203 results about "Gain parameter" patented technology

The invention provides an unmanned aerial vehicle motor rotating speed control method based on active disturbance rejection. The method comprises the following steps: 1) measuring the rotating speed of a motor by adopting a high-precision rotating speed sensor based on a quantum effect; 2) designing a tracking differentiator TD to smooth an expected rotating speed, and outputting a rotating speed tracking signal and a rotating speed tracking signal change rate; 3) introducing a dynamic weight matrix to form a dynamic weight expansion state observer DWESO, and taking an output state variable estimation value and a total disturbance estimation value as the input of the NLSEF; 4) automatically searching the optimal combination of the gain parameters of the DWESO by adopting an ALA (artificial Louver Mouse Algorithm), so that the optimal state variable estimation value and the optimal total disturbance estimation value are output to the NLSEF; compared with the prior art, the method for controlling the rotating speed of the unmanned aerial vehicle has the advantages that the dynamic response speed, the anti-jamming capability and the parameter adaptability of the system are remarkably improved, and the flight performance and the robustness of the unmanned aerial vehicle are improved.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention discloses a control method applied to self-piercing riveting, and relates to the technical field of self-piercing riveting control. According to the method, riveting material data are collected in real time through a multi-source data fusion interface, the high-dynamic deformation process of a riveting head is captured, deformation rate and rebound value characteristic values are extracted, and a multi-dimensional process parameter set is constructed. And comprehensively analyzing the hardness, thickness and plastic dissipation characteristics of the material, and establishing an anti-deformation capability evaluation function. By introducing the fluctuation characteristics of the punching force, the nonlinear relation between the punching force and the elastic deformation of the riveting rod is analyzed, a hybrid compensation model is constructed, and a riveting dynamic coupling compensation vector is output. And generating a compensation weight matrix through an adaptive optimization algorithm, and dynamically adjusting the stamping force and the displacement of the riveting rod in combination with an anti-saturation control strategy. And dynamically adjusting a gain parameter through multi-scale filtering and a Lyapunov stability criterion, and triggering iterative updating of a compensation weight parameter during out-of-tolerance so as to optimize a control effect. Riveting precision and stability are improved, and the service life of equipment is prolonged.

The invention belongs to the technical field of phase control cabinet phase regulation and control, and particularly relates to a phase control cabinet regulation and control system and method based on multistage phase compensation. According to the method, the voltage waveform and the current waveform output by the phase control cabinet are preliminarily compensated so as to reduce the initial phase deviation, environmental parameters are matched with a pre-stored interference feature library, the type of an interference source can be identified, and the interference source is quantified as a thermal noise contribution value to calculate an interference intensity prediction value in a prediction window, so that the prediction accuracy is improved. And then providing data support for secondary compensation through a phase residual error output by an interference intensity predicted value, determining a compensation gain parameter according to a dynamic error coefficient during secondary compensation, performing secondary compensation on a voltage waveform and a current waveform to enable a phase deviation to be converged to a preset range, and according to the phase convergence information after secondary compensation, determining the interference intensity predicted value according to the phase convergence information after secondary compensation. And the sampling frequency and the compensation weight factor of the sensor array are reversely adjusted to form a closed-loop compensation control link, so that continuous optimization and improvement of the compensation process are realized.

The invention relates to the technical field of flight control, in particular to a hybrid control FPV unmanned aerial vehicle flight control system and method. The method comprises the following steps: acquiring triaxial angular velocity data of the unmanned aerial vehicle in real time by using an inertial navigation system, eliminating high-frequency noise and performing zero offset calibration; constructing a corresponding linear extended state observer to adjust a gain parameter in real time based on an absolute value of an observation error, and filtering tiny noise disturbance through a dead zone function to realize high-precision estimation of the system state and total disturbance of the unmanned aerial vehicle so as to obtain an error and an error change rate; and designing a self-adaptive PID control law to dynamically adjust PID control quantities corresponding to proportion, integral and differential gains, carrying out dynamic weighted stacking according to a flight mode, generating a three-axis control quantity, carrying out flight control on the FPV unmanned aerial vehicle, and meanwhile, realizing real-time visual verification of a control effect corresponding to the flight control. According to the invention, high-precision and high-robustness attitude control can be realized.

The invention relates to the technical field of high-precision positioning, and discloses a linear module high-precision positioning control method and system, and the method comprises the steps: carrying out the feature point extraction and feature point tracking of a motion image sequence of a linear module, and obtaining displacement measurement data and vibration feature data; performing nonlinear compensation on a linear module according to the displacement measurement data and the vibration characteristic data, and constructing a standard linear state equation; performing sliding mode surface design and gain parameter calculation based on the standard linear state equation to obtain an initial sliding mode controller parameter; track interpolation and motion feedback are carried out on the linear module according to the initial sliding mode controller parameters, and real-time position deviation data are obtained; the initial sliding mode controller parameters are iteratively optimized based on the real-time position deviation data, and target sliding mode controller parameters are obtained.According to the method, impact in the acceleration and deceleration process is reduced, system vibration is effectively reduced, and the positioning precision is improved.

The invention discloses an open-phase self-fault-tolerant control method for a phase-shift-free dual three-phase permanent magnet synchronous motor. The method can automatically adjust a current distribution relation in the case of an open-phase fault and suppress torque pulsation in combination with neutral point voltage compensation. Comprising the following steps: driving two pairs of three-phase winding modules of the phase-shift-free DTP-PMSM in parallel by adopting two two-level three-phase voltage source inverters, and providing a mathematical model of the motor; current distribution is automatically adjusted; compensating the voltage of the neutral point, and calculating the offset of the fault module; the current is redistributed to suppress the torque ripple after the fault, and the secondary oblique wave component generated by the phase failure fault is eliminated; a current loop controller is improved through dead-beat predictive control; the speed ring controller is optimally designed into an improved sliding mode controller; an improved double-loop controller system is formed by combining improved sliding mode control and dead-beat prediction control, high-gain parameters are set to improve the dynamic response performance of the system, and a new self-fault-tolerant control system is formed.

The invention discloses a wind power generation system control method and system based on an improved recurrent neural network (RNN), and belongs to the field of wind power generation system control. The method comprises the following steps: training an improved RNN model by adopting a training data set, and carrying out forward propagation on an input vector to obtain gain data; quantizing a loss value between the gain parameter and an ideal gain parameter, constructing a loss function according to the loss value, obtaining a weight gradient of each layer by using a gradient back propagation algorithm, updating a network weight according to the weight gradient and in combination with a momentum gradient descent algorithm, applying amplitude constraint to a recursive weight, and performing iterative training to obtain an ideal gain parameter. A trained RNN network model is obtained; obtaining a gain parameter of the wind power generation system according to the trained RNN network model, and controlling the operation state of the wind power generation system according to the gain parameter; according to the method, PI controller self-adaption is achieved by introducing the recurrent neural network, the system response time, static errors and overshoot can be remarkably reduced, and therefore the system performance is improved.

The invention relates to the field of data processing, in particular to a rapid temperature change test box temperature control method and system, and the method comprises the steps: collecting the internal temperature and external temperature of a test box in real time; calculating the internal and external temperature change consistency of the local time period at the current moment; calculating a correction value of the internal temperature at the current moment by using the internal and external temperature change consistency; and presetting a gain parameter vector, calculating an adjustment value of the gain parameter vector at the current moment, inputting the adjustment value of the gain parameter vector at the current moment and an error between the correction value of the corresponding internal temperature and the target temperature in the test box into a PID controller, outputting a control signal, and performing temperature control on the test box according to the control signal. According to the invention, by adaptively adjusting the gain parameter, the overshoot caused by too fast system response speed and the oscillation phenomenon caused by the overshoot can be effectively avoided, so that the temperature change in the test box is more stable, and the stability of the temperature control process is ensured.

The invention discloses an IMU attitude calculation method, device and equipment and a storage medium, and the method comprises the steps: obtaining IMU original data which comprises a three-axis acceleration measurement value and a three-axis angular velocity measurement value; dynamically updating gain parameters of a Mahony complementary filtering algorithm based on the three-axis acceleration measurement value, the gain parameters including a proportional gain parameter and an integral gain parameter; based on the gain parameter, correcting the triaxial angular velocity measurement value to obtain a target triaxial angular velocity value; and calculating body attitude information based on the three-axis acceleration measurement value and the target three-axis angular velocity value. Compared with a traditional Mahony algorithm adopting fixed gain parameters, the method can reduce error fluctuation of the airframe under rapid movement and improve the accuracy of attitude calculation.

A hybrid controller and method for mitigating frequency disturbances in a multi-area power plant including multiple thermal energy generators and renewable energy sources includes a cascaded fractional model predictive controller (CFMPC), first and second fractional-order proportional-integral-derivative controllers (FOPID-1 and FOPID-2) and a sooty terns controller. The CFMPC generates a minimized area central error (ACE) signal based on minimizing a controlled fitness equation, an ACE signal and a load power disturbance signal (ΔPL). The FOPID-1 generates a frequency disturbance correction signal based on the frequency disturbance value (Δfi). A combined frequency correction signal is generated by adding negative values of the minimized ACE signal and the frequency disturbance correction signal. The FOPID-2 receives the combined frequency correction signal and generates a frequency error correction signal. The sooty terns controller generates optimized gain parameters and transmits the optimized parameters to the FOPID-1 and the FOPID-2 to mitigate the frequency disturbances.

The invention discloses a double-feedback control debugging method of an ultra-precision servo system, and relates to the technical field of semiconductor manufacturing equipment. In order to solve the defects that in the prior art, coupling interference is serious in the debugging process, frequency domain performance optimization is insufficient, filter configuration does not have adaptivity, and an efficient anti-interference debugging mechanism under multi-source disturbance is lacked, the technical scheme provided by the invention comprises the steps that a double-feedback servo system comprising a speed ring and a position ring is constructed; debugging a speed feedback loop, optimizing controller gain parameters based on frequency domain analysis, and outputting speed loop response characteristics; a position feedback loop is debugged, and position loop controller parameters are configured in combination with speed loop response and acceleration sensor signals; the notch filter is automatically adjusted according to the dynamic vibration characteristics; and frequency domain self-adaptive debugging of the double-feedback controller is completed. The method is suitable for servo control debugging work of high-precision motion systems such as a wafer carrying table in semiconductor manufacturing equipment, and is especially suitable for nanoscale positioning scenes such as a photoetching machine and measuring equipment.

The invention provides a signal gain self-adaptive control method based on dynamic feedback, which relates to the technical field of self-adaptive control, and comprises the following steps: carrying out initial gain control on an input signal to obtain a system output signal; the feedback quantity of the system output signal is collected, and the filtered actual output signal is obtained through filtering processing; based on the expected output signal and the actual output signal, calculating multi-order errors, including an amplitude error, a first-order derivative error and a second-order derivative error; calculating a collaborative weighting function according to the multi-order error; constructing a comprehensive error cost function based on the multi-order error and the collaborative weighting function; according to gradient information of the comprehensive error cost function and a product term of a second derivative error and a collaborative weighting function, updating a gain parameter by adopting a dynamic step length; and performing gain control on the input signal by using the updated gain parameter to obtain a new system output signal. According to the invention, dynamic self-adaptive adjustment of signal gain parameters can be realized, so that the response capability of the system to rapid dynamic change is improved.

The invention discloses a method and equipment for controlling the virtual precession rotating speed of a micro-hemisphere gyroscope in a full-angle mode and a medium. The method comprises the following steps: enabling the micro-hemisphere gyroscope to be in the full-angle mode through three loops of amplitude, orthogonality and frequency tracking; applying a control force in the angle control equation to force the standing wave to self-rotate at a corresponding virtual angular velocity; in a standing wave precession state, identifying a total loop gain parameter in real time by using an adaptive identification technology; and the excitation voltage is multiplied by an external given excitation voltage to obtain a compensated excitation voltage, so that on-line compensation of the standing wave virtual procession is realized, and the accuracy and stability of the rotating speed of the virtual procession are improved.

The invention discloses a robot joint dynamic precision control method. The method comprises the following steps that the interaction torque, the angle and the angular speed of a robot joint are obtained; based on the interaction torque, the angle and the angular velocity of the robot joint, a robot joint dynamic model containing friction and external disturbance is constructed; designing a super-spiral sliding mode control law based on the robot joint kinetic model; optimizing a sliding mode gain parameter in the super-spiral sliding mode control law based on a PPO algorithm to obtain an optimized super-spiral sliding mode control law; and controlling the dynamic precision of the robot joint based on the optimized super-spiral sliding mode control law. According to the method, the MEMS torque sensor and deep reinforcement learning are combined, and a new effective method is provided for robot joint dynamic precision control.

The invention relates to a Markov jump network system control method under DoS attack based on a phototropic growth algorithm. The method comprises the following steps: establishing a networked system model with Markov jump parameters; an energy-limited DoS attack model is constructed; introducing a dynamic event triggering mechanism, and determining a triggering moment based on a measurement error, a weighting coefficient, a design parameter and an internal dynamic variable; based on a networked system model and an energy-limited DoS attack model, a dynamic event triggering mechanism is fused to construct a closed-loop system dynamic model; establishing a hybrid anti-interference performance index; performing global optimization on gain parameters of the closed-loop system dynamic model by adopting a phototropic growth algorithm to obtain an optimal controller gain; and constructing a controller under the energy-limited DoS attack, and realizing the control of the Markov jump network system under the DoS attack. The finally constructed controller can realize high robustness, low communication burden and excellent dynamic performance control of the Markov hopping network system in an energy limited DoS attack scene.

The invention provides an attitude control system for a short-wave satellite, and relates to the technical field of data processing, and the attitude control system comprises the steps: inputting a three-axis synthesis control torque instruction into a reaction flywheel controller, converting the instruction into a flywheel motor rotating speed adjustment instruction, and driving a reaction flywheel to execute; adjusting and analyzing the continuously updated attitude deviation angle based on the actual attitude change after the execution of the driving reaction flywheel, obtaining the updated gain parameter of the sliding mode controller, and feeding back the parameter of the updated sliding mode controller; performing control based on the updated parameters of the sliding mode controller, performing direction consistency verification on a newly output sun direction vector measurement value, and verifying an actual included angle; if the actual included angle is smaller than the preset precision threshold value, a new round of data acquisition cycle is triggered, and closed-loop control is achieved. According to the invention, the timeliness and effectiveness of attitude control are ensured, and the control efficiency and reliability are improved.

The invention discloses a reinforcement learning-based PID (Proportion Integration Differentiation) control method for a hydraulic mechanical arm, which belongs to the technical field of heavy-load hydraulic mechanical arm control and comprises the following steps of: S1, acquiring hydraulic cylinder displacement data of the hydraulic mechanical arm in an experiment process by utilizing a displacement sensor; s2, designing a PID-like controller based on the obtained hydraulic cylinder displacement signal and the hydraulic cylinder instruction displacement signal; s3, automatically adjusting a time-varying gain by using an evaluation neural network-execution neural network architecture, and correcting a gain parameter of the PID controller in real time to obtain an optimal gain parameter of the PID controller; and S4, realizing high-precision tracking control of the hydraulic cylinder based on the optimal control parameters of the PID controller. The method is used for compensating for strong nonlinearity, unknown disturbance, multi-joint coupling and the like existing in mechanical and hydraulic systems, the tracking precision of a hydraulic mechanical arm system is improved, the complex and time-consuming manual parameter adjusting process in traditional PID control is avoided, the structure is simple, and meanwhile the strong self-adaption and learning capacity is achieved.

The invention discloses a self-adaptive fuzzy PID flow field control method and device, and the method comprises the steps: generating an initial PID gain parameter in real time through a fuzzy reasoning module, introducing an RBF neural network module to carry out the dynamic optimization compensation of the initial parameter, and generating a correction gain. And a final PID parameter is dynamically synthesized by adopting a weighted fusion formula, and a weight coefficient is adjustable so as to balance the contribution of fuzzy rules and neural network learning. The nonlinear approximation capability of the RBF network is utilized, network parameters are updated in real time through a gradient descent method, a Jacobian matrix of a controlled system is output, and the sensitivity of the control quantity to input changes is accurately recognized. The parameter updating rate is dynamically adjusted through a momentum item and an exponential decay function in combination with environmental sensor data, flow feedback and historical deviation, and a closed-loop control loop of monitoring, fuzzy reasoning, RBF optimization, PID output and feedback is formed. The accuracy of flow field control is improved.

The invention relates to the technical field of resonance suppression, in particular to a direct-drive fan grid-connected inverter high-frequency resonance dynamic suppression method and system, and the method comprises the steps: obtaining a preliminary damping value according to extracted high-frequency resonance natural frequency oscillation distribution data; based on the initial damping value and a power grid voltage fluctuation influence factor, performing dynamic compensation adjustment on the active resonance suppression time delay factor to obtain a high-frequency resonance characteristic value; calculating a frequency drift offset according to the high-frequency resonance characteristic value and a bandwidth upper limit value of a grid-connected inverter controller; and when the frequency drift offset exceeds a preset drift threshold range, optimizing the active damping gain parameter by taking minimization of a resonance peak value and maximization of a dynamic response margin as optimization targets, outputting a high-frequency resonance active suppression parameter optimization set, and completing high-frequency resonance active suppression of the direct-drive fan grid-connected inverter system. By optimizing the active damping gain parameters, high-frequency resonance of the direct-drive fan grid-connected inverter system is effectively suppressed, and stable operation of the system is guaranteed.

The invention relates to a full-link differentiable physical enhancement type automatic driving control method and a full-link differentiable physical enhancement type automatic driving control system. The method comprises the following steps: constructing a full-link differentiable PERL dynamical model to reconstruct real vehicle dynamics in a high-fidelity manner; establishing a micro-preview PID execution layer, calculating a transverse error and a longitudinal speed error based on a dynamic preview distance, and generating a steering wheel angle instruction and an automobile longitudinal acceleration instruction by using a PID control law supporting gradient return; and the PID gain parameter and the preview distance are adaptively adjusted in real time by using the SAC network according to the current vehicle state. According to the full-link micro closed-loop training architecture designed by the invention, end-to-end optimization of control parameters is directly guided by utilizing gradient information of dynamic residual errors, the problem of distortion of a Sim2Real model is effectively solved, the physical interpretability and smoothness of a control strategy are ensured, and meanwhile, the optimal optimization of the control parameters is realized. The trained strategy network significantly improves the path tracking precision and robustness of the vehicle under a complex working condition.

The invention discloses an automatic setting method for control parameters. The method comprises the following steps: acquiring a currently set rigidity level; controlling the motor to move according to the motion instruction; according to the currently set rigidity level, determining an optimal gain parameter which does not exceed the current rigidity level in a preset rigidity gain table; performing automatic inertia identification when the motor moves according to the movement instruction; and adjusting a gain parameter according to the identified inertia parameter to obtain a target rigidity level and a corresponding target gain parameter. The automatic setting of the gain parameter is realized through the automatically identified inertia parameter, and the requirements of the servo driver for debugging rapidity and stability are met; according to the invention, the gain parameter of the servo driver can be automatically and quickly set without manual participation, and the parameter setting efficiency of the servo driver is improved.

A dynamic wireless charging system optimal control method based on accurate output regulation comprises the following steps: constructing an external system capable of generating an interference signal and a reference signal by using an accurate output regulation theory and aiming at the interference and constant reference voltage suffered by an energy receiving end Boost type DC-DC converter in an electric vehicle dynamic wireless charging system; therefore, a system control problem is converted into an output adjustment problem; and designing a state feedback controller based on the constructed external system and the mathematical model of the DC-DC converter, optimizing a state feedback gain parameter by means of a linear quadratic regulator (LQR), and finally obtaining an optimal state feedback controller. According to the method, the influence of large-range energy disturbance caused by movement of the electric vehicle on the system can be effectively restrained, optimal adjustment of system output can be achieved, and the method has practical engineering application prospects.

This invention discloses a distributed adaptive fault-tolerant control method and system for nonlinear inline systems, relating to the field of distributed control technology for nonlinear inline systems. The method includes: for each subsystem in the nonlinear inline system, constructing the actual output of each actuator in the subsystem based on the fault type of the actuator; the nonlinear inline system includes several subsystems; for each subsystem, constructing a local objective function, an error optimizer, an auxiliary system, high-gain parameters, and a control law, and generating an optimal error trajectory based on the local objective function and the error optimizer. At the upper level, this invention designs an error optimizer to generate the optimal error trajectory based on a local objective function that converges to zero; at the lower level, it designs a trajectory tracking fault-tolerant controller with the help of an auxiliary system, employing a dynamic high-gain approach to adaptively handle the effects of uncertain parameters, actuator faults, and unknown strong inline coupling.

The invention provides a data-driven distributed learning control method for a multi-vehicle network system, and the method comprises the steps: firstly, designing different test inputs for each individual vehicle, executing test iteration, and collecting corresponding output data; each individual vehicle determines a gain matrix of a distributed learning control algorithm by using output data collected by the individual vehicle; according to the topology of the multi-vehicle network system and the expected target output receiving condition, each individual vehicle determines an adjacent weight, and a gain parameter is determined based on the adjacent weight; and in an adjustment iteration stage, each individual vehicle establishes a distributed learning control algorithm by using a gain matrix, a gain parameter, collected input data, an adjacent weight and other data, updates control input during next iteration, and controls the multi-vehicle network system to execute the next iteration. According to the invention, all individual vehicles in a multi-vehicle network system with unknown model information can be ensured to realize high-precision real-time tracking of expected target output within the preset operation time.

The present invention belongs to the technical field of ship path tracking, and discloses a ship path tracking method based on adaptive line-of-sight guidance and fuzzy adaptive PID. First, preset path points to obtain real-time coordinates; acquire the actual desired heading angle and the ship's observed heading angle, subtract them to obtain the heading deviation, and calculate the heading deviation rate; input the heading deviation and the heading deviation rate into the fuzzy adaptive PID motion control module, and output gain parameters by the fuzzy control algorithm; input the gain parameters and the PID controller to calculate the ship's rudder angle; update the motion state by inputting the motion mathematical model, form a closed-loop to update the motion state to make the ship approach the preset path points; judge the ship path after updating the state information. The present invention calculates the ship's desired heading in real time through adaptive line-of-sight guidance and adjusts the ship's sideslip angle and ship control parameters, and automatically adjusts the control parameters in real time by means of the fuzzy adaptive PID motion control module to achieve precise motion control, so that the ship sails along the preset planned path.

The embodiment of the invention provides a motor speed observation method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly, obtaining an electrical angle of a motor; then, gain processing is carried out based on the electrical angle, the observer proportional gain parameter and the observer integral gain parameter, and a proportional integral output value is obtained; next, feed-forward processing is carried out based on the electrical angle and a feed-forward gain parameter, a feed-forward output value is obtained, and the feed-forward gain parameter, an observer proportional gain parameter and an observer integral gain parameter are all generated based on the speed ring bandwidth of the speed ring; and finally, performing superposition processing based on the proportional integral output value and the feedforward output value to obtain a speed observation result of the motor, thereby greatly improving the observation real-time performance and accuracy of motor speed observation.

This invention discloses a method for predicting robot motion under time-varying delay conditions. The method determines the number of secondary predictors and their gain parameters based on the maximum time delay, the robot's initial joint position, and the initial joint velocity. The time-varying prediction interval of each secondary predictor is determined by the time-varying delay magnitude. The robot's state from time-delayed motion to the current motion interval is divided into motion sub-states, the same number as the number of secondary predictors. Secondary predictors are constructed, the same number as the number of motion sub-states. The secondary predictors are connected in series, with the time-varying delay robot joint position and velocity signals as input to the first secondary predictor, and the predicted values ​​output by the previous secondary predictor as input to each subsequent secondary predictor. The last secondary predictor outputs the predicted values ​​of the robot's actual joint position and actual joint velocity. This invention offers high prediction accuracy, wide applicability, ease of engineering application and promotion, and contributes to the development of robotics technology.

The invention relates to the technical field of industrial control, in particular to a configuration method for taking a servo drive as an external encoder, which comprises the following steps: taking a first control unit as an external encoder and configuring the external encoder as an absolute value mode, taking a second control unit as a main drive unit and setting the external encoder as a full-closed-loop control mode, and converting absolute position data of a first motor into absolute position data of a second motor; the signal is converted into an incremental pulse signal, and the pulse signal is used as feedback data to be transmitted to a second servo driver for full-closed-loop position correction; recording the pulse number of the second motor and the feedback pulse number of the first control unit, calculating an electronic gear ratio, and writing the electronic gear ratio into a second servo driver; an excitation signal is injected into the second servo driver through the upper computer, response data of the second motor and the load end are recorded, gain parameters are calculated and adjusted based on the dynamic characteristics of the mechanical transmission chain of the load end, and the gain parameters are written into the second servo driver. Servo serves as an encoder, and absolute value full closed loop is achieved.

The invention provides a parameter calibration method, a motor controller, a system, a device, equipment and a medium, and the method comprises the steps: receiving a calibration instruction sent by an upper computer; based on the calibration instruction, an inverter is controlled to send a pulse signal set to a load so that the load can generate winding current, the upper computer, a motor controller and the load are sequentially connected, and the inverter is arranged in the motor controller; collecting a first current peak value set of the winding current; receiving a second current peak value set sent by the upper computer, wherein the second current peak value set is obtained by collecting the winding current by the upper computer; and performing iterative calculation based on the first current peak value set and the second current peak value set to obtain a gain parameter and a bias parameter of a current sensor in the motor controller. Through the method, the current calibration efficiency can be improved.