216 results about "Fuzzy model" patented technology

The invention relates to a multi-target adaptive cruise control method based on fuzzy model predictive control. The multi-target adaptive cruise control method comprises the following steps: collecting real-time operation data of a vehicle; inputting the real-time operation data of the vehicle into the improved vehicle longitudinal dynamic model, and predicting the future motion state of the vehicle; on the basis of the output of the vehicle longitudinal dynamics model, a feedback correction ACC system vehicle following prediction model is used for predicting and correcting the vehicle following behavior in real time, and a vehicle following control strategy is dynamically adjusted; dynamically adjusting the weight of a target function under model prediction control by adopting a fuzzy variable weight adjustment strategy; and performing model prediction control according to the target function and considering multiple constraint conditions, generating accurate acceleration and deceleration control parameters, acting on the controlled vehicle, and realizing multi-target adaptive cruise control. Compared with the prior art, safe, energy-saving and efficient self-adaptive vehicle following control can be achieved in a complex and changeable traffic environment, and an effective technical scheme is provided for improving the response speed and adaptability of an intelligent cruise control system.

The invention belongs to the field of artificial intelligence and video anomaly detection, and discloses a weak supervision video anomaly detection method based on curriculum learning and contrast learning strategies. A course learning strategy is designed by simulating a human learning mode, an anomaly detection model is trained through a contrast learning method based on a normal prototype, and the method comprises the following steps: preprocessing a video and extracting an optical flow image; video appearance and motion features are extracted and fused; inputting into a pre-classifier to obtain a pseudo label; judging the anomaly detection difficulty according to the false label, and dividing a simple set and a difficult set; initializing a model by using a simple set; and performing two-stage training by using the difficult set, and updating model parameters through normal prototype contrast learning. According to the invention, the discrimination capability of a weak supervision video anomaly detection model can be effectively improved, the problems of model deviation, fuzzy positive and abnormal boundaries and insufficient model discrimination capability under the influence of noise in the prior art are solved to a great extent, and the accuracy of video anomaly detection is remarkably improved.

The invention belongs to the technical field of intelligent substations, and particularly relates to an intelligent nuclear phase method and system for a substation. According to the method, a dynamic priority evaluation model is constructed, multi-source data of four kinds of basic factors including voltage phase difference, environment temperature and humidity, equipment vibration and historical errors are synthesized, and a fuzzy comprehensive evaluation method is utilized to calculate weights and dynamically sort the weights; a multi-port phase scattering model is constructed based on transformer substation topology, impedance mismatch points are analyzed by means of a Smith chart, and dual-stage optimization is implemented; constructing a three-layer decision input set according to the optimization model, designing a fuzzy logic driven adaptive rule engine, dynamically outputting a working condition level and matching a nuclear phase strategy; multi-dimensional quality evaluation, dynamic updating of factor weights and optimization of fuzzy model parameters are carried out by monitoring indexes such as reflection coefficients in real time, abnormal mode recognition and compensation strategy solidification are realized by fusing LSTM and a graph neural network, a self-evolution closed-loop system is formed, and finally, crossing of a nuclear phase process from accurate matching to autonomous evolution is realized.

The present invention relates to the technical field of water distillers, and disclosed are a thermocompression multi-effect water distiller system and a control method therefor. The thermocompression multi-effect water distiller system comprises a distillation unit and a control unit; the distillation unit comprises a multi-effect evaporator; the multi-effect evaporator is a horizontal tube falling film evaporator and has a side-by-side configuration, and the shell pass of a preceding effect evaporator is communicated with the tube pass of a following effect evaporator; the control unit controls the distillation unit during working, and the control unit is a feedforward-cascaded fuzzy PID model predictive control system and comprises an inner-loop control system and an outer-loop control system; the inner-loop control system uses fuzzy PID control, and the inner-loop control system is provided with a control system for fluctuations in industrial steam pressure Psteam; and the outer-loop control system uses model predictive control. In the present invention, by using the thermocompression multi-effect water distiller system and the control method therefor, the heat exchange efficiency is greatly improved; and a novel control system is provided, enabling normal operation within a range of 40-200% of a set operating condition.

The invention discloses a T-S fuzzy and TD3-based slow dynamic unknown coal-fired power generation system coordination control method, which comprises the following steps: firstly, decomposing a coal-fired power generation system into a fast subsystem and a slow subsystem by using a singular perturbation theory, and decomposing an original control task into a stabilization task of the fast subsystem and a tracking task of the slow subsystem; for the fast subsystem, a fuzzy set is selected on a definition domain of the fast subsystem by using the slow variation characteristic of steam pressure, and a T-S fuzzy model is constructed; a fast subsystem controller is obtained by solving algebraic Riccati equations corresponding to a plurality of linear systems. For a slow subsystem, a double-Q network is adopted to reduce overestimation of a Q value, a dynamic learning rate and a batch size adjustment mechanism are introduced to accelerate training convergence, and learning is carried out under a TD3 algorithm framework to obtain control input of the slow subsystem. The fast subsystem and the slow subsystem control inputs to be combined and then act on the original system to obtain state information at the next moment, and the intelligent agent interacts with the original system to complete collaborative optimization.

The invention relates to a networked nonlinear system control method based on fuzzy adaptive event triggering. The method comprises the following steps: S1, carrying out equivalent T-S fuzzy modeling on a networked nonlinear system based on a T-S fuzzy theory; s2, constructing a fuzzy adaptive event triggering mechanism based on T-S fuzzy model structured characteristics and fuzzy membership function information; s3, based on the fuzzy adaptive event trigger mechanism and the sampling control strategy, constructing a T-S fuzzy networked closed-loop system based on the fuzzy adaptive event trigger mechanism under sampling input; s4, based on a Lyapunov stability theory, establishing a sufficiency condition of asymptotic stability of the T-S fuzzy networked closed-loop system based on a fuzzy adaptive event trigger mechanism; and S5, based on a linear matrix inequality theory, calculating a state feedback gain matrix of the T-S fuzzy networked closed-loop system, and realizing control of the networked nonlinear system based on the T-S fuzzy model. According to the method, unnecessary communication transmission is reduced and the control cost is saved while the stability of the system is ensured.

The invention discloses a potash magnesium sulphate synergistic application mechanism optimization method, and relates to the field of intelligent agricultural fertilization control. According to the method, growth characteristic parameters such as the fruit weight gain rate and the root distribution depth of the citrus reiculata Blanco are collected in real time, and the fertilization amount is calculated and dynamically adjusted in combination with the T-S fuzzy model, so that the fertilization amount can accurately meet the growth requirements of crops, and excessive fertilization or insufficient fertilization is avoided.

The invention aims to provide a battery state of charge (SOC) estimation method based on fuzzy mathematics and a genetic algorithm. The method comprises the following steps: step a, establishing a fuzzy model, capturing a fuzzy boundary and uncertainty of parameters by using the fuzzy model, and constructing and performing fuzzy optimization on a fitness function through the fuzzy boundary and the uncertainty; step b, integrating the fitness function after fuzzy optimization into an actual calculation process of a genetic algorithm, and making a decision according to the fitness function after fuzzy optimization; c, through multi-generation genetic iteration, the population is gradually converged to an individual with high fitness, an optimal calculation model of the target SOC value is obtained, then the calculation result of the model is compared with an actual observation value, the model parameter or structure is corrected according to the comparison result, the next cycle is entered, and the steps are repeated, the model is dynamically adjusted and optimized, and the SOC value of the target SOC value is obtained. Therefore, the nonlinear change of the power battery state is adapted. The method can be applied to the technical field of battery state monitoring and estimation.

The invention discloses a strip steel hot continuous rolling loop system modeling and fault detection method and system, and relates to the technical field of industrial steel rolling process modeling and fault detection, and the method comprises the steps: building a state space model of a strip steel hot continuous rolling loop system at a plurality of stable working points based on least square identification; fuzzifying the state space model based on a preset fuzzy rule to obtain a T-S fuzzy model of the strip steel hot continuous rolling loop system; optimizing the T-S fuzzy model based on a neural network, and establishing a target system model in which the neural network and fuzzy kernel representation are combined; and performing fault detection on the strip steel hot continuous rolling loop system based on the sum of residual signals between the model output of the target system model in the preset time period and the real output of the strip steel hot continuous rolling loop system. The technical problems that in the prior art, an accurate mathematical model of a loop is difficult to establish in a model-based fault detection method, and a data-driven fault detection method is low in accuracy and difficult in fault interpretation are solved.

The invention discloses an intelligent automobile double-event trigger control method aiming at double communication networks. The method comprises the following steps: initializing; establishing intelligence to judge whether an SC event triggering condition and a CA event triggering condition are met or not; solving a front wheel steering angle control instruction; and performing intelligent automobile double-event trigger control. According to the invention, a double-event trigger condition is designed to determine whether a state signal of an intelligent automobile path tracking type 1 fuzzy model is updated to a type 1 fuzzy time-lag controller or not and determine whether a front wheel steering angle control instruction calculated by the type 1 fuzzy time-lag controller is updated to an actuator module or not; communication resources of double communication networks are effectively saved; the method has the capability of efficiently avoiding the problem of communication congestion, can save a lot of communication resources, and improves the reliability of the intelligent automobile in the path tracking control process. The fuzzy time-delay controller is designed to calculate a front wheel steering angle control instruction, and the reliability of path tracking control of the intelligent automobile is ensured.

The invention belongs to the technical field of power distribution network fault analysis, and relates to a power distribution network typhoon disaster chain dynamic analysis method based on a dynamic Bayesian network, and the method comprises the steps: combing a typhoon disaster-causing mechanism according to historical disaster events, and forming a typhoon disaster chain; establishing a typhoon probability model; establishing a space-time distribution model of typhoon wind speed; establishing a space-time distribution model of typhoon rainfall; establishing an element fault rate model; analyzing the dynamic evolution characteristics of the typhoon disaster chain by using a dynamic Bayesian network; according to the method, in combination with the influence of the secondary disaster of rainstorm in a typhoon disaster chain on the power distribution network element fault, the application range of power distribution network element fault rate calculation in a real scene under the typhoon disaster is widened; an empirical model and a fuzzy model are adopted to replace mechanical calculation, so that the calculation is more convenient, and the application potential is improved; a dynamic Bayesian network is adopted to replace a static Bayesian network, and key elements of dynamic evolution of a typhoon disaster chain in time sequence can be captured.

The invention discloses a controllable load adjustable potential evaluation method considering user response willingness, and the method comprises the steps: carrying out the quantitative aggregation of the user response willingness according to different power utilization characteristics of a flexible load, and constructing a flexible load influence factor membership function through a TSK fuzzy model; the relation between the up-regulation and down-regulation response willingness of the flexible load cluster and other influence factors is quantified, the upper and lower limits of the adjustable potential of the flexible load cluster are calculated according to the determined response willingness threshold value to serve as one of consideration of the reliability of the adjusting quantity, richer information is provided for optimized operation of the power distribution network, and the reliability of the power distribution network is improved. And a real-time electricity price scheme is combined to guide the electricity consumption behavior of the user, thereby reducing the power grid load peak and filling up the power grid load valley.

The invention discloses a fuzzy model predictive control method for a coal-fired boiler-steam turbine system, and mainly relates to the technical field of intelligent environmental protection. Comprising the following steps: S1, constructing a fuzzy prediction model by using Takagi-Sugeno fuzzy logic; s2, constructing a control efficiency evaluation index, and establishing a dual-channel event triggering mechanism; s3, constructing a model prediction controller, constructing an objective function based on the prediction model, and solving an optimal control signal at each sampling moment; s4, performing offline training on the fuzzy prediction model through actual data of the boiler-steam turbine system to obtain a prediction model for a control system; s5, on the basis of a dual-channel event triggering mechanism, updating the prediction model according to real-time data through an online updating strategy; according to the method, the steam drum pressure, the generated power and the steam drum water level can be efficiently and accurately controlled, the calculation overhead can be effectively reduced, and the real-time performance and the stability of a control system are improved.

The invention discloses an abnormal index detection method and device for a distributed architecture system and a medium, belongs to the technical field of intelligent operation and maintenance, and is used for solving the technical problems that in the operation and maintenance process of an existing distributed architecture system, fault recognition lags behind, fault discovery accuracy is low, the problem discovery period is long easily, and user experience is affected. The method comprises the following steps: acquiring key index data of an accessed observed system; performing clustering analysis on the key index data, and constructing a normal monitoring index fuzzy model through a Gaussian fuzzy technology; performing data exception matching processing on the observed current monitoring index data through a normal monitoring index fuzzy model to obtain exception monitoring index data; and performing warning processing on the abnormal monitoring index data. The state of the observed system can be accurately judged in time, the problem discovery period is greatly shortened, and the user experience is enhanced.

The invention discloses an extruder moving beam position tracking control method based on adaptive iterative learning, and belongs to the technical field of position control. An extruding machine moving beam position tracking control method based on adaptive iterative learning comprises the following steps that according to the physical property of a moving beam, the motion mode of the moving beam is analyzed, and a kinetic model of a nonlinear parameterized moving beam system is constructed; establishing a fuzzy model for an unknown part in the dynamic model, and determining a position tracking error system of the moving beam; based on a self-adaptive iterative learning control theory, designing a finite-time high-precision position tracking control strategy of the movable beam of the extruding machine; and analyzing the stability of the controller. The invention relates to an extruder moving beam position tracking control method based on self-adaptive iterative learning, which realizes finite time high-precision position tracking control of a moving beam by combining a Fourier series-fuzzy approximation method based on a self-adaptive iterative learning control theory.

The invention discloses a temperature and speed intermittent control method for a hypersonic rocket vehicle. The method comprises the following steps: S1, establishing a dynamic model comprising a speed subsystem and a temperature subsystem of the hypersonic rocket vehicle; introducing an error variable, and converting the kinetic model into an error system model; establishing a coupling fuzzy error system model by combining a T-S fuzzy model and constructing a coupling state variable; s2, designing a discontinuous state observer and an intermittent feedback control signal of a hypersonic rocket vehicle system based on the coupling fuzzy error system model of the hypersonic rocket vehicle; in a stability analysis process, determining design parameters of the intermittent feedback control signal; and S3, performing intermittent control on the hypersonic rocket vehicle by using the intermittent feedback control signal. According to the control method, the control problem of the hypersonic rocket vehicle under the conditions that the state information cannot be completely measured and the communication bandwidth is limited is solved, and the control performance of the system can be ensured while the communication burden of the system can be effectively reduced.

The invention discloses an indoor environment dynamic prediction method and system, a medium and equipment, and relates to the technical field of building intelligence. The method comprises the following steps: constructing a plurality of mechanism models for indoor environment prediction, inputting indoor environment related data into the plurality of mechanism models, and obtaining a plurality of first moment prediction values of the plurality of mechanism models; taking the first moment predicted value as the input of a neural network, carrying out nonlinear capture training on the neural network, obtaining a nonlinear fusion device, and obtaining a first moment indoor physical parameter predicted value; determining a residual error between the indoor physical parameter predicted value and the indoor physical parameter true value at the first moment, and inputting the residual error into a depth layered fuzzy model for training to obtain a data compensation model; inputting the indoor environment related data into the data compensation model to obtain a data compensation amount; and adding the residual error and the data compensation amount to obtain a second moment physical parameter prediction value related to the indoor environment. According to the scheme, the environment parameters of the complex indoor environment can be accurately predicted.

The invention discloses a T-S fuzzy model-based two-stage anti-attack coupling memristive neural network finite time bipartite synchronization control method. The method comprises the steps of establishing a memristive neural network model and a symbol topological structure; converting the memristive neural network model into a fuzzy coupling memristive neural network model, defining a synchronous error state variable, and establishing an error system of the memristive neural network; converting the error system into a form based on differential inclusion; a two-stage finite time synchronous controller is designed, in the first stage, when the state of an error system meets the condition, a pulse controller based on a hybrid trigger mechanism is designed to resist spoofing attacks; and in the second stage, a controller combining finite time control and fixed time control mechanisms is designed to resist DoS attacks. According to the invention, a two-stage controller is designed, and the controller integrates pulse safety control, feedback control and a finite time and control mechanism, and can simultaneously resist spoofing attacks with energy constraints and DoS attacks with random outbreak features.

The invention discloses a centrifuge liquid flow detection system based on a fuzzy recognition and classification technology, and relates to the technical field of centrifuge flow detection, and the centrifuge liquid flow detection system comprises the following steps: collecting an image set, and carrying out preprocessing and marking processing to obtain a segmentation flow rate; obtaining characteristic parameters; obtaining a preferred fuzzy model; characteristic parameters of the real-time liquid image are obtained, and flow detection is conducted on the real-time liquid; the method is used for solving the problems that in an existing method for detecting the liquid flow, when the liquid surface is irregular or liquid fluctuation exists, the accuracy and stability of data received by a sensor can not be affected, and consequently the collected liquid flow is different from the actual liquid flow; and the operation of the centrifugal machine is influenced.

The invention provides a preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback, and the method comprises the steps: constructing a nonlinear error dynamic model, and employing a fuzzy modeling technology to represent the system as a combination of a plurality of linear subsystems; introducing a time scaling function, and transforming the error state of the global fuzzy model by using the time scaling function to obtain an error scaling state; an event triggering mechanism is designed to realize intermittent state feedback so as to reduce the communication cost; based on the Lyapunov stability theory, a sufficient condition that the system is stable within preset time is deduced, and a controller gain is obtained by solving a linear matrix inequality of the sufficient condition, so that the system still has robustness under external interference. Agile response and low-communication-cost operation of the reentrant manufacturing system within the preset deadline are realized.

The invention discloses a power distribution network dynamic safety space solving method and system for virtual power plant aggregation, and relates to the technical field of power distribution network safety operation, and the method comprises the steps: obtaining the network topology information of a power distribution network, and carrying out the calculation based on the network topology information of the power distribution network to obtain a hyperplane representation power distribution network safety domain; obtaining a resource fuzzy boundary uploaded by the virtual power plant, performing optimization decision calculation based on the resource fuzzy boundary uploaded by the virtual power plant, and generating a power distribution network resource fuzzy model by using an optimization decision value; inputting the fuzzy model of the power distribution network resource into a hyperplane representation power distribution network security domain, outputting to obtain a fuzzy power distribution network dynamic security space, and performing reduction and defuzzification on the fuzzy power distribution network dynamic security space by using an improved enhanced opposite search algorithm to obtain a power distribution network dynamic security space; inputting the dynamic safety space of the power distribution network into a pre-established aggregation model of a virtual power plant, and outputting to obtain aggregation capacity of the virtual power plant; and calculating the virtual power plant blocking capacity caused by the dynamic safety space of the power distribution network in combination with the original virtual power plant aggregation capacity.

The invention discloses an end face tool sharpener control system, and relates to the technical field of numerical control systems. The end face tool sharpener control system comprises a data preparation and recording module, a tool type parameter dynamic modeling module, a multi-axis linkage control module, a grinding wheel pressure self-adaption control module, a grinding wheel abrasion compensation module and a technological parameter optimization module. By comprehensively collecting multi-dimensional historical processing data and constructing a set framework, automatic classified storage of parameters is realized; the NURBS curve interpolation algorithm is adopted, and the machining precision and smoothness of the complex shape are guaranteed; instructions are synchronized in real time, errors are compensated, vibration is monitored in combination with an acoustic emission sensor, and machining stability is improved; a fuzzy PID model and machine learning are utilized to dynamically adjust parameters and optimize the parameters in advance; through machine vision and digital twinning, abrasion is accurately recognized, and the finishing period is predicted. And based on the hybrid model and reinforcement learning, predicting a processing result, dynamically adjusting parameters, and performing error compensation and sensitivity analysis.

The invention relates to the technical field of hydroelectric engineering, in particular to a T-S fuzzy robust control method for a hydroelectric generating set adjusting system based on a linear matrix inequality, which comprises the following steps: establishing a system nonlinear mathematical model considering modeling uncertainty and external interference under a rigid water attack condition, and converting the system nonlinear mathematical model into an affine nonlinear state space form; a T-S fuzzy model is constructed, and precise approximation of global nonlinear characteristics is realized through linearization of a linear triangular membership function and representative working condition points; a fuzzy controller is designed based on a parallel distribution compensation strategy, and a robust control problem is converted into a linear matrix inequality form to solve a feedback gain matrix in combination with a Lyapunov stability theory and an H-infinity performance index. The method effectively improves the adaptability of the system to a complex operation environment, enhances the anti-disturbance capability, can significantly reduce the system oscillation, shortens the stabilization time, guarantees the safe and reliable operation of the hydroelectric generating set, and has important engineering application value.

The invention discloses an image deblurring method based on multi-scale feature weighting, and the method comprises the steps: collecting fuzzy ship sign picture data of a real scene to obtain a preliminary data set, and estimating a basic fuzzy kernel and a mixing coefficient from the preliminary data set through employing a fuzzy estimation network; obtaining a group of clear ship license pictures, converting the clear ship license pictures into fuzzy ship license pictures by using a basic fuzzy kernel and a mixing coefficient, and gathering each clear-fuzzy ship license image pair to obtain a clear fuzzy data set; constructing a deblurring model which is based on the multi-scale feature extraction network and is fused with a feature weighted fusion module; training a deblurring model by using the clear fuzzy data set, and optimizing the deblurring model through a loss function based on a frequency domain; and inputting a blurred image to be processed into the trained deblurring model to obtain a clear image. According to the method, detail features can be better restored, and the deblurring effect and the overall definition and accuracy of the generated image are improved.

The invention discloses a dynamic scene deblurring method and system based on physical information adversarial learning, and the method comprises the steps: obtaining original blurred image data, carrying out the preprocessing of the original blurred image data, and obtaining a preliminary deblurred image; inputting the preliminary deblurred image into an initial dynamic scene deblurring model for training to obtain a dynamic scene deblurring model; the initial dynamic scene deblurring model comprises a generator network, an optical flow estimation network, a three-stage progressive training strategy and a multi-scale discriminator network, the generator network is used for mapping an initial deblurred image into a deblurred image, and the optical flow estimation network is used for estimating a motion field and calculating optical flow consistency loss; the three-stage progressive training strategy is used for carrying out three-stage training on the initial dynamic scene fuzzy model in sequence, and the multi-scale discriminator network is used for judging image authenticity on different scales; and inputting a to-be-processed blurred image into the dynamic scene deblurring model to obtain a blurred image. According to the invention, the deblurring effect is improved.

The invention provides a coupling fuzzy control, ORC (Organic Rankine Cycle) and Carnot cell waste heat utilization method and system, which combines a fuzzy T-S model, an ORC and Carnot cell coupling system, and excites an ORC waste heat generator set by using a pseudo-random signal modulated based on a fuzzy model. The implementation process of flue gas waste heat utilization and Carnot cell compensation is monitored, regulated and controlled in real time, the adaptability limitation of a traditional static model on the time-varying characteristic of waste heat parameters is broken through, and coordinated control over multi-level gradient utilization of industrial waste heat and stored energy release is achieved. And the flexibility, the energy efficiency level and the new energy friendly access capability of the energy system are improved. According to the method, a multi-energy-flow collaborative optimization mechanism is constructed based on the fuzzy T-S dynamic clustering algorithm, the flue gas waste heat recovery rate is increased, the waste heat resource gradient utilization efficiency and the energy system operation flexibility are remarkably improved, and an efficient solution is provided for low-temperature waste heat deep recovery in the iron and steel industry.

The invention discloses a camera image three-dimensional reconstruction method and device, equipment and a medium, and relates to the technical field of computer vision, and the method comprises the steps: constructing a distortion model, a vignetting model and a fuzzy model, in the three-dimensional reconstruction, firstly, utilizing the constructed distortion model, vignetting model and fuzzy model to obtain a three-dimensional reconstruction model; different aberrations of a camera lens are predicted and added into an image to form an aberration image added with the aberration of the camera lens, then three-dimensional reconstruction is carried out by utilizing the aberration image with corrected aberration, and correction and three-dimensional reconstruction of the aberration of the camera lens are carried out step by step in the process. Different from a method of continuously predicting lens parameters and reconstructing in a reconstruction process at the present stage, the method has the advantages that the influence of multi-view inconsistency caused by aberration on reconstruction is greatly reduced, the quality of a reconstructed object is improved, the difference between a three-dimensional model of the reconstructed object and a real scene is small, and the three-dimensional information of the object is clearly displayed.

The invention relates to the technical field of unmanned ship safety control, and discloses an anti-spoofing-attack self-adaptive sliding mode control method and system for an unmanned ship and a medium, and the method comprises the steps: S1, constructing an unmanned ship T-S fuzzy model under the spoofing attack and external disturbance; s2, designing a dual-adaptive sliding mode observer based on the unmanned ship T-S fuzzy model constructed in S1; s3, designing a sliding mode controller based on S1 and S2; and S4, forming a closed-loop control system based on S1-S3 to perform sliding mode control: integrating the unmanned ship T-S fuzzy model constructed in S1, the dual-adaptive sliding mode observer designed in S2 and the sliding mode controller designed in S3 to form the closed-loop control system, and realizing sliding mode control. According to the method, the dynamic suppression capability of the composite threat can be remarkably improved, precise compensation of a spoofing attack signal and external disturbance of the ocean is realized, high-precision zero-flutter control is realized under the coexistence of the spoofing attack and the external disturbance, and an autonomous control solution with safety, robustness and real-time performance is provided for the unmanned ship.

The invention discloses a cross arm and insulator installation robot motor control method based on a T-S fuzzy model, and the method comprises the following steps: building a permanent magnet synchronous motor nonlinear model according to electromagnetism and motor dynamics; based on a sector nonlinear method, establishing a permanent magnet synchronous motor T-S fuzzy model; designing an observer to obtain a load torque estimated value; designing an L-infinity robust controller to ensure that the angular velocity of the motor can track a reference instruction; and an anti-control gain matrix solving criterion is formulated, and it is ensured that the system meets performance indexes. According to the method, the nonlinear control problem of the permanent magnet synchronous motor system is effectively solved, the influence of continuous interference signals on the system can be effectively suppressed, and the robustness of the system is enhanced.

The invention discloses an unmanned aerial vehicle guaranteed performance trajectory control method, and relates to the field of unmanned aerial vehicle tracking control, and the method comprises the following operation steps: S1, carrying out the longitudinal dynamics modeling of a fixed-wing unmanned aerial vehicle; s2, constructing a T-S fuzzy model; s3, a Fuzzy-GCC controller is designed; s4, the LMI of guaranteed performance control is solved; and S5, intelligently optimizing the parameters. According to the unmanned aerial vehicle guaranteed performance trajectory control method, the control models of the fixed-wing unmanned aerial vehicle can be integrated to obtain a unified form, so that the control law design and parameter tuning of the fixed-wing unmanned aerial vehicle are greatly simplified, a complex propulsion system and a longitudinal controller are decoupled through a lookup table, and the control efficiency of the fixed-wing unmanned aerial vehicle is improved. According to the method, the complexity and nonlinearity of the whole model are remarkably reduced while the control precision is kept, a T-S fuzzy model for longitudinal control of the fixed-wing unmanned aerial vehicle is deduced, and a GCC method and a stability criterion thereof are proposed based on the model, so that the global asymptotic stability of the designed nonlinear state feedback controller is ensured.