110 results about "Gain parameter" patented technology

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 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 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.

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 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 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 belongs to the technical field of internal combustion engine electronic control, and discloses a Smith-augmented reduced-order ESO common rail pressure control method based on online parameter identification, which comprises the following steps: step 1, data acquisition and preprocessing, step 2, high-pressure common rail system modeling and discretization to obtain a high-pressure common rail system discretization model, and step 3, high-pressure common rail system control. The method comprises the following steps of: 1, constructing an online parameter identification module based on a recursive least square method with a forgetting factor to solve a system pole parameter, a control gain parameter and a constant bias parameter, 3, calculating a time-delay-free virtual prediction rail pressure by a Smith predictor, 4, observing a total disturbance estimated value and a change rate estimated value of a system by an augmented reduced-order expansion state observer, and 5, calculating a total disturbance estimated value and a change rate estimated value of the system by the augmented reduced-order expansion state observer. And combining with the composite anti-interference control law, and controlling the output control quantity to act on a high-pressure oil pump actuator.

This invention discloses a robust fixed-time encirclement tracking control method for heterogeneous multi-robot systems, applied to the field of cooperative control technology for multi-robot systems. First, a heterogeneous multi-robot system composed of first-order followers and fractional-order leaders is constructed, and a fixed-time encirclement tracking control objective is determined. Second, a robust fixed-time encirclement tracking controller composed of a sign function term and a power exponent term is designed, introducing high-gain parameters to suppress the influence of external disturbances and uncertainties. Simultaneously, adaptive techniques are used to estimate actuator failure factors in real time, thereby improving the robustness of the closed-loop system. Further, a Lyapunov function is constructed, and the stability conditions of the closed-loop error system are analyzed to obtain the upper bound of the settling time and the control parameter conditions. Finally, the designed robust fixed-time encirclement tracking controller is applied to the heterogeneous multi-robot system, demonstrating that all followers can converge to the dynamic convex hull constructed by the leaders within a fixed time.

The application discloses a kind of double three-phase permanent magnet synchronous motor adaptive model-free predictive control methods with optimal virtual voltage vector, S1, the mathematical model of double three-phase permanent magnet synchronous motor is established, and the discrete state space prediction model is constructed;S2, establish hyperlocal model ULM, and design extended state observer ESO;The control gain parameters of hyperlocal model and the observer bandwidth of ESO are updated recursively online, and adaptive model-free predictive control AMFPC is formed;S3, construct the virtual voltage vector set for inhibiting harmonic current;The current of motor future time is predicted using updated AMFPC, and the optimal virtual voltage vector is selected from the virtual voltage vector set;The optimized vector is generated through the center symmetrical pulse width modulation mode to generate switching signal, to drive six-phase voltage source inverter, to control double three-phase permanent magnet synchronous motor.The application provides a promising solution for high-performance double three-phase permanent magnet synchronous motor drive system under complex working conditions.

The invention discloses a subsynchronous oscillation suppression method and suppression system for an offshore wind turbine generator, and relates to the technical field of new energy grid-connected control, and the method comprises the steps: obtaining a stator current signal and an actual rotating speed of the wind turbine generator, and carrying out the preprocessing of the stator current signal; constructing a three-order expansion state observer model based on the preprocessed stator current signal, determining an observer gain parameter through simulation optimization, and extracting a disturbance component; calculating an error signal according to a difference value between the preprocessed stator current signal and a first-order state variable output by a third-order expansion state observer model, generating a dynamic compensation signal based on the error signal and a disturbance component in combination with a nonlinear feedback mechanism, and superposing the dynamic compensation signal to an original current instruction to form a corrected current instruction; according to the amplitude of the disturbance component, the resistance, inductance and capacitance of the virtual impedance are dynamically adjusted according to the disturbance amplitude, and a virtual impedance control signal is obtained; and superposing the corrected current instruction and the virtual impedance control signal to generate a final current instruction.

The invention discloses a state estimation method for a denial of service attack Lipschitz nonlinear system, which comprises the following steps of: aiming at DoS attack possibly occurring in network transmission and gain perturbation during implementation of an estimator, firstly constructing an event triggering communication mechanism, and only sending sampling data when triggering error energy exceeds a set threshold value; then, Bernoulli distribution is adopted to model whether single data transmission is attacked and blocked or not, and a non-fragile state estimator containing additive perturbation is designed; a state estimation error system is defined, a sufficiency matrix inequality which guarantees random stability and has H infinity performance is derived based on the Lyapunov stability theory, then the sufficiency matrix inequality is converted into a linear matrix inequality, an event triggering weight matrix and estimator gain parameters are solved at a time, and joint design of a triggering condition and estimator gain is achieved. Simulation shows that even under the condition that DoS attack and gain perturbation exist at the same time, the method can still accurately and rapidly estimate the system state, and the non-vulnerability of the system and the network resource utilization rate are remarkably improved.

The present application relates to a nonlinear multi-mode adaptive intelligent control method for large angle slewing in the field of radio tracking measurement, comprising the following steps: obtaining the maximum speed, maximum acceleration and speed loop gain parameters of measurement; calculating the antenna large angle slewing error, and using the last position loop regulator output speed to calculate the current position loop regulator output speed U n ; calculating the descending section position loop output speed value U d according to the antenna actual maximum speed, speed loop gain and antenna large angle slewing error; comparing the sizes of U d and U n ; if U n is greater than or equal to U d , then U d is assigned to U n ; if U n is less than U d , then the value of U n remains unchanged; calculating the gain control output U pi in the digital PI controller; comparing the sizes of the descending section position loop output speed value U d and the value of U pi ; if U d is greater than or equal to U pi , then U pi is assigned to U n ; if U d is less than the value of U pi , then the value of U n remains unchanged. The present application avoids excessive acceleration / deceleration, reduces the impact on the antenna transmission mechanism, and improves the service life of the antenna.

The invention provides a doubly-fed fan rotor side converter current loop LADRC parameter setting method and device. The method comprises the steps that control gain parameters of a doubly-fed fan rotor side converter current loop LADRC are acquired; increasing the bandwidth of the linear expansion state observer and the bandwidth of the controller in proportion until the noise causes the output fluctuation or oscillation of the system; finely adjusting the bandwidth of the linear expansion state observer and the bandwidth of the controller; finely adjusting the proportional gain parameter and the second linear parameter until the closed-loop dynamic performance of the system reaches the expected dynamic performance, and recording an undetermined parameter set under the current control gain parameter; after the control gain parameter is changed, the steps are repeated to obtain multiple sets of undetermined parameters; and LADRC parameters meeting the requirements are selected according to a preset standard. The parameter setting method is simple and easy to implement, gives full play to the immunity and damping capacity of LADRC, and is superior to an existing additional damping control strategy.

The invention discloses a ship active mooring control method and system based on variable stiffness gain scheduling, and belongs to the technical field of ocean engineering and ship localization, and the method comprises the steps: collecting and obtaining ship six-degree-of-freedom motion data and mooring rope tension data in real time, and obtaining a system state set; filtering high-frequency wave interference and extracting a low-frequency drift motion component by using a signal preprocessing algorithm, and generating a control deviation signal relative to a preset target position; according to a comparison result of the control deviation signal and a preset dead zone threshold value, generating a real-time rigidity gain parameter through a gain scheduling strategy; according to the real-time rigidity gain parameter and the control deviation signal, a total control force demand is calculated in combination with a virtual damping mechanism; and according to the total control force requirement and the spatial geometric distribution of each mooring rope, target tension increment of the mooring rope is generated to drive the mooring winch to adjust the original length of the mooring rope, and active mooring control is achieved. Fatigue damage of the mooring system is effectively reduced, and low-frequency resonance is restrained.

The application discloses an online parameter self-tuning method and system of a variable-inertia servo system, and relates to the technical field of servo control. The method comprises the following steps: setting a speed loop expected performance index and injecting a test instruction; based on system feedback, performing a steady-state observation and a dynamic estimation algorithm in parallel, and obtaining a high-precision equivalent rotational inertia estimation value in real time through adaptive fusion; according to the estimation value and the set performance index, automatically calculating the proportional and integral gain parameters of the speed loop controller by using a preset mapping relationship; and finally, updating the parameters to the controller in real time to realize the self-adaptation to the load inertia change. The application considers the steady-state precision and dynamic rapidity of identification through composite inertia identification, and makes the tuning process intuitive and the system dynamic performance consistent through performance index mapping, thereby effectively solving the oscillation problem of the servo system under the variable-inertia working condition in a wide range.

The invention relates to the technical field of numerical control machine tool precision control, in particular to a micro-nano structure ultra-precision cutting intelligent compensation method and system based on an artificial intelligence chip and an FPGA, and the scheme comprises the steps: obtaining multi-physical field data including vibration, temperature and wear in parallel through a collection module driven by the FPGA; transmitting the data to an artificial intelligence chip through a UDP protocol to execute error prediction and generate a dynamic compensation instruction, and synchronously extracting a vibration spectrum characteristic component by an FPGA through an FFT butterfly algorithm; carrying out feed-forward adjustment and calculating a servo gain parameter by fusing the enhanced HRV filter; and a reverse compensation waveform is generated through the DDS module and is injected into the servo control ring through the DAC to counteract the machining error. According to the technical scheme, real-time sensing, error prediction and instruction injection of multi-source physical signals in the micro / nano-scale cutting process can be achieved, and the track precision of numerical control machining is improved.

This application belongs to the field of DC bus voltage control, specifically disclosing a super-spiral sliding mode active disturbance rejection control method based on an RBF neural network, including the following steps: establishing a mathematical model of a bidirectional Buck / Boost circuit; designing an active disturbance rejection controller, which includes a tracking differentiator and an extended state observer; using an RBF neural network to dynamically optimize the gain parameters of the extended state observer in real time; constructing a super-spiral integral sliding mode controller to replace the nonlinear state error feedback loop in the active disturbance rejection controller; calculating a current reference signal based on the output signal of the tracking differentiator and the observation signal of the extended state observer through the super-spiral integral sliding mode controller; generating a drive signal based on the current reference signal to control the bidirectional Buck / Boost circuit to maintain the stability of the DC bus voltage. This application can effectively improve the stability and disturbance rejection capability of the DC microgrid bus voltage.

The embodiment of the invention provides an open earphone active noise reduction method and device, electronic equipment and a storage medium, and belongs to the technical field of earphone noise reduction. The method comprises the steps that a microphone collects an environment sound signal; the input compressor monitors the environment sound signal; in response to the change of the environment sound signal, the input compressor performs noise dynamic analysis on the environment sound signal to obtain a primary gain parameter; the first gain module performs first gain processing on the environment sound signal based on the primary gain parameter to obtain a primary noise reduction audio; the noise reduction filter carries out filtering processing on the primary noise reduction audio to obtain advanced noise reduction audio; a second gain module performs second gain processing on the advanced noise reduction audio to obtain a target output audio; and the sound output module outputs the target output audio. According to the embodiment of the invention, active noise reduction can be carried out on burst noise on the earphone.

The invention discloses an online parameter self-tuning method and system for a variable inertia servo system, and relates to the technical field of servo control, and the method comprises the following steps: setting a speed ring expected performance index, and injecting a test instruction; steady-state observation and dynamic estimation algorithms are executed in parallel based on system feedback, and a high-precision equivalent rotational inertia estimated value is obtained in real time through adaptive fusion; according to the estimated value and a set performance index, automatically calculating proportion and integral gain parameters of a speed loop controller by using a preset mapping relation; and finally, the parameters are updated to a controller in real time, so that self-adaption to load inertia change is realized. According to the method, the steady-state precision and the dynamic rapidity of identification are both considered through composite inertia identification, the setting process is visual through performance index mapping, the dynamic performance of the system can be kept consistent, and the oscillation problem of the servo system under the wide-range variable inertia working condition is effectively solved.

The invention provides a head motion tracking method and device, computer equipment and a storage medium. The method comprises the steps that reference prediction state data are determined according to user head tracking data collected by at least one sensor at the current moment, and the reference prediction state data comprise reference prediction speed data; determining a target gain parameter according to the reference prediction speed data; according to the target gain parameter and reference prediction state data of at least one previous moment, performing dynamic smoothing processing on the reference prediction state data at the current moment to obtain target prediction state data; and generating a prediction picture according to the target prediction state data, and presenting the prediction picture to a user. According to the scheme, the more stable picture can be generated, and the stable virtual object is presented to the user, so that the user can interact with the virtual object, and the effect of improving the use experience of the user is achieved.

The invention belongs to the technical field of flexible mechanical arms, and provides a flexible mechanical arm rigidity control method based on feedback control parameter dynamic adjustment, which comprises the following steps: establishing a rigidity adjustment model representing a mapping relation between feedback controller parameters and joint rigidity according to joint dynamic characteristic data of a flexible mechanical arm; task characteristic parameters of the flexible mechanical arm are obtained, and a current task scene and target joint rigidity corresponding to the current task scene are determined based on the task characteristic parameters; determining an optimal parameter combination of a feedback controller corresponding to the target joint stiffness according to the stiffness adjustment model by taking the target joint stiffness and the optimal system stability as an optimization target; and according to the optimal parameter combination, gain parameters of the feedback controller are dynamically adjusted so as to control the joint rigidity of the flexible mechanical arm. According to the scheme, the adaptability and operation flexibility of the flexible mechanical arm in a task scene are improved, and the control precision and stability of the joint rigidity are effectively guaranteed.

The invention provides a temperature control method of a temperature controller and the temperature controller, and relates to the technical field of temperature control, and the method comprises the steps: collecting multi-source state parameters of the temperature controller, and constructing a comprehensive state matrix, the multi-source state parameters comprise historical control output, set water temperature, actual water temperature, human skin temperature, equipment operation temperature, heater working state and cooler working state; strategy scores are obtained according to the comprehensive state matrix, and the strategy scores comprise a gain feature strategy score, a temperature trend strategy score, a system state evaluation score and a security constraint strategy score; and adjusting the gain parameter of the PID controller according to the strategy score, and obtaining the control output of the current period for improving the temperature control precision of the temperature controller.

The invention discloses a gain optimization method for a vibration observer of a permanent magnet wheel hub motor, belongs to the technical field of vibration soft measurement, and aims to solve the problem that gain parameters are difficult to adjust when the vibration state of the wheel hub motor is observed. Comprising the following steps: establishing a permanent magnet hub motor structure dynamic model; establishing a vibration fast-slow varying double-time-scale observer by adopting a regular perturbation theory dimension reduction method; a simulated annealing grey wolf fusion algorithm is designed, a motor vibration observer gain parameter is autonomously optimized, a black wolf variation strategy and a simulated annealing optimal solution selection mechanism are introduced, and a black wolf individual optimization transformation architecture is constructed to solve the problems that a grey wolf optimization algorithm is low in convergence speed and falls into a local optimal solution; and the effectiveness of the simulated annealing grey wolf fusion algorithm and the superiority of the improved double-time-scale observer are verified, so that the accuracy of the vibration response observation of the hub motor is effectively improved.

The application relates to the technical field of engine control, in particular to a controller optimization processing method of an engine servo system. The method comprises the following steps: acquiring environmental data during engine operation in the past and monitoring data of a to-be-analyzed type, initial clustering and an initial clustering center; acquiring a monitoring data segment of the monitoring data of the to-be-analyzed type in an initial cluster, and then calculating the average influence degree of each type of environmental data on each to-be-analyzed type; then, the influence degree of each to-be-analyzed type on the initial clustering center at each moment is obtained, the initial clustering center is updated, and a final cluster is obtained; the average adjustment effect of each final cluster is obtained according to the adjustment effect after each instruction is sent; and the integral gain parameter of a PID algorithm is adjusted according to the average adjustment effect of the final cluster to which the current moment belongs. The application can improve the control stability and accuracy of the engine servo system controller.