278 results about "Motor model" patented technology

The invention provides a tractor semitrailer full-working-condition active stability control system and method, and belongs to the technical field of vehicle engineering. The system comprises a kinetic model module, a reference quantity determination module and an active stability controller module. The dynamic model module constructs a linear three-degree-of-freedom tractor semitrailer model, a hub motor model and a longitudinal driver model. The reference quantity determination module generates an ideal state value according to the steady state condition of the system and corrects the ideal state value in combination with the road attachment condition. The active stability controller module is divided into three layers, an MIMO-IMFAC controller calculates additional yawing moment, a torque optimization controller optimizes tire load distribution through a quadratic programming algorithm, and a driving / braking torque distribution controller preferentially uses regenerative braking and dynamically adjusts braking torque of the semitrailer according to the yawing moment. By adopting the control system and the control method, the stability and the dynamic response capability of the vehicle under complex working conditions are remarkably improved.

The invention relates to the technical field of servo control, in particular to a high-precision current closed-loop control system and method for a servo driver, and the method comprises the steps: collecting three-phase current, rotor position, winding temperature and bus voltage, and constructing a state data set; constructing a parameter identification model fusing physical prior and an attention mechanism, deducing inductance and flux linkage parameters influenced by temperature, saturation and cross coupling, and correcting a motor model in real time; the correction parameters are injected into an FOC decoupling and feedforward link, and a self-adaptive PI regulator is linked to set the gain of a current loop on line; calculating a d-q axis voltage command according to the update model to respectively control excitation and torque components; an extended state observer is introduced to estimate load abrupt change and parameter disturbance, feed-forward compensation is carried out, and three-phase PWM is generated through anti-Parker and SVPWM; and an inverter nonlinear compensation model of a dead zone and switch delay is constructed, PWM is pre-distorted and output to a power device, and robust and low-ripple current closed-loop control of the servo motor is realized.

The method comprises the following steps: judging whether each wheel clamping force sensor works normally, if so, directly obtaining a brake clamping force as a final brake clamping force, if not, obtaining a first brake clamping force through a motor model, and if not, obtaining a second brake clamping force through a motor model; second brake clamping force is obtained through the brake friction plate model, and fusion is conducted through the fuzzy logic weight algorithm to obtain final brake clamping force; judging whether the wheel has a locking risk or not, if not, calculating the current ground longitudinal force of the wheel according to a kinetic model of the wheel, if so, triggering longitudinal force locking logic, and acquiring the current locking ground longitudinal force as the current ground longitudinal force of the wheel; calculating the current utilization adhesion coefficient of each wheel in combination with the vertical force; and determining the road surface type of the vehicle by using a fuzzy rule based on the adhesion coefficient of each wheel. According to the invention, the problem of low identification precision is solved.

The invention provides a motor parameter online identification method based on an improved golden section method, and belongs to the technical field of motor parameter identification, and the method comprises the steps: S1, obtaining a residual function of a recursive least square method with a forgetting factor when parameter identification is not carried out on a motor model for the first time; s2, detecting whether the residual function exceeds a preset threshold value or not, and executing a step S3 when the residual function exceeds the preset threshold value; s3, taking the residual function as a target function, and adjusting the forgetting factor by using a golden section method to obtain an optimized forgetting factor; and S4, carrying out motor parameter online identification by using a recursive least square method with the optimized forgetting factor. The method has the advantages that the forgetting factor is dynamically adjusted through the golden section method, the residual function serves as the target function, the optimal solution of the forgetting factor under the corresponding working condition can be found when the system changes rapidly, the tracking speed is increased, the steady-state precision is further improved, and therefore the overall performance of parameter identification is improved.

The invention relates to the technical field of motor model selection, in particular to a motor model selection method and system, and the method comprises the steps: constructing a distributed data query library, and importing a standardized feature vector into the distributed data query library to obtain a candidate motor model set through matching; obtaining the comprehensive performance score of each candidate motor model, triggering a model rejection mechanism based on a preset score threshold value, and carrying out performance parameter sorting on the reserved candidate motor models to generate a preferred motor model set; obtaining a comprehensive energy efficiency score of each preferred motor model, and establishing a Pareto frontier solution set for secondary screening to obtain a recommended motor model set; the recommended motor model is divided into a plurality of motor model selection hierarchies, the motor model selection hierarchies serve as the final motor model selection result to be output, the scientificity, accuracy and practicability of motor model selection are improved through the intelligent motor model selection process, and high-quality model selection decision support is provided for a user.

The invention discloses a magnetic lead screw motor linear speed control method based on an improved active-disturbance-rejection controller, and the method comprises the following steps: building a magnetic lead screw motor model, designing an active-disturbance-rejection control law according to the motor model, carrying out the higher-order operation of an extended state observer in a controller, and introducing an acceleration error at a reasonable position of a state equation, thereby achieving the linear speed control of a magnetic lead screw motor. And the improved active disturbance rejection controller is obtained. Compared with the prior art, the improved method provided by the invention has the advantages that the observation capability of the expansion state observer on the disturbance error is improved, so that the anti-interference performance of the control system is improved, and the method has certain universality. Simulation and experiment results show that high-quality operation of the linear driving system of the magnetic lead screw motor can be realized by adopting the improved active-disturbance-rejection control strategy.

The invention discloses a 15-degree-of-freedom whole vehicle dynamics modeling method for a steer-by-wire system, and the method comprises the steps: dividing a whole vehicle model into six dynamics modules: a vehicle body model, a nonlinear tire model, a suspension model, a wheel motion model, a motor model, and a steer-by-wire model; in addition, a tire slip angle and slip rate state quantity calculation module and a steer-by-wire control module are adopted, a whole vehicle dynamic model is established and comprises six degrees of freedom of longitudinal, lateral, yawing, pitching, tilting and vertical movement of a vehicle body, two degrees of freedom of rotation and vertical movement of four distributed wheels and one degree of freedom of vehicle steering, and the total number of the degrees of freedom is 15. The method designed by the invention is suitable for common vehicle driving conditions so as to research the control performance, the stability, the safety, the dynamic response and the characteristics of a steer-by-wire system of the vehicle and vehicle performance under different working conditions, and theoretical support is provided for optimization of future vehicle design and control strategies.

The invention is suitable for the technical field of permanent magnet synchronous motor performance improvement, and provides a motor multi-objective optimization method and system based on an improved dung beetle optimization algorithm, and the method comprises the steps: building a mathematical model of the torque ripple and dynamic response capability of a motor, and determining initial design parameters which affect the torque ripple and dynamic response capability; according to the simulation data of the stator skewed slot of the motor, determining an optimal angle meeting a first constraint condition so as to establish a motor model; based on the motor model, motor structure parameters influencing the initial design parameter sensitivity are analyzed, and the obtained motor structure parameters include the stator outer diameter, the permanent magnet thickness, the tooth width and the air gap length; according to the invention, research is carried out from the angle of a stator skewed slot, and an optimal angle with large torque pulsation reduction and small dynamic response capability reduction is obtained; the initial design parameters are subjected to sensitivity analysis to obtain structure parameters, algorithm optimization is carried out after fitting, and multi-objective optimization of the motor is achieved.

According to the ZYNQ platform-based motor simulation method provided by the invention, the ARM + FPGA heterogeneous architecture characteristic of ZYNQ is fully utilized, a motor simulation model is intelligently segmented and deployed, and a set of complete solution from model design to code generation, deployment and debugging is constructed based on a real-time Linux system. The method has the advantages of being high in universality, easy and convenient to use and low in cost, the motor model can be rapidly built according to the electrical characteristics of the motor, codes suitable for different computing resources are generated through the code generation technology, and efficient hardware-in-the-loop simulation is achieved.

The invention discloses a voltage distribution and insulation weak point prediction method based on a flat wire winding of a variable frequency motor, and belongs to the technical field of motor design and insulation evaluation. The method comprehensively considers the geometrical shape, the insulation structure, the working voltage and the like of a motor stator, establishes a complete finite element model of a flat wire winding motor, and improves the reliability of the motor. According to the method, distribution parameters such as capacitance, inductance and resistance of a cable and a motor winding are extracted, a winding 'I-type high-frequency equivalent circuit model is built, field-circuit coupling electromagnetic analysis is carried out by utilizing finite element simulation software, the process can be automatically realized through a compiled program, and a user only needs to provide an original motor model and working parameters, so that the method is simple and easy to implement. According to the method, the voltage distribution of the flat wire type motor winding under variable frequency driving can be efficiently and accurately predicted, the radar map capable of visually displaying the insulation weak point is generated, a new thought is provided for positioning the insulation weak point of the motor, the motor design is guided and optimized, and the evaluation efficiency and accuracy of the insulation performance of the motor are improved.

The invention discloses an encoder delay compensation method and system and a related device, which are used for effectively compensating angle lag caused by encoder signal processing delay in different rotating speed ranges and improving the control performance of a motor. The method comprises the following steps: under the steady-state condition of constant-speed no-load operation of a motor, acquiring current and electrical angular velocity in a motor controller and first voltage output by a control loop; calculating a second voltage based on a motor model according to the current, the electrical angular velocity and the motor operation parameters; calculating an initial angle deviation of the encoder according to the first voltage and the second voltage; performing iterative adjustment on the initial angle deviation until the difference value between the first voltage and the second voltage meets a preset error threshold value; establishing a fitting model according to the at least two groups of electrical angular velocities and the corresponding angular deviation data to obtain a compensation coefficient; and when the motor runs, performing delay compensation on the output angle of the encoder according to the compensation coefficient and the real-time electrical angular velocity.

The invention discloses a deadbeat predictive current control method based on a composite disturbance observer, and belongs to the technical field of dual three-phase permanent magnet motor control. The dual three-phase permanent magnet motor is composed of two sets of stator three-phase windings, and the space phase shift angle is 30 degrees. The method comprises the following steps: establishing a discretized motor model containing total disturbance under a double-dq coordinate system; a composite disturbance observer is designed, and direct current disturbance and alternating current disturbance are synchronously estimated through a second-order disturbance expansion state observer embedded into a generalized integrator; and performing feed-forward compensation by using the disturbance estimation value, and calculating a voltage control quantity in combination with deadbeat prediction. By introducing the second-order disturbance expansion state observer and the generalized integrator, synchronous compensation of direct current disturbance and alternating current disturbance is realized, so that current harmonics are effectively reduced, and the current waveform quality is improved. Meanwhile, complete decoupling of the current control loop and the disturbance suppression loop is realized. In addition, the method still shows high robustness under the condition of motor parameter mismatch.

The invention discloses a high-performance reaction flywheel anti-interference control method, system and device and a medium, relates to the technical field of aerospace, and solves the problems that parameters of a motor model are inaccurate, and time-varying friction torque and load torque bring difficulty to implementation of high-dynamic and high-precision control of a reaction flywheel. The control method comprises the following steps: establishing a kinematics and dynamics model of the reaction flywheel; a mathematical model for controlling the rotating speed of the reaction flywheel is established, the mathematical model considers the uncertainty of model parameters and Td, and Td represents the influence of time-varying friction torque and load torque; designing a robust disturbance observer to estimate the uncertainty of the model parameters and the Td to obtain a disturbance estimation result; and designing a rotating speed loop controller and a current loop controller according to an interference estimation result. According to the method, high-dynamic and high-precision reaction flywheel rotating speed control can be realized under the influence of inaccurate model parameters, time-varying friction torque and load torque.

The invention discloses an automobile motor recycling and cleaning system based on automatic control, and relates to the technical field of cleaning. The system comprises a basic technical feature acquisition module used for acquiring basic technical features; the initialization module is used for being connected with an automobile motor cleaning workshop; the disassembling tool information base connection module is used for connecting a disassembling tool information base; the automobile motor twinning model generation module is used for generating an automobile motor twinning model; the coordinate calibration configuration module is used for performing coordinate calibration configuration; and the complementary control iteration module is used for performing complementary control iteration. The technical problems that when a precise and complex motor structure is processed, automobile recycling and cleaning depend on automatic operation control of single mapping, and the cleaning quality is difficult to guarantee are solved, and the purposes that based on the motor model and structure information, proper disassembling tools and cleaning modes are selected, and the cleaning quality is guaranteed through real-time monitoring and intelligent adjustment are achieved. The technical effect that the automobile motor is efficiently and accurately disassembled and cleaned is achieved.

The invention relates to the technical field of motor control, and discloses a sensorless direct-current brushless motor control system which comprises a motor modeling module, a state estimation module, a deep learning prediction module, a filtering fusion module, a self-adaptive control module and a control execution module. The invention also discloses a sensorless direct current brushless motor control method, which comprises the following steps of: model establishment: constructing an electrical model and a mechanical model for describing the dynamic behavior of the motor so as to obtain a motor operation dynamic model suitable for control calculation; and state estimation: performing preliminary estimation on the rotor position and the rotor speed of the motor by utilizing the running dynamic model and combining current and voltage real-time signals acquired in the running process of the motor. According to the method, high-precision and high-stability control of the motor under different load and operation conditions is realized through adaptive control and rotor state estimation optimization technologies, real-time dynamic adjustment and commutation accuracy of the motor are ensured, and the overall control performance is improved.

The invention discloses a closed space temperature rise evaluation method and device for a Stirling cylindrical linear motor. The method comprises the following steps: building a motor model; obtaining a fluid domain model of the closed space according to the motor model; performing CFD simulation according to the fluid domain model to obtain a convective heat dissipation coefficient; acquiring loss and heat generation rate of the heat source part; obtaining a first model according to the motor model, the fluid domain model and the loss and heat generation rate; performing steady-state thermal simulation according to the first model based on a first condition to obtain steady-state temperature distribution; the first condition comprises removal of convection heat dissipation of a cylindrical linear motor and a fluid domain contact surface; importing the convective heat dissipation coefficient into the first model to obtain a second model; and taking the steady-state temperature distribution as initial temperature distribution, performing transient thermal simulation according to the second model, and taking a simulation result as initial temperature distribution of transient thermal simulation of a next time step until calculation of a preset maximum time step is completed, so as to obtain the running temperature rise of the motor.

The invention provides a motor simulator control system and method based on an ARM and double-FPGA architecture, and a computer readable storage medium, and mainly solves the technical problem that the test precision of an existing motor simulator on a motor controller is reduced due to the fact that the simulation precision of the existing motor simulator on a real motor is not enough. Wherein the control system comprises an ARM control unit, a centralized control FPGA unit and a rotary transformer FPGA unit, the ARM control unit and the centralized control FPGA unit mainly realize functions of a motor control algorithm, PWM output and the like, the rotary transformer FPGA unit mainly undertakes a rotary transformer simulation function, and the three units jointly form a whole set of motor simulator model algorithm. Therefore, accurate simulation and high-precision output of the motor model are realized.

The present invention discloses a multi-objective optimization method and system for a cylindrical permanent magnet linear motor based on NSGA-II. A sensitive parameter algorithm is used to construct a motor optimization objective function. A two-dimensional motor model is constructed according to the motor optimization objective function and the motor size. Then, motor-related data is derived from the two-dimensional motor model. A bagging algorithm is used for fitting training to obtain a training model with a higher degree of fit between training values ​​and target values. Finally, an optimal value interval of the motor optimization objective is determined according to the training model, and the NSGA-II algorithm is used to optimize the optimal interval to obtain an optimal solution for multi-objective optimization, thereby realizing multi-objective optimization of the motor and outputting a Pareto front solution set of the optimized output power and efficiency, as well as various optimization parameters.

The invention discloses a layered optimization design method for a dual three-phase permanent magnet synchronous motor, and the method comprises the steps: firstly building a motor agent model according to a motor optimization target, and carrying out the optimization of a motor body through a multi-target optimization algorithm, so as to obtain a Pareto leading edge of a motor parameter; then, Kp and Ki parameter setting is carried out on a motor model in the Pareto frontier, so that the dynamic performance of the motor is optimized; and finally, determining an optimal solution containing dynamic performance and a motor body optimization target by combining a table look-up method with a multi-target optimization algorithm. According to the method, through joint optimization of the motor body and the control system, the dynamic performance of the motor and the body optimization target can be considered in the design stage, the overall robustness and the performance consistency of the motor are improved, and it is ensured that the performance of a machining prototype is kept consistent with a simulation result.

The invention is suitable for the technical field of energy storage, and provides a flywheel energy storage charging control method, which comprises the following steps of: 1, modeling a high-speed permanent magnet synchronous motor, establishing a flywheel energy storage system type, simplifying a PMSM mathematical model, constructing a PMSM model under an ABC coordinate system, and converting to a PMSM model under a d-q coordinate system; and 2, performing model prediction current control on the MRAS, performing double-vector model prediction current control, performing model reference adaptive method, performing parameter identification precision verification based on the MRAS, and adopting a parameter step-by-step identification method based on the MRAS to solve the problem that the model prediction current control height depends on prediction model accuracy, so that the accuracy of the model prediction current control is not influenced. The problem that prediction current control performance of a double-vector model is reduced due to mismatching of model parameters is solved, the identification precision of rotor flux linkage, stator resistance and stator inductance is high, meanwhile, the speed regulation range is widened, the dynamic response characteristic is improved, and the motor model can reflect the actual situation more accurately.

The invention discloses a model prediction control method for a low-common-mode-voltage dual three-phase motor with improved steady-state performance, and belongs to the technical field of dual three-phase permanent magnet synchronous motor control. The method comprises the following steps: firstly, suppressing harmonic current by synthesizing virtual voltage vectors with a specific duty ratio, and synthesizing a candidate output voltage vector by using two adjacent virtual voltage vectors according to a predefined duty ratio; designing a value function without a weight coefficient to select an optimal voltage vector; designing a dual duty ratio technology to calculate the duty ratios of the two virtual voltage vectors for synthesizing the optimal voltage vector; a specific switching sequence in which virtual zero vectors are arranged on two sides and in the middle of a control period is designed, and the switching sequence is generated by using a phase-shifting carrier method. According to the invention, the common-mode voltage can be effectively reduced without adding any hardware circuit, the current harmonic wave and the torque ripple are obviously reduced by expanding the modulation range, the steady-state performance of the system is improved, and the method is suitable for a dual three-phase motor driving system with high requirements on performance and reliability.

The invention relates to a novel intelligent motor control method based on model predictive control. The method comprises the following steps: acquiring a two-phase rotating coordinate system current signal and a rotating angular velocity signal of a motor; measuring the noise covariance matrix to identify the stator resistance, inductance and flux linkage of the motor based on the current signal, the rotation angular velocity signal and the noise covariance matrix by adopting an online parameter identification method of extended Kalman filtering; wherein the noise covariance matrix and the measurement noise covariance matrix are set based on an improved artificial jellyfish search algorithm; a motor parameter model is obtained based on stator resistance, inductance and flux linkage of the motor, and speed and position control over the motor is achieved through model prediction control based on the motor parameter model. Compared with the prior art, the method has the advantages that the motor control convergence speed is increased, local optimum is reduced, and an accurate motor model is obtained.

The invention relates to the field of airplane airborne pod servo control algorithm design, and provides a method and a system for calculating external force disturbance resistance according to current feedback of a servo motor. The method comprises the steps that three-phase currents Ia, Ib and Ic and an electrical angle theta are collected, two-phase currents Id and Iq are obtained through Clarke conversion and Park conversion, and the Iq is defined as an external force disturbance resistance estimator IQ; establishing a set value r (t) and a deviation e (t) = r (t)-IQ, calculating an output u (t) of a PID controller, and writing the u (t) as a q-axis control reference into a driver: mapping to Vq and Vd in a voltage control mode for inverse transformation and PWM modulation, mapping to q-axis current reference Iqref in a current control mode, converting the q-axis current reference Iqref into a corresponding voltage instruction by a driver current loop, and driving a servo motor to generate an electromagnetic torque for counteracting disturbance and reactance. A complex motor model and a high-end inertial sensor are not needed, the calculation amount is small, disturbance can be rapidly and accurately estimated, and the stable control performance of the aircraft pod in a complex flight environment is guaranteed.

The invention provides a driving anti-slip control method, device and equipment for an electrically driven vehicle based on road surface recognition and a medium. The method comprises the following steps: establishing a vehicle system model comprising a single-wheel dynamics model and a motor model; a road surface recognizer is constructed based on the Burckhardt tire mathematical model and similar road surface matching, the road surface recognizer adopts a double-layer screening strategy to carry out similar road surface matching, and the optimal slip rate and the maximum road surface adhesion coefficient of the current road surface are recognized through the road surface recognizer; a composite PID-SMC controller is constructed based on the optimal slip rate and the real-time slip rate of the wheels; based on the slip rate error, a smooth switching function is adopted to determine the weight factor of a sliding mode controller, the total anti-slip correction torque is calculated, and the weights of the two controllers are adaptively adjusted for smooth control, so that the technical problem that the buffeting phenomenon is still difficult to completely eliminate under high-gain switching of the distributed electric drive vehicle is solved.

The invention relates to an electromechanical coupling rotor system starting and vibration suppression method based on fuzzy PID control, and belongs to the field of electromechanical coupling rotor systems. According to the method, an 8-degree-of-freedom rotor system mechanical dynamics model and an equivalent motor model are established through a finite element method, an electromechanical coupling rotor simulation model is established based on Matlab / Simulink, and then a fuzzy PID control module is established to be assembled with an electromechanical coupling rotor system. Finally, simulation analysis is conducted on the electromechanical coupling rotor assembled with the fuzzy PID control module and the normal PID control module, and the result shows that the fuzzy PID control module can effectively shorten the starting response time of the rotor system and the vibration amplitude during stable operation.

The invention provides a permanent magnet generator fault monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of motor fault detection, and the method comprises the steps: obtaining the historical operation data of a target permanent magnet generator; according to the historical operation data, establishing a motor model of the target permanent magnet generator in each working condition state; determining time domain features and frequency domain features of the target permanent magnet generator in each working condition state based on the motor model; and performing fault monitoring on the target permanent magnet generator based on the time domain feature and the frequency domain feature. Therefore, compared with the prior art that data acquisition is carried out through installation of a sensor, high-frequency voltage is injected through an inverter, then corresponding high-frequency inductance is calculated, and then identification analysis of specific harmonic waves is completed, the scheme in the invention can realize continuous monitoring and diagnosis, fault occurrence can be effectively prevented, and the fault diagnosis accuracy is improved. The dependence on sensors and monitoring conditions is small, and the operation efficiency and economic benefits of the whole wind power plant can be remarkably improved.

This invention discloses a dynamic weighted adaptive predictive control method and apparatus based on twin model fidelity feedback. The method includes: establishing a digital twin model of the motor to obtain the predicted current of the motor's future state, addressing the impact of motor model uncertainty and time-varying parameters on control performance; calculating the prediction fidelity of the motor's digital twin model in real time based on the predicted current and the measured current of the motor; dynamically adjusting weight factors using a fuzzy logic decision system based on the motor's real-time operating conditions and the prediction fidelity to achieve switching of the control objective; calculating the optimal voltage vector corresponding to minimizing the cost function using model predictive control theory based on the predicted current and the dynamically adjusted weight factors; and achieving operation of the permanent magnet synchronous motor at low switching frequencies based on the optimal voltage vector. This invention can effectively compensate for control delay and model uncertainty at low switching frequencies, significantly improving the high dynamic response performance and robustness of the permanent magnet synchronous motor.

A method for controlling operation of a brushless winch motor includes comparing an actual first-moment operating parameter with a predicted first-moment operating parameter in first predicted operating data to obtain a first offset, outputting a first feedback parameter based on the first offset and a motor model and adjusting operation of the winch motor based on the first feedback parameter, obtaining an actual second-moment operating parameter of the adjusted winch motor, and comparing the actual second-moment operating parameter and the predicted second-moment operating parameter to obtain a second offset, correcting the operating model of the winch motor based on the second offset, outputting the predicted second-moment operating data based on the corrected operating model of the winch motor and adjusting motor operation based on a difference between the actual operating parameter of the winch motor and the predicted second-moment operating data as a second feedback parameter.

The invention relates to a model prediction control method for temperature parameter self-tuning in a rubber mixing process. The method comprises the following steps: S1, constructing a mechanism model and a rotor motor model in a mixing heat transfer process; s2, constructing a prediction model under an incremental control method based on the mechanism model and the rotor motor model; s3, constructing an MPC controller based on the prediction model, wherein the controller adopts a genetic algorithm to set parameters; s4, the MPC controller obtains the control target, the controlled quantity, the state quantity and the disturbance quantity after discrete sampling, the control target, the controlled quantity, the state quantity and the disturbance quantity are input into a prediction model, the prediction model outputs a prediction sequence, the prediction sequence is input into a rolling optimization module of the MPC controller to obtain an optimal control sequence, and the rolling optimization module adopts the parameters set in the S3; and S5, inputting the optimal control sequence into the internal mixer for control. Compared with the prior art, the method has the advantages that the problems that high-precision control over the internal mixing temperature is difficult, control parameters are difficult to set, and real-time observation is difficult are solved.