788 results about "MATLAB" patented technology

A multi-fault diagnosis method for a power distribution network is provided. The method includes: performing short-circuit fault analysis on a line of a power distribution network by using a MATLAB platform, so as to obtain an electrical quantity-based fault information decision table; performing modeling and simulation on the fault information decision table by using a Simulink platform; denoising and collecting output training data of a neural network of the power distribution network by using a wavelet transformation method, and forming a related fault information decision table as a training sample of the neural network; and optimizing weights and thresholds of the neural network by using an improved Artificial Tree intelligent optimization algorithm, selecting some of the data as fault data, and using the neural network trained to perform fault detection.

The invention discloses a mechanical property prediction method of a composite material based on an improved sparrow algorithm-random forest, and belongs to the technical field of composite material property prediction, and the method comprises the following steps: carrying out finite element multi-scale simulation on a carbon fiber composite material to obtain an elastic constant matrix; connecting the elastic constant matrix material to a macroscopic model for macroscopic simulation to obtain sample data; a random forest RF model is constructed in Matlab software; an improved sparrow search algorithm is introduced into a random forest RF model to construct a CFSSA-SF prediction model; training a CFSSA-SF prediction model by using the training set data; and inputting test set data into the CFSSA-SF prediction model to obtain a prediction result. According to the method, the finite element method is adopted to carry out multi-scale modeling, the improved sparrow algorithm is combined with the random forest to construct the fusion CFSSA-RF prediction model, and the mechanical properties of the carbon fiber composite material under different parameters are effectively predicted.

The invention discloses a high-precision and high-reliability harmonic reducer tooth profile simulation machining design method, and belongs to the field of precision transmission. A composite cycloid flexible gear tooth profile is designed by establishing a harmonic reducer geometrical relationship model, deducing a flexible gear deformation equation, an accurate rotation angle relationship and a meshing theory based on an envelope method, and a conjugate region and a conjugate tooth profile are calculated by utilizing MATLAB. Actual deformation and theoretical deformation errors of the flexible gear are analyzed through finite element simulation, flexible gear profile parameters are optimized, an actual neutral layer curve is generated, and then a rigid gear profile is enveloped and calculated. The tooth profile design adopts a non-zero inclination angle composite cycloid, so that the meshing area is expanded by more than 30%, and the maximum stress of the flexible gear is reduced by 22.75% in combination with a linear modification method and a finite element modification method. In addition, through a multi-physical field simulation verification system (including static deformation, dynamic contact and fatigue life prediction) and a neural network optimization tooth shape parameter, the transmission performance is remarkably improved. According to the method, the torsional rigidity can be improved by 66%, and the transmission error is controlled within a high-precision range.

The invention provides a CO2 sequestration simulation method based on transfer learning and a physical information neural network, which aims at a geological carbon dioxide sequestration process by combining the transfer learning and the physical information neural network, and comprises the following steps of: simulating and generating data by using a Matlab oil reservoir simulation tool box, and constructing a neural network basic model; the physical law of carbon dioxide migration in the porous medium is trained and learned through coarse resolution, then fine tuning is performed on the generated actual geological model data by adopting a migration learning strategy to adapt to different geological structures and injection conditions, and a neural network loss function is embedded in combination with a Darwest equation and a mass conservation equation to obtain a carbon dioxide migration model. Compared with a traditional numerical simulation method, the method has the advantages that the calculation efficiency is remarkably improved while the prediction precision is kept, and the method is suitable for carbon dioxide sequestration optimization and environmental risk assessment under complex geological conditions and has good industrial popularization value and environmental benefits.

The invention provides a real-time monitoring system and method for sorting of a digital twin-driven Delta mechanical arm. According to the system, firstly, a physical experiment platform is built, and a virtual model is built through SolidWorks, 3DMAX and Unity 3D. A communication framework is designed, and wireless communication of the PLC, the sensor, the mechanical arm, the conveying belt and the digital twin system is achieved. Positive and inverse kinematics analysis and Cartesian space trajectory planning are carried out on a Delta robot, and a related algorithm is packaged into an MATLAB function and compiled into a DLL library for C # calling. The system collects joint angle data in real time based on a rotary encoder, transmits the joint angle data to a digital twin system through an OPC UA protocol, and drives a virtual mechanical arm model after resolving, so that high-precision and low-delay virtual-real synchronous pose reconstruction is realized. In addition, the system also develops a collision detection function based on a bounding box algorithm, and performs visual detection by using camera calibration and a YOLO model.

A method for predicting in-situ leaching effects of sandstone-type uranium mining deposits after blasting is provided, which comprises: based on a finite element analysis platform, establishing an HJC rock-blasting constitutive model, and carrying out a blasting numerical simulation; calibrating a cloud map of blasting crack damage by using a Kriging interpolation algorithm; establishing a COMSOL multiphysics model; establishing a coupling interface between the COMSOL multiphysics model and PHREEQC based on MATLAB, and optimizing a cycle duration and a time step; performing a geochemical reaction calculation and saving results, running COMSOL files, and inputting the results of geochemical reaction calculation into PHREEQC to obtain a dynamic cyclic iterative simulation; dynamically correcting migration coefficients of uranyl complexes by integrating an LSTM neural network, and stopping the cycle to output multi-scale dynamic simulation results of migration of uranium.

The invention discloses a Taylor space external fixation support PID control method based on an improved South goose algorithm, and belongs to the technical field of PID control, and the method specifically comprises the steps: 1, building a Taylor space external fixation support control model based on a PID controller; step 2, improving a South goose algorithm, including introducing boundary excitation and a chaotic elite strategy to initialize a population so as to give consideration to global and boundary exploration; an elitist retention and worst reset mechanism is adopted to ensure excellent solution inheritance and diversity; the global search efficiency is improved by utilizing a golden proportion reverse exploration method; multi-directional Levy flight is introduced to enhance the local optimal jumping capability; step 3, optimizing PID controller parameters by using an improved South goose algorithm to obtain optimal Kp, Ki and Kd; 4, simulating a PID (Proportion Integration Differentiation) controller of the control model by adopting MATLAB (Matrix Laboratory) and Simulink; the improved Swane algorithm can improve the adaptive control capability of the system, and enhance the stability and control precision.

The invention belongs to the technical field of new energy power generation and grid connection, and particularly relates to a wind power plant electric energy quality optimization method and system based on an improved particle swarm algorithm. Aiming at the problems of voltage fluctuation, frequency deviation and high harmonic content during grid connection of a wind power plant, a wind power plant simulation model is built through Matlab / Simulink, a multi-target fitness function is built according to the voltage deviation, the frequency deviation and the total harmonic distortion (THD), the self-adaptive inertia weight and learning factors of a particle swarm optimization algorithm are improved, and a wind power plant grid connection model is built. And dynamically adjusting voltage adjustment gain, reactive compensation amount and filter parameters. According to the embodiment, compared with a traditional method, the number of iterations is reduced by 40%, the voltage fluctuation suppression ratio is improved by 10%, the frequency adjusting time is shortened to be within 0.3 second, the THD is reduced to 2.1% from 5.2%, and the power grid stability and the electric energy quality are improved.

The embodiment of the invention discloses an energy storage optimization scheduling method. The method comprises the following steps: acquiring peak regulation basic data; taking the minimum pumped storage stabilizing net load fluctuation and the maximum pumped storage calling benefit as targets to construct an upper-layer scheduling model; on the basis of the upper-layer scheduling model, constructing a lower-layer scheduling model by taking the minimum total operation cost of the system as a target; and based on the peak regulation basic data and an MATLAB algorithm, solving the upper layer scheduling model and the lower layer scheduling model to obtain an optimal scheduling scheme. According to the optimal scheduling method provided by the invention, the regulation capacity is increased, the thermal power depth peak regulation pressure is relieved, new energy consumption is promoted, and the system economy improvement effect is optimal.

The invention discloses a multi-stage collaborative protection simulation verification method for a subway power supply system, and the method comprises the steps: building an integrated simulation model which comprises an automatic reclosing switch, a spare power automatic switching switch and an automatic change-over switch based on an MATLAB / Simulink platform, and especially carrying out the precise modeling of an advanced compensation function of ATSE. The model is verified by a power-off test, a simulation result and actually measured data are highly consistent in voltage recovery time, an error of advanced compensation action time of ATSE is less than 2ms, and dynamic characteristics of an actual system are accurately reproduced. Model simulation shows that the advanced compensation strategy of the ATSE can effectively control the voltage sag amplitude within 15%, the power supply reliability of the system is remarkably improved, and through strict'simulation-actual measurement 'double-track verification, the model accurately reproduces the dynamic characteristics of the actual system, and shows remarkable advantages in the aspect of improving the power supply reliability.

The invention discloses a power system time domain simulation method based on a physical information depth operator network model. The method comprises the following steps: initializing an MATLAB environment; calling actual example data; setting a fault type and a scene; performing time domain simulation by using a PST tool box; recording the data of the simulation curve and the state variable; preprocessing the data; generating and storing a training sample; initializing parameters of the depth operator network model; calling a training set and a test set; training parameters are set; determining key parameters; calculating a loss function; storing model parameters; and setting the feasibility and the actual effect of the fault scene verification model. By applying a DeepONet innovative algorithm, the calculation time of time domain simulation of the power system is greatly shortened, the efficiency of the method is far higher than that of a traditional numerical integration method, and the method can meet application scenes with extremely high real-time requirements; good generalization ability is achieved, and stable performance can be kept in different fault scenes; by embedding the physical constraint, the physical interpretability and reliability of the simulation result are improved.

The invention relates to the technical field of tunnel monitoring, and discloses a tunnel fan health diagnosis and early warning system and method based on multi-source fusion, comprising a sensing layer, a data acquisition and transmission layer, a data processing and fusion layer, an intelligent diagnosis and early warning layer and an application layer which are arranged in sequence from bottom to top by adopting a layered architecture, all the layers cooperate to achieve tunnel fan collision safety and equipment health two-dimensional management and control. Vibration, displacement, strain, temperature and power multi-source monitoring data are fused, single-parameter monitoring limitation is broken through, wind turbine typical faults such as bearing damage, coupling misalignment and foundation looseness and specific risks such as over-limit vehicle collision are covered, and a full-dimension monitoring system is constructed. By means of a deep belief network (DBN), a convolutional neural network (CNN) and an MATLAB core algorithm, and in combination with a fault-feature mapping library and a multi-evidence combined judgment mechanism, the fault diagnosis accuracy is greatly improved.

The invention relates to the technical field of metal material processing engineering, discloses a full-automatic full-process modeling method and system of crystal plasticity finite element model for laser welding weld, which adopts MATLAB to call ABAQUS and PYTHON for co-simulation programming. In this text, a full-automatic modeling method of the crystal plasticity finite element model for laser welding weld is proposed, and a one-key crystal plastic finite element model for laser welding weld is established based on EBSD experimental data. In addition, the full-process full-automatic modeling method of crystal plastic finite element for laser welding weld is universal, and can be used for the establishment of the crystal plasticity finite element model for the microstructure of the base material of aluminum alloy, titanium alloy, magnesium alloy, stainless steel and other materials.

The invention discloses an innovative pit body volume detection system based on a three-dimensional laser scanning technology. Acquiring the volume point cloud information of the pit body by adopting a three-dimensional laser scanning technology; the volume is calculated through double-engine cross validation, two algorithms are used for cross validation, and it is ensured that the volume calculation result relative error meets the requirement; comprising the steps that a Matlab layered integral algorithm is adopted, specifically, discrete layered volume integration is achieved through elevation layered cutting, boundary polygon generation of a polyshape function and AlphaShape geometric reconstruction; and a Geomagic curved surface reconstruction algorithm: generating a watertight grid model based on an NURBS curved surface technology, and completing continuous volume calculation. According to the method, a non-contact measurement system is established, so that the measurement efficiency is greatly improved; a three-dimensional point cloud digital processing platform is created, full-process digitalization of scanning data automatic registration, boundary recognition and volume calculation and real-time visual output of measurement results are achieved, secondary errors caused by manual intervention are reduced, the project acceptance data traceability requirement is met, and the measurement comprehensive error rate is greatly reduced.

The invention discloses a synchronous generator excitation method based on mathematical modeling and gain scheduling, and relates to the technical field of synchronous generator control, and the method comprises the steps: building a synchronous generator global dynamic mathematical model comprising the electromagnetic dynamic characteristics of a stator winding, a rotor winding and a damping winding through a modeling module; a discretization simulation platform is constructed in a Matlab / Simulink environment by utilizing a simulation module, model parameters are corrected, dynamic adjustment is realized through a gain scheduling excitation controller, an automatic voltage regulator (AVR) adopts PID (Proportion Integration Differentiation) control and comprises a voltage deviation integral separation strategy, and a power system stabilizer (PSS) dynamically adjusts parameters of a lead-lag compensator based on active power. According to the method, the deviation between a model and an actual system can be reduced by more than 30%, the electromechanical oscillation damping ratio within the rated power range of 10%-150% is increased by 25%-40%, the harmonic content of exciting current is reduced to be lower than 5%, the stability of an electric power system and the electric energy quality are remarkably improved, the engineering practicability is high, and the method is easy to transform and upgrade on an existing synchronous generator.

The invention relates to a porous medium modeling method based on a Lagguerre-Voronoi method, and the method comprises the steps: firstly generating a group of sphere data with the sphere diameter in logarithmic normal distribution by utilizing MATLAB software; using LAMMPS software to randomly stack the group of spheres in the fixed cubic space to obtain the sphere center coordinate and diameter of each sphere; the method comprises the following steps: randomly stacking spheres, performing space division on the randomly stacked spheres by using a Lagguerre-Voronoi method to obtain a corresponding three-dimensional Lagguerre-Voronoi graph, retaining edges and vertexes of each sphere division space in the three-dimensional Lagguerre-Voronoi graph, and generating a porous medium skeleton based on the edges and vertexes of each sphere division space; and finally, establishing a porous medium model of a required framework section shape according to the porous medium framework. Compared with a commercially available commercial foam structure, the porous medium model established by the invention can reproduce an actual foam structure more truly, and internal pores have good performance in the aspects of disorder and connectivity.

The invention discloses a radar radiation source individual identification method based on multi-domain feature joint judgment. The technical scheme comprises the following steps: step 1, signal preprocessing; 2, original signal detection and parameter measurement; 3, signal feature extraction and multi-feature-domain conjoint analysis are carried out; and step 4, signal characteristic quantity visualized visual display is carried out. Therefore, multi-domain feature extraction and recognition of the radar radiation source individuals are realized. According to the method, the multi-domain features of the radar radiation source signals are extracted, multiple kinds of reference information are provided for distinguishing a radar carrier platform, the problem that the single feature recognition effect is poor is solved, and the radar radiation source individual recognition accuracy is improved; a set of powerful tool is provided for a user by utilizing an MATLAB GUI user interface prototype, so that radar signals can be better understood and analyzed. A user can realize comprehensive analysis from an original signal to multi-dimensional fine characteristics through the interface, so that the user can deeply research signal characteristics, and more accurate decision making and further research are facilitated.

The invention discloses an insulating pull rod lightweight design method based on a multi-objective optimization algorithm, and aims to solve the problems of heavy weight and heavy workload of a bearing tool in hot-line work of an ultra-high voltage transmission line. The method comprises the following steps: setting electrical and mechanical property parameters of the insulating pull rod; then, a three-dimensional model is built through SolidWorks, and finite element analysis is carried out through COMSOL; then, an optimized data set is obtained through joint simulation of MATLAB, COMSOL and SolidWorks; secondly, establishing a mathematical optimization model, solving by adopting a particle swarm-grey wolf optimization algorithm, and improving the algorithm through Sine-Tent-Cosine chaotic mapping population initialization and a crisscross strategy; and finally performing simulation verification on the optimization scheme. The device is applied to the lightweight design of large-load insulating force-bearing tools, effectively shortens the development period of the tools, saves the production cost, improves the safety coefficient of hot-line work, and remarkably reduces the workload of hot-line workers.

The present disclosure provides a semi-physical simulation verification method for a control strategy of a distributed hybrid power system, including the following steps: designing a rapid prototyping controller; completing a connection between a semi-physical simulation test platform and an upper computer; building a distributed hybrid power system simulation model using MATLAB / Simulink; and forming a code in Simulink and generating a MATLAB Function module, setting a signal output module for a control signal output under a control strategy, and establishing a corresponding relationship between the signal output module and an output signal interface of the rapid prototyping controller. According to the present disclosure, the test cost generated during verifying the feasibility of the control strategy of the distributed hybrid system is effectively reduced.

The invention discloses a method for automatically calculating the waviness of an aircraft profile in an MATLAB environment, and the method comprises the steps: 1, obtaining the plane section point location data of the waviness of the aircraft profile based on the reverse point cloud data of the aircraft profile; and step 2, automatically calculating the airplane profile waviness in the MATLAB environment, including plane classification of point location data, three-dimensional coordinate system conversion, two-dimensional wave crest and wave trough point location calculation, wave crest and wave trough point coordinate system reverse conversion, waviness calculation and form report output on the point location data of the plane cross section obtained in the step 1 through a program in the MATLAB environment. Therefore, automatic calculation of the airplane appearance waviness is completed. According to the invention, the problem that the existing detection mode for the airplane profile corrugation is difficult to have convenient operability, detection precision and detection efficiency at the same time is solved.

The invention relates to the field of electric power system detection, and provides a transformer substation grounding grid corrosion detection and positioning method based on an improved optimization algorithm, which comprises the following steps: detecting a grounding grid by using a ground penetrating radar device and an electromagnetic induction instrument, obtaining GPR and EMI data, inputting the data into an MATLAB / Simulink platform for analog simulation, respectively carrying out space-time convolution processing on the received GPR and EMI data, and carrying out optimization processing on the GPR and EMI data; extracting feature information of time and space dimensions; a self-attention algorithm is applied to a processing result of space-time convolution, a long-range dependency relationship is captured, meanwhile, dynamic weight distribution is carried out on different dimension features through self-adaptive gating fusion, and feature information of time and space dimensions of GPR and EMI is fused; comparing the fused feature information with pre-stored feature information of an original grounding grid to generate a detection result; according to the method, accurate detection and positioning of corrosion of the transformer substation grounding system are realized by improving a multi-modal data fusion framework of the space-time convolutional neural network and the self-attention mechanism.

The invention provides an intelligent algorithm optimization gate adaptive steady-state adjustment method, and relates to the field of water conservancy projects. The intelligent algorithm optimization gate self-adaptive steady-state adjustment method comprises gate adjustment system model building, gate feedback adjustment algorithm definition, complex system transfer function fitting, MATLAB / Simulink simulation verification analysis and one-dimensional hydraulic model verification, and specifically comprises the steps of firstly building a gate self-adaptive steady-state adjustment model, defining a gate feedback adjustment algorithm, and performing one-dimensional hydraulic model verification; considering that a gate adjusting system is a large-time-delay and strong-coupling system, fitting is conducted on a transfer function according to actually-measured water level and flow data, then an IPSO-Fuzzy PID control model is established according to the principle and characteristics of an IPSO optimization algorithm, three simulation models of PID control, Fuzzy PID control and PSO-Fuzzy PID control are established based on MATLAB / Simulink, and a simulation model is established for verifying the self-adaptability of the optimization model. Step signals are applied to simulate external interference, simulation comparison is carried out, the built model is utilized, actual measurement data are input, and optimal control over the gate self-adaptive steady-state adjusting system through an intelligent algorithm is achieved.

The invention relates to an alternating-current trunk line or an alternating-current power distribution network, in particular to a method for improving the stability of a wind power system through combined control of network construction type energy storage and a nine-switch UPQC. The method comprises the following steps: firstly, proposing an active regulation and inertial support control method based on a virtual synchronous machine mechanism for network construction type energy storage; and secondly, by constructing a coordination control framework, dynamic response complementation of the energy storage system and the UPQC under disturbance is realized, so that the overall frequency stability and voltage supporting capability of the system are improved. And finally, building a system model in Matlab / Simulink, setting three typical working conditions of wind power fluctuation, frequency disturbance and serious voltage drop, and carrying out scheme validity verification. Simulation results show that the method can effectively stabilize the wind power output fluctuation, enhance the frequency and voltage support capability, and significantly improve the stability of a wind power system.

The invention discloses a method for simulating partial discharge in a GIS (Gas Insulated Switchgear) under X-ray excitation. The method comprises the following steps: calculating initial electric field distribution in the GIS in simulation software; importing the initial electric field distribution into an MATLAB model, defining each simulation parameter, and obtaining an initial effective electron generation rate in the SF6 gas in the GIS and a photoelectron rate generated under the action of X-ray photoionization; whether partial discharge is generated or not is judged according to the internal electric field intensity of the GIS and the generated electron rate, conduction attenuation of charges in SF6 gas is simulated, and the numerical value change process of charged particles is described; after partial discharge is generated, the charge quantity of partial discharge and the discharge repetition rate are output; according to the method, the change of electrons in the partial discharge process is quantitatively described through a mathematical expression, the generation of partial discharge is judged according to two characteristic quantities, namely the electric field intensity and the number of electrons, and the obtained partial discharge quantity and the discharge repetition rate are close to those of actual discharge.

The invention relates to a MATLAB working condition prediction and ANSYS matching surface creep analysis method, and belongs to the technical field of mechanical engineering structure connection analysis and monitoring and computer-aided engineering software integration. The method comprises the following steps that a user inputs geometric parameters and typical working conditions through an MATLAB GUIDE interaction interface, and a system can generate a theoretical response curve of the matching surface pressure changing along with the magnitude of interference, the load and the rotating speed in real time and is used for evaluating the maximum allowable working parameter range. And then ANSYS is called to carry out batch finite element simulation, and actual contact pressure distribution and a creep strain cloud picture are obtained, so that the influence of different working condition combinations on the creep behavior of the matching interface is quantified. The method supports visual comparative analysis under the combination of multiple working conditions, and a user can intuitively master the evolution law and the creep development trend of the pressure distribution of the matching surface. The interactive critical failure discrimination module integrated in the system can automatically identify typical contact failure situations and perform significant annotation in images.

The invention relates to an air suspension controller function automation test system and a test method. The air suspension controller function automation test system comprises a cabinet which comprises a board card assembly and a real-time processor, and the board card assembly comprises an IO board card and a load board card; a tested air suspension controller; a real load; a CANoe software, a Notepad + + software and a MATLAB / Simulink software are operated on the upper computer; during working, the upper computer generates a control signal to trigger a test, the real-time processor sends an analog sensor signal, the controller calculates a target current, the shunt drives a real load and model update, and the controller generates an observation signal and returns the observation signal to the upper computer after a model state is fed back. The system and the method can realize full-working-condition automatic testing and improve the efficiency and the coverage range.

The invention provides a metro station air-conditioning water system energy saving method based on a Modelica model and a genetic algorithm, and relates to the technical field of rail transit building energy optimization and intelligent control, the method comprises the following steps: establishing a simulation model of a metro station air-conditioning water system in Dymola software based on a Modelica language; performing population initialization on the established simulation model, realizing a genetic algorithm by utilizing MATLAB, and taking minimization of the total energy consumption of the air-conditioning water system as a target function to obtain an optimization control strategy; according to the optimization control strategy, a Dymola-MATLAB joint simulation interface is constructed through Python; and according to the joint simulation interface, an optimal control strategy of the subway station air conditioner water system is obtained through genetic algorithm iterative optimization. According to the method, through the technical path of'precise modeling-intelligent optimization-closed-loop control ', the industrial problems of'high energy consumption and difficult regulation and control' of the subway station air conditioning water system are systematically solved, and an innovative solution is provided for intelligent rail transit construction.

The invention relates to a new energy GCI dq impedance acquisition method based on a BKA-BiLSTM model, and the method comprises the following steps: firstly, carrying out the mechanism modeling of a new energy grid-connected inverter based on the Norton's theorem, and analyzing the nonlinear mapping relation between the equivalent dq impedance of the new energy GCI and the port characteristics; secondly, establishing a new energy unit grid-connected system simulation model by utilizing MATLAB / Simulink software, and acquiring a data set required by BiLSTM neural network training under various working conditions; secondly, training a BiLSTM neural network by utilizing simulation data, and optimizing hyper-parameters of the BiLSTM by adopting a black-wing optimization algorithm (BKA); and finally, an RT-LAB hardware-in-loop test technology is combined with a small perturbation method to scan impedance characteristics of the GCI, and an actual impedance value required by model verification is obtained. Through example simulation analysis, the correctness of the GCI dq impedance identification method provided by the invention is verified.

The invention discloses an adaptive neural network fixed time control method for an electro-hydraulic servo system with model uncertainty, and the method comprises the related steps: carrying out the modeling of an electro-hydraulic servo system, and obtaining a state equation; defining a consistency tracking error z1 of a valve response position of the electro-hydraulic servo system, and designing a first virtual control law alpha1; the method comprises the following steps: designing a first virtual law alpha 1 and a second virtual law alpha 2, defining a consistency tracking error z2 of the response speed of an electro-hydraulic servo system valve, designing a second virtual law alpha 2 and a self-adaptive law # imgabs0 #, defining a consistency tracking error z3 of the pressure borne by the electro-hydraulic servo system valve, designing a final actual control law u and a self-adaptive law # imgabs1 # based on a Matlab / Simulink simulation platform and an electro-hydraulic servo experiment platform, and carrying out an experiment on the algorithm. The invention provides self-adaptive neural network fixed time control of an electro-hydraulic servo system, and the rapid convergence performance and the steady-state precision of a hydraulic system under the condition that a hydraulic model is uncertain are improved. Strict theoretical derivation proves that the electro-hydraulic servo system has actual fixed time stability in different system initial states, so that all system state errors are converged to be close to zero within fixed time and are irrelevant to initial conditions of the electro-hydraulic servo system. Finally, simulation and experiment results verify the effectiveness of the control method.

The invention discloses a coil structure-based length self-sensing method for high polymer twisted artificial muscle, which comprises the following steps of: firstly, enabling the artificial muscle to be equivalent to N equidistant circular current loops, establishing an inductance calculation formula by combining a Biot-Savart law and a magnetic field superposition principle, and breaking through the limitation of a traditional solenoid model on a helical angle; then through MATLAB numerical simulation and iterative computation, fitting to obtain a power function describing the relation between inductance and length; and finally, proposing a parameter dynamic correction mode based on single-time initial inductance measurement according to complex factors such as actual magnetic flux leakage and skin effect, and realizing rapid adaptive calibration by adjusting a parameter b. In a word, the method creatively combines theoretical modeling with experimental correction, the sensing precision in a dynamic stretching scene is remarkably improved while the inductance-length macroscopic trend prediction capability is ensured, dependence on a large amount of experimental data is not needed, and the method has good engineering applicability and is worthy of popularization.