131 results about "Fuzzy model" patented technology

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 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 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 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 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 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 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 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.

The present application relates to a kind of facing polynomial fuzzy control system cost control stability judging method, comprising the following steps: establishing polynomial fuzzy model;According to the first SOS condition based on Lyapunov function, try to solve the first polynomial matrix and the second polynomial matrix, omit non-convex term in solving process, judge whether it is successful, if yes, based on the first polynomial matrix and the second polynomial matrix, obtain feedback gain;Based on feedback gain, according to the second SOS condition, try to solve positive definite matrix, judge whether it is successful, if yes, based on positive definite matrix, construct polynomial Lyapunov function, realize cost control analysis based on polynomial Lyapunov function.Compared with prior art, the present application solves the non-convex optimization problem in cost control in two steps, avoids the influence of the constraint existing in input matrix itself on the flexibility of fuzzy control system design, and improves the performance of control system by obtaining the numerical value of cost J.

The invention discloses a method for constructing an image classification model based on semi-supervised learning, and belongs to the technical field of image classification. According to the method, the risk coefficient of an unmarked image sample is judged, fine-grained evaluation can be carried out on an unmarked image, and the image classification accuracy is improved. A relatively high risk coefficient is set for the image of which the feature information is difficult to distinguish compared with the fuzzy model, so that the influence of the image in the model training process is reduced; and the performance robustness of the image classification model is ensured based on the integrated learning method and the baseline model. Specifically, due to the fact that uncertainty exists in the model training process of unmarked images, a plurality of semi-supervised learning subsets are constructed from an original data set through the sampling technology, a plurality of image classification models are trained based on the subsets to serve as base learners, and therefore a base learner pool is created; the final image classification model is generated from the base learner pool based on ensemble learning, and it can be guaranteed that the model performance is not weaker than that of a baseline model trained from marked image samples.

The present invention belongs to the technical field of smart substations, and specifically relates to a method and system for smart phase checking in substations. The method constructs a dynamic priority evaluation model, integrates multi-source data of four basic factors: voltage phase difference, ambient temperature and humidity, equipment vibration, and historical error, and uses a fuzzy comprehensive evaluation method to calculate weights and dynamically sort them. A multi-port phase scattering model is constructed based on the substation topology, and impedance mismatch points are analyzed with the help of a Smith chart to implement two-stage optimization. A three-layer decision input set is constructed based on the optimization model, and a fuzzy logic-driven adaptive rule engine is designed to dynamically output the operating condition level and match the phase checking strategy. Multi-dimensional quality assessment is performed through real-time monitoring of indicators such as reflection coefficient, dynamically updating factor weights, optimizing fuzzy model parameters, and integrating LSTM and graph neural networks to realize abnormal pattern recognition and compensation strategy solidification, forming a self-evolving closed-loop system, and ultimately achieving a transition from precise matching to autonomous evolution in the phase checking process.

The present invention discloses a corner tracking control method for an electro-hydraulic composite steer-by-wire system considering hydraulic time delay, belonging to the technical field of electro-hydraulic composite steering. The steps are as follows: establishing a T-S fuzzy model based on the time-varying characteristics of the electro-hydraulic composite steer-by-wire system; reconstructing the system Tube invariant set based on the characteristic that the value ranges of the electro-hydraulic composite steer-by-wire system are different under different state variables due to time-varying time delay; calculating the current optimal control quantity of the system by combining the cost function and the self-triggered Tube MPC algorithm; the present invention improves the corner control performance of the electro-hydraulic composite steer-by-wire system with time-varying hydraulic time delay.

The invention discloses a nuclear power plant reactor coolant system fault diagnosis method and a related device, and relates to the field of fault diagnosis, and the method comprises the steps: obtaining sensor data of target equipment, inputting the sensor data to a fuzzy CNN model, and obtaining a plurality of pooling feature intervals based on an interval type-2 fuzzy set; the fuzzy CNN model is determined according to the CNN model and an interval type-2 fuzzy set method; inputting all pooling feature intervals based on the interval type-2 fuzzy set into a fuzzy GRU model to obtain a plurality of hidden state intervals based on the interval type-2 fuzzy set; the fuzzy GRU model is determined according to the GRU model and an interval type-2 fuzzy set method; inputting the hidden state intervals of all the interval type-2 fuzzy sets into a fuzzy softmax model to obtain a fault diagnosis result of the target equipment; the fuzzy softmax model is determined according to the softmax model and an interval type-2 fuzzy set method. According to the invention, the fault identification precision can be improved in a complex working environment.

The invention discloses a motor modeling and non-cascade servo control method and system based on T-S fuzzy. The method comprises the following steps: establishing a mathematical model on a two-phase synchronous rotation orthogonal coordinate system to obtain state space description; obtaining a T-S fuzzy model and a closed-loop system according to the state space description; and for the closed-loop system, determining the gain of the non-cascade servo controller based on the Lyapunov stability theory. According to the invention, the control performance and the anti-interference capability of the driving system are effectively improved, and parameter adjustment and physical realization are easy.

The present invention provides a method for optimizing the trajectory deviation of the end of a multi-section arm of a concrete pump truck, comprising: S1: using a Jacobian matrix to establish a nonlinear three-dimensional space dynamic equation of a rule tracking system of a multi-section arm concrete pump truck; S2: using a fuzzy logic expression method to transform the obtained three-dimensional space dynamic equation into a fuzzy model of the nonlinear three-dimensional space dynamic equation; S3: introducing an integral sliding film function, using a BP neural network to approximate the influence of unknown position deviation, and reconstructing the solution of the learning algorithm in a robust control framework of the discrete system; S4: designing a BP neural network optimization learning algorithm based on state feedback control; the method for optimizing the trajectory deviation of the end of a multi-section arm of a concrete pump truck provided by the present invention can reduce the deviation of the motion trajectory of the end of the segment arm of the concrete pump truck, make the pouring process safer and more reliable, and is suitable for further promotion and application.

This invention belongs to the field of unmanned driving technology and discloses a trajectory tracking control method, device, equipment, and storage medium for unmanned mining trucks. The method includes: establishing a trajectory tracking error model for the unmanned mining truck; discretizing the model; counteracting actuator delay by setting a heading angle pre-aiming distance; determining the heading angle error after pre-aiming to construct the target state variable; determining the gain coefficient using a TS fuzzy model; determining the Q-weight matrix and R-weight matrix after gain based on lateral error and road curvature to achieve real-time transformation matrices; determining the optimal feedback control sequence based on the Q-weight matrix, R-weight matrix, and the discretized state space model; determining the target control quantity based on the optimal feedback control sequence and the target state variable; and controlling the unmanned mining truck based on the target control quantity. Through this method, the unmanned mining truck can provide control quantities in advance during trajectory tracking, improving the accuracy of trajectory tracking control for unmanned mining trucks.

The invention discloses a T-S fuzzy rule reduction modeling method for a photovoltaic power generation system, and belongs to the technical field of new energy photovoltaic power generation system modeling. The modeling method comprises the following steps: constructing a nonlinear dynamic model of the photovoltaic power generation system; establishing a system state equation based on the dynamic model, and dividing non-linear terms in the equation into direct non-linear terms and indirect non-linear terms; defining a antecedent variable, a membership function and a fuzzy rule, performing fuzzy linearization on direct and indirect non-linear terms by using a sector non-linear method, and giving a T-S fuzzy model; and carrying out defuzzification to obtain a T-S fuzzy photovoltaic power generation system model. According to the method, the fuzzy coupling relation between the nonlinear terms of the system is reconstructed by using the method for dividing the nonlinear terms, the fuzzy rule of the T-S fuzzy photovoltaic power generation system model is effectively reduced, and the calculation complexity of a model-based algorithm in actual engineering can be remarkably reduced.

The application discloses an unmanned ship power positioning integral sliding mode fault-tolerant control method based on a fuzzy system, and comprises the following steps: a mathematical model of unmanned ship movement under an actuator fault is established; a T-S fuzzy model of the unmanned ship corresponding to the mathematical model is established; based on the established T-S fuzzy model of the unmanned ship, an integral sliding mode surface with fault information is constructed; and the asymptotic stability of the sliding mode dynamics of the system is proved by combining the definition of performance index and related lemmas.Aiming at the nonlinear term in the T-S fuzzy model of the unmanned ship, a fuzzy logic method is used to approximate the unknown smooth nonlinear term; based on the constructed integral sliding mode surface with fault information, a suitable Lyapunov function is selected, a fault-tolerant controller and an adaptive law are designed, and the reachability of the system is proved.The application can solve the problem that the resolution and complexity of the T-S model of the unmanned ship are difficult to balance, and can also ensure that the system has robustness from the initial stage.

The invention discloses a digital intelligent simulation method for a hydroelectric generating set speed regulating system based on hydroelectric-mechanical coupling, and aims to solve the problems of low hydroelectric-mechanical coupling degree, poor controllability of a simulation tool and insufficient consideration of a water hammer effect in traditional simulation. The method comprises the following steps: establishing a hydraulic electromechanical coupling model containing hydraulic power (simulating hydraulic transient by a characteristic line method), machinery (a T-S fuzzy model self-defined speed regulator) and electricity (PSS / E tidal current + generator equation); constructing a load flow calculation interface layer for secondary development of a PSS / E Fortran API, and integrating intelligent algorithm modules of GSA, DEPSO and BP neural networks; carrying out real-time interactive simulation and optimizing PID parameters of the speed regulator; and verifying dynamic characteristics of the model based on a water hammer effect analytic solution. The method realizes hydroelectric-mechanical-electrical deep coupling, improves simulation controllability and precision, adapts to unit debugging and power grid dispatching verification, and ensures safe and stable operation of the hydroelectric generating set and the power grid.

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 belongs to the technical field of power grid control, and particularly relates to a T-S fuzzy model-based large signal stability optimization control method for a flexible interconnection DC micro-grid cluster, and the method comprises the following steps: firstly, obtaining the characteristic current of a DC micro-grid based on a distributed communication network; characteristic current between adjacent networks forms a power control layer of an interconnection converter through a proportional integral link, meanwhile, the characteristic current is multiplied by a feedback coefficient to form a system stability optimization control layer, then a T-S fuzzy model of a system is obtained by establishing a fuzzy rule, an asymptotic stability domain of a cluster single network is obtained based on a Lyapunov function theory framework, and the stability of the cluster single network is optimized. And finally, a feedback coefficient is set by optimizing the asymptotic stability domain of the system, a system feedback coefficient matrix is obtained, and it is ensured that the stability of the flexible interconnection direct-current micro-grid cluster is optimized. On the basis of not changing a communication architecture in the network, a power control layer of the interconnection converter is designed based on a sparse communication network, and mutual power assistance between direct-current micro-grids is achieved.

The present invention discloses a nonlinear parallel distributed compensation method for a grid-connected synchronous control system, belonging to the technical field of grid-connected synchronous control. First, a large-signal model of the system is established, its Jacobian matrix is obtained, a linearization point is selected, and its state space A is determined. i , membership function ω i , and an open-loop T-S fuzzy model of the system is established; then, corresponding to each state space A in the open-loop T-S fuzzy model i , a corresponding state feedback F i x is added, and its input column vector B is determined, so that the state space A of the system after adding the state feedback i -BF i has negative eigenvalues, where x is the system state column vector; the fuzzy rules and the closed-loop T-S fuzzy model of the system are determined. The present invention performs nonlinear control on the nonlinear grid-connected synchronous control system, enabling the grid-connected synchronous control system to be stable at any phase angle operating point, improving the ability of the grid-connected synchronous control system to cope with grid faults, and enabling the traditional phase-locked loop to have a faster phase-locking speed.

The application discloses a kind of based on profile model's shovel loader steering control method, device and shovel loader, belong to shovel loader control field.The present application obtains the profile information and walking information of shovel loader;According to profile information and walking information determine position deviation and direction deviation;If the position deviation is greater than the first preset threshold value simultaneously the direction deviation is greater than the second preset threshold value, according to walking information whether the vehicle body movement direction is deviated from path direction;If yes, with preset increase strategy adjustment directional control valve opening;If no, with preset decrease strategy adjustment directional control valve opening;If position deviation is greater than the first preset threshold value simultaneously direction deviation is less than or equal to the second preset threshold value or position deviation is less than or equal to the first preset threshold value simultaneously direction deviation is greater than the second preset threshold value, with preset decrease strategy adjustment directional control valve opening.Using shovel loader profile model, set fuzzy model, under the premise of guaranteeing equipment safety, reach the purpose of safe driving.

The invention discloses a helicopter demand power model calculation method and system, and belongs to the technical field of helicopter control, and the method comprises the steps: constructing a T-S fuzzy model, and dividing flight modes on a flight envelope based on the constructed T-S fuzzy model, so as to obtain a flight mode set; obtaining time sequence data in the flight process of the helicopter, and determining a current flight mode in the flight mode set based on the obtained time sequence data; and calculating the required power of the helicopter based on the determined current flight mode. A flight mode set is divided on a flight envelope through the constructed T-S fuzzy model, the required power of an engine can be well estimated by recognizing flight modes and calculating the required power in the flight process, power matching with the engine is achieved, and a basis is provided for subsequent high-speed helicopter control.

The present invention discloses a coordinated control method for a slow-dynamic unknown coal-fired power generation system based on T-S fuzzy and TD3, comprising the following steps: first, the coal-fired power generation system is decomposed into a fast subsystem and a slow subsystem using singular perturbation theory, and the original control task is decomposed into a stabilization task of the fast subsystem and a tracking task of the slow subsystem. For the fast subsystem, the slow-varying characteristics of steam pressure are utilized to select fuzzy sets on its definition domain to construct a T-S fuzzy model. The fast subsystem controller is obtained by solving the algebraic Riccati equations corresponding to multiple linear systems. For the slow subsystem, a dual-Q network is used to reduce the overestimation of the Q value, and a dynamic learning rate and batch size adjustment mechanism are introduced to accelerate training convergence. The control input of the slow subsystem is obtained by learning under the TD3 algorithm framework. The control inputs of the fast and slow subsystems are combined and acted on the original system to obtain the state information at the next moment. The intelligent agent interacts with the original system to complete collaborative optimization.