The invention discloses a multi-axis servo turntable system and a control method, and belongs to the technical field of mechanical control. The multi-axis servo rotary table system comprises a driving mechanism, a control unit and a rotary table body, and the driving mechanism comprises a plurality of servo motors and is used for driving the rotary table body to rotate and axially move; and the control unit controls each servo motor by using a fuzzy adaptive sliding mode iterative learning control algorithm. The sliding mode parameters are dynamically adjusted by introducing fuzzy logic, and the compensation of periodic disturbance by iterative learning is combined, so that the system buffeting is remarkably reduced, the dynamic response speed is improved, and the high-precision control of multi-axis collaboration is realized.

The invention provides a self-adaptive pipeline vibration suppression system and method based on multi-mode coupling analysis. The system comprises a sensing module, a vibration analysis module and a control execution module, multiple sensing signals in the vibration process are obtained through measurement of a multi-sensor array, the vibration analysis module comprises a wavelet decomposition feature extraction unit, a prediction network unit and a judgment unit, and the wavelet decomposition feature extraction unit extracts feature data of the multiple sensing signals and sends the feature data to the control execution module; the prediction network unit obtains the probability distribution of various vibration modes according to the characteristic data, the judgment unit determines the dominant vibration frequency according to the probability distribution of various vibration modes, and the fuzzy adaptive unit of the control execution module dynamically distributes the output value of an actuator according to the dominant vibration frequency. And the hybrid execution unit controls the piezoelectric actuator and the magneto-rheological damper to perform hybrid suppression on vibration according to the dynamically distributed output value of the actuator. The system can accurately identify the vibration source, improve the control responsivity, and improve the overall high temperature resistance of the system.

The invention discloses a BLDC (brushless direct current) driving parameter self-tuning embedded system and method for multiple equipment types, and the method comprises the steps: constructing a BLDC unified mathematical model containing an electromagnetic torque equation, a back electromotive force equation and a mechanical motion equation, and building a nonlinear mapping relation library of motor type identification codes and a key electrical parameter set; a predefined multi-mode excitation signal sequence is injected into the to-be-driven BLDC motor, dynamic response data are synchronously collected, and the dynamic response data are used for representing the characteristics of the key electrical parameter set; based on collected dynamic response data, equivalent inductance and equivalent resistance in a key electrical parameter set are decoupled and extracted through a frequency domain impedance analysis method, and meanwhile, a counter electromotive force coefficient and rotational inertia in the key electrical parameter set are identified by combining time domain counter electromotive force zero crossing point detection with rotating speed differential operation. Based on a motor fingerprint feature library and a fuzzy adaptive architecture, multi-model debugging-free plug-and-play is realized, and stable control performance is maintained under a dynamic working condition.

The invention relates to the technical field of rocket thrust chamber manufacturing, and discloses an additive forming part linear cutting and taking auxiliary system for a rocket thrust chamber. The system comprises a multi-source data acquisition module for acquiring geometrical morphology data of the additive forming part through various sensors; the cutting path planning module is used for generating a global cutting path sequence based on an improved genetic algorithm; the dynamic deformation compensation module is used for compensating thermal deformation and vibration interference in real time by using a fuzzy adaptive observer; and the multi-axis cooperative control module is used for constructing a distributed constraint optimization model to realize accurate control of multi-axis motion. The system can accurately collect data, reasonably plan a cutting path, effectively compensate deformation, ensure multi-axis cooperative work, remarkably improve the quality and efficiency of linear cutting and taking of the rocket thrust chamber additive forming part, reduce the production cost and provide powerful technical support for spaceflight manufacturing.

The invention relates to the technical field of ship navigation, and discloses a self-adaptive ship navigation terminal. A navigation environment sensing module of the terminal collects dynamic parameters of a ship navigation environment in real time through a multi-source sensor and sends the dynamic parameters to a self-adaptive control decision module and a remote monitoring platform; the adaptive control decision module obtains navigation deviation based on the real-time parameters and preset navigation parameters, calculates a control instruction through a fuzzy adaptive algorithm and sends the control instruction to the course adjustment execution module; the course adjustment execution module executes steering or navigational speed adjustment according to the control instruction and corrects a navigation track; the navigation state evaluation module monitors the adjusting process, generates a state signal and sends the state signal to the remote monitoring platform, and the remote monitoring platform gives out early warning when receiving the early warning signal. According to the terminal, a navigation strategy is dynamically adjusted by utilizing a fuzzy adaptive algorithm through multi-module cooperation, and the navigation adaptability and safety of a ship in a complex environment are improved in combination with real-time state evaluation and remote early warning.

The invention relates to the technical field of data processing, and discloses a virtual synchronous machine control method and device of a photovoltaic power station. The method comprises the steps that virtual synchronous calculation is conducted on a virtual inertia parameter and a virtual damping coefficient through a double-closed-loop controller, a PWM control signal of an inverter is obtained, an outer loop is power angle control, and an inner loop is current proportional resonance control; performing coordination control on the power grid frequency change data and the virtual inertia parameters through a dynamic adjustment algorithm to obtain a system power reserve control strategy and a seamless switching strategy; and performing performance evaluation and parameter optimization on the system operation data through a fuzzy adaptive algorithm to obtain optimized virtual synchronous machine parameters and MPPT control parameters. According to the invention, the efficiency and accuracy of virtual synchronous machine control of the photovoltaic power station are improved.

The invention discloses a pure electric ship battery equalization control system based on a PID algorithm, and the system comprises a battery state monitoring module which is used for monitoring the state parameter of each single battery in a battery pack; the fuzzy self-adaptive PID controller is used for converting the received state parameters into PID parameter adjustment values through fuzzy control and PID control and outputting control signals; the efficient equalization module is used for evaluating the state of each single battery based on the control signal and the state parameter, and formulating an equalization strategy for battery equalization according to the state evaluation result of the single battery; the energy feedback module is used for converting redundant electric energy generated in the battery equalization process into charging electric energy of other single batteries based on an efficient DC-DC converter technology of a high-frequency transformer; and the control module is used for controlling the battery state monitoring module, the fuzzy self-adaptive PID controller, the efficient equalization module and the energy feedback module. And the battery equalization control precision and the energy recovery efficiency are improved.

The invention belongs to the technical field of automobiles, and particularly relates to an electronic mechanical braking system self-adaptive clamping force control method considering friction hindrance. Comprising the steps that 1, a proportional-integral theory is adopted to design a clamping force closed-loop controller and a fuzzy self-adaptive control parameter adjusting method of the electronic mechanical braking system, and proportional parameters and integral parameters of the clamping force closed-loop built-in controller are dynamically adjusted; secondly, feedforward compensation control based on friction estimation is designed through a third-order expansion state observer, and dynamic estimation is conducted on nonlinear friction obstruction of the electronic mechanical braking system; and step 3, the permanent magnet synchronous motor servo control comprehensively applies excitation axis zero control setting, double-axis compensation constraint and a proportional-integral algorithm to realize position closed-loop and current closed-loop controller design. According to the invention, the problems of remarkable nonlinear stiffness characteristic, nonlinear friction obstruction, electromagnetic coupling of the excitation shaft and the torque shaft of the permanent magnet synchronous motor and the like in the working process of an electronic mechanical braking system are effectively solved.

The invention relates to an unmanned aerial vehicle path planning method based on a fuzzy adaptive parameter RRT algorithm, and belongs to the technical field of path planning. Comprising the following steps: S1, system initialization and environment perception: defining a starting point and a target point of an unmanned aerial vehicle in a three-dimensional task space, loading information of a static obstacle and a dynamic obstacle, and configuring a sensor to obtain environment parameters; s2, designing a discrete fuzzy inference system: defining input variables as obstacle density RN and total node number TN, and defining output variables as step length SS and target deviation probability RP; constructing a fuzzy rule base for dynamically reasoning an output variable according to the input variable; s3, executing a fuzzy adaptive parameter RRT algorithm, optimizing and improving the discrete fuzzy inference system, and adjusting adaptive parameters; and S4, path optimization: optimizing the initial path by adopting a cost function rewiring method, and reducing the length of the path. In the dynamic process of path planning, according to the number of random nodes and the total number of nodes of the current random tree, a fuzzy inference system is designed to adaptively adjust the selection probability and the step length of an algorithm, and the path planning efficiency and the success rate are effectively improved. The problems in the prior art are solved.

The invention belongs to the technical field of heavy-load vehicle control, and particularly discloses a bottom die trolley walking control method and system, and the control method comprises the steps: starting a task and setting parameters; collecting multi-dimensional data in real time; a displacement target is judged; calculating theoretical motion parameters; target torque distribution and instruction adjustment; and finishing the task and resetting the system. According to the control method, torque control, speed control, displacement control and energy consumption optimization are fused, a closed-loop feedback strategy, a fuzzy PID self-adaptive algorithm and a multi-motor cooperative algorithm are utilized, and track pre-detection and energy recovery are combined, so that the heavy-load and high-rigidity bottom die trolley can still run stably and accurately under the non-ideal track condition, and the control method is suitable for large-scale production. The control precision and stability of trolley running are remarkably improved, running deviation caused by poor rails and structural rigidity is reduced, the linear and dimensional precision of segmental beam prefabrication is guaranteed, and meanwhile the equipment failure rate, the maintenance cost and the energy consumption are reduced.

The invention discloses a dexterous hand fruit and vegetable grabbing control method based on fuzzy variable impedance control. The method comprises the following steps: firstly, establishing a multi-finger collaborative kinematics model based on a spinor theory, and determining fingertip poses, grabbing force of each finger and multi-finger resultant force distribution; on the basis of force sealing, an optimization model with the minimum contact force as a target is constructed, an NSGA-II algorithm is adopted for solving, and the minimum grabbing force needed by stable and lossless grabbing is obtained; establishing an impedance control model in a Cartesian space, enabling a grabbing system to be equivalent to a mass-spring-damping system, analyzing the influence of an impedance parameter on grabbing performance, and determining an inertial parameter and a damping coefficient as key adjustment objects; fuzzy control is further introduced, the grabbing force error and the change rate of the grabbing force error serve as input and output inertia and damping adjustment amount, a fuzzy self-adaptive variable impedance controller is constructed, and dynamic optimization of impedance parameters and accurate tracking of the grabbing force are achieved. The fruit and vegetable grabbing device can be effectively applied to rapid, stable and lossless grabbing of various fruits and vegetables in an unstructured agricultural environment.

The invention discloses an intelligent load distribution system and method for a centrifugal compressor unit. The system comprises a data sensing module, an intelligent decision-making module, an instruction execution module and a model updating module. The method comprises the steps that operation parameters of multiple parallel centrifugal compressors are synchronously collected in a high-precision mode through an industrial real-time network supporting hardware timestamps, and time-aligned system state observation data are obtained; constructing a state vector based on the data and a load prediction result, inputting a deep reinforcement learning agent fused with compressor physical characteristic constraints, and generating a load distribution instruction and a unit start-stop suggestion; after safety boundary verification is conducted on the original action, the guide vane opening degree is adjusted through a feedforward speed planning and fuzzy self-adaption PID composite servo control algorithm, and starting and stopping operation is executed; meanwhile, empirical data are collected online, and model parameters are periodically and finely adjusted. According to the invention, efficient, safe, accurate and self-evolution compressor group control is realized.

The invention discloses a fixed-time fuzzy self-adaptive output feedback formation collision avoidance control method for a multi-unmanned system, and the method comprises the steps: constructing an artificial potential energy function for collision avoidance constraint through the formation position error of the multi-unmanned formation system, so as to build a collision avoidance condition, and achieve the collision avoidance function in the formation process of the multi-unmanned system. A fuzzy state observer is constructed according to an unmanned system state equation, the problem that speed information of an unmanned system cannot be measured due to the fact that unmanned system carrying equipment may have limitation or has accidental faults and interference is solved, a fixed-time fuzzy self-adaptive formation collision avoidance virtual controller is constructed, and the speed information of the unmanned system can be measured. A formation controller is constructed in combination with a parameter adaptive law and a fuzzy state observer, and in order to realize a formation control target, uninterrupted network information transmission is generally needed, and problems of communication redundancy, communication limitation and the like can occur, an event triggering mechanism of a dynamic threshold value is designed to reduce waste of network communication resources; and a fixed-time fuzzy adaptive output feedback formation collision avoidance controller based on an event triggering mechanism is constructed, so that fixed-time formation collision avoidance control of the multi-unmanned system with unknown dynamics and unmeasurable speed is realized.

The invention relates to a stabilization self-adaptive control method and device based on double gyroscopes, and the self-adaptive control method comprises the steps: constructing a real-time updated'sea wave-ship-double gyroscope 'coupling dynamic model, and introducing the online identification of ship rolling parameters; physical limit, damping, maximum torque and other constraints of the double-gyroscope executing mechanism are explicitly written into a control law; the two gyroscopes are symmetrically arranged and opposite in precession direction, so that yawing torques are mutually counteracted and rolling torques are superposed, and the stabilization efficiency is improved; adaptively selecting PID according to the rolling amplitude, the sea condition uncertainty and the working state of the actuator, and adaptively switching the fuzzy adaptive PID and the model prediction control strategy; calculating a control instruction based on the selected strategy and the updating model, and performing physical constraint processing such as torque saturation and angle amplitude limiting; and outputting a safety instruction to drive the gyroscope to generate stabilization torque. The method can adapt to different working conditions, and the efficient and stable active stabilization performance and stabilization effect are kept under the multiple working conditions.

The invention provides a gas relay intelligent detection test bench based on cloud computing and an automatic calibration system, and relates to the technical field of specific cloud computing models. An intelligent decision fusion unit is constructed through cooperation of a fuzzy adaptive neural network and PINNs to realize intelligent setting of a threshold value, and standardized accurate calibration of a mechanical floater and a fault mechanism is completed by virtue of a technical route of high-precision sensor-cloud nonlinear dynamic modeling-closed-loop online optimization. And the detection consistency and the response reliability of the multi-model gas relays are obviously improved.