116results about How to "Few adjustment parameters" patented technology

The invention discloses a system and method for controlling the current of a permanent magnet synchronous motor for an electric vehicle. The system comprises a rotating speed / position detection module used for detecting the rotating speed value omega and the position angle theta of the permanent magnet synchronous motor, a current sensor, a first coordinate transformation module, a second coordinate transformation module, a PI speed ring controller used for conducting PI operation on the motor rotating speed value omega obtained through the rotating speed / position detection module and a given motor rotating speed value omega r to obtain a q-axis current reference value, a current ring prediction control module, a sliding formwork disturbance observation module, a third coordinate transformation module and a space vector pulse width duration modulation module, wherein the space vector pulse width duration modulation module is used for calculating u alpha and u beta to obtain six-path PWM signal output, PWM signals are used for controlling an inverter, and thus three-phase output voltage is obtained to drive the motor to operate. An advanced continuous time generalized prediction control method is adopted for the tracking and controlling of the current of the permanent magnet synchronous motor for the electric vehicle, and the system and method have the advantages of being small in calculated amount, good in control effect and the like.

The invention discloses an addressing and capacity configuration method used for a UPFC. A UPFC addressing and capacity configuration model with transient stability constraints is established, a difference evolution algorithm serves as a framework, and the installation position and the capacity of the UPFC are optimized; in addition, a centrality correction interior point method is adopted for carrying out optimization and fitness assessment on continuous variables of each difference evolution unit. The difference evolution algorithm is simple in operation, high in searching capability, fewer in adjusting parameters and suitable for mixed optimization, and the interior point method is good in convergence and strong in robustness, so that the provided mixed algorithm can well solve the problems of UPFC addressing and capacity configuration, the method is highly efficient and convenient, and the application prospect is good.

The invention discloses a power transformer fault diagnosis method based on an improved firefly algorithm (PFA) optimized probabilistic neural network (PNN). The power transformer fault diagnosis method comprises the following steps: firstly, collecting fault characteristic gas by using a gas chromatographic analysis method and carrying out pretreatment by using a fused DGA algorithm; initializinga PNN neural network, a firefly algorithm and a two-dimensional particle swarm; taking the PNN smoothing factor as a firefly individual, and calculating the position and brightness of the firefly; feeding the solving result of each firefly algorithm back to the particle swarm algorithm, carrying out fitness evaluation on each particle, and updating the positions and speeds of the particles; carrying out loop iteration, substituting the obtained optimal smoothing factor into the PNN to carry out fault prediction, and training a PNN model after PFA optimization; inputting a test sample, and outputting a fault type result, thereby achieving the fault diagnosis of the power transformer. The method is high in search speed, high in diagnosis precision, small in error, and obvious in classification effect.

The invention discloses a design method for path tracking guidance and control structures of constrained unmanned ships. The method includes the following steps: designing a disturbance observer; designing a LOS guidance law module; designing a heading control module; and designing a speed control module. Estimation can be performed on lumped uncertainty consisting of constrained unmanned ship dynamics modeling uncertainty and external disturbance brought by marine environments by adopting the disturbance observer, so that the method is less in required adjustment parameter and easy to adjustparameters; the calculating load of a guidance and control structure can be effectively reduced by sending the estimated value of the lumped uncertainty to the heading control module and the speed control module to control a ship to move, so that rapid convergence can be realized, the anti-interference capabilities of the guidance and control structure can be enhanced, and good control performancecan be achieved; and through an online scrolling optimization and feedback correction strategy, the guidance and control structure of a constrained unmanned ship can fully utilize allowed control motion, so that performance can be enhanced.

The invention discloses a diesel engine DPF active regeneration control method. The diesel engine DPF active regeneration control method comprises the following steps that the exhaust mass flow of anengine, the DOC inlet temperature and the air pressure difference between a DPF outlet and a DPF inlet are acquired, and whether the regeneration triggering condition at the moment is met or not is judged; when the regeneration triggering condition is met, the regeneration control is triggered, the initial oil injection quantity in the current state is calculated; an oil injection device is controlled to start to perform oil injection, and meanwhile, whether the DPF inlet temperature meets the set regeneration target temperature or not is detected; when the DPF inlet temperature meets the setregeneration target temperature, oil injection is carried out at the current oil injection quantity; when the DPF inlet temperature does not meet the set regeneration target temperature, the fuel oilinjection amount is regulated through a regeneration controller, so that a proper oil injection quantity is guaranteed to be regenerated, and meanwhile, whether the air pressure difference before andafter a DPF is reduced to a set lower limit value or not is detected; and when the regeneration end condition is met, oil injection is stopped, and the regeneration process is completed. According tothe diesel engine DPF active regeneration control method, parameters needing to be calibrated are few, the universality is high, the calculation fluctuation under a feed-forward oil quantity transientcondition is reduced, and the regeneration efficiency is improved.

The invention discloses an iteration text clustering method based on self-adaptation subspace study. The method includes the following steps: (1) initiation: text linguistic data is expressed as a text vector space, initial K clusters are generated through an affine propagation clustering method, and all text clustering categories are expressed as an initial category affiliation indication matrix; and (2) iteration between the subspace projection and the clusters: the initial category affiliation indication matrix is used as prior knowledge, a maximum average neighborhood edge is used as a target to solve a subspace projection matrix, the text vector space is projected to a subspace, K clusters are generated through the affine propagation clustering method in the subspace, and a category affiliation indication matrix is updated; and a convergent function is calculated based on the subspace projection matrix and the category affiliation indication matrix till the function is converged, iteration exits, and text clustering is finished. The iteration text clustering method does not limit the capacity and distribution of text data, subspace solution and clusters are fused under a uniform frame, and an overall optimal clustering result is obtained through an iteration strategy.

The invention relates to the technical field of battery state-of-charge estimation of an electric vehicle power battery system, in particular to an algorithm based on SOC and SOH joint estimation of apower battery. The method optimizes parameters of an RF algorithm through a TSBSO algorithm so as to optimize an algorithm pruning threshold, the number of pre-test samples and the number of decisiontrees, and the optimized algorithm can quickly find a globally optimal solution and improve the algorithm efficiency. The SOH of the power battery is estimated through the RBM, the RBM is optimized through the WOA, and model parameters are prevented from falling into local optimum and thus the purposes of correcting the maximum available capacity of the battery and improving the SOC estimation precision of the power battery under the full-time working condition are achieved; the SOC of the power battery is estimated through the combination of a TSBSO-RF H-infinity calculation method and filtering, the advantages of the two algorithms are exerted by adopting a linear fusion algorithm, the defects of the two algorithms are avoided and the SOC estimation precision of the power battery is higher.

A TBM vibration damping control method based on a particle swarm optimization algorithm PSO comprises the following steps: 1, building a TBM concentration parameter dynamics model; 2, modeling each rigidity node in the TBM concentration parameter dynamics model; 3, taking the minimum TBM vibration intensity and the minimum TBM driving specific energy as targets, and building a multi-target optimization vibration damping control model; 4, using the PSO algorithm to solve the multi-target optimization vibration damping control model, comparing the real time measured vibration parameter with a theory vibration parameter calculated by the concentration parameter dynamics model, further adjusting a control parameter, related to the vibration parameter, in the multi-target optimization vibrationdamping control model; using the optimization iteration algorithm to obtain the optimal rigidity parameter and driving parameter, further adjusting the control parameter in the TBM actual operation process according to the optimal rigidity parameter and driving parameter, thus realizing optimization control of TBM vibration, and realizing the high efficiency driving target of the TBM under the low vibration level conditions.

The invention provides a stochastic resonance method based on multi-scale noise adjustment. Relations among a nonlinear system, signals and noise are balanced only by adjusting multi-scale noise modes of the signals according to weak signal detection under a strong noise background, so that a stochastic resonance effect under the fixed nonlinear system and the fixed input noise is realized. By the method, the multi-scale noise modes of the signals to be processed are adjusted, so that the strength of the signals at the scale of signal characteristic frequency is the highest and then gradually reduced along with increase of the scale frequency, the nonlinear system, the signals and the noise are optimally matched by adjusting optimal parameters, so that the stochastic resonance effect under the fixed nonlinear system and the fixed input noise is realized. The method at least has the advantages that adjusting parameters are fewer, the method is insensitive to the noise strength of the signals, high frequency can be directly detected, higher signal-to-noise ratio can be obtained and the like.

The invention discloses an active power filter (APF) control method based on fuzzy neural network PID. The method comprises the steps that a non-linear dynamic model of an APF inverter is established according to power electronics; a PIC controller of a current control loop is designed according to the established non-linear dynamic model; a fuzzy neural network compensating controller is designed and used for improving the dynamic response of the current control loop of the inverter; a PI controller of a voltage control loop of the inverter is designed, and a reference current value which needs to be tracked by the output current of the inverter is solved. According to the method, PID control and fuzzy neural network control are combined, the design process is simple, engineering realization is easy, and the response performance of the output current of the inverter is further improved.

The invention discloses a passive time difference positioning system optimization station distribution method based on an improved grey wolf optimization algorithm. The method comprises: converting the station distribution problem of the passive time difference positioning system into a minimum value problem for solving the objective function; establishing an optimized station distribution model on the basis of a passive time difference positioning system; and solving the model by using an improved grey wolf optimization algorithm, and the basic flow of the solution is as follows: establishingan optimal station distribution model of the passive time difference positioning system, and solving the model by using an M-GWO algorithm, so that the base station position when the target functionvalue of the model is minimum is the optimal station distribution position of the positioning system. The method is used for solving the higher-precision station arrangement position of the passive time difference positioning system for the target area, the optimal station arrangement position in any station arrangement range can be solved, and universality is achieved.

The invention discloses an APF control method based on active disturbance rejection and repetitive control. The APF control method based on active disturbance rejection and repetitive control includes the following steps that 1, a non-linear dynamic model of an APF inverter is built according to power electronics; 2, an APF inverter controller is designed according to the non-linear dynamic model built in the step 1 and an active disturbance rejection and repetitive control combined method; 3, the dynamic response performance of a current control loop of the inverter is improved through an active disturbance rejection controller; 4, the steady response performance of the current control loop of the inverter is improved through a repetitive controller; 5, a voltage loop controller of the inverter is designed through a PI algorithm, and a reference current value needing to be tracked by the output current of the inverter is figured out. The active disturbance rejection and repetitive control combined method has the advantages that the design process is simple and engineering realization is easy, and the dynamic response performance and the steady response performance of the output tracking current of the inverter are further improved.

The invention discloses an intelligent granary environment safety monitoring system based on a field bus. The system is composed of a granary environment parameter collection platform based on a CAN field bus, and a granary environment safety evaluation subsystem. The system realizes the intelligent detection of granary environment parameters and the intelligent evaluation of granary environment safety. Many problems still existing in the granary environment caused by the unreasonably designed and poor traditional granary environment multi-parameter detection equipment, the incomplete detection system and the like are solved. Based on the nonlinearity and large lag of granary environment parameter changes and the large area and complex structure of the granary environment, the defects of inaccuracy, low reliability and the like of a granary environment monitoring system are overcome, accurate detection and reliable classification of granary environment parameters are realized, and therefore, the accuracy and robustness of granary environment parameter detection are greatly improved.

The invention discloses a greenhouse environment multi-parameter intelligent monitoring system based on the Internet of Things. The system is composed of a watermelon greenhouse environment parameteracquisition platform based on a ZigBee network and a watermelon greenhouse environment microclimate factor evaluation subsystem. The system realizes intelligent detection of watermelon greenhouse environment temperature and evaluation of microclimate environment factors. The problems that an existing watermelon greenhouse environment monitoring system does not accurately detect watermelon greenhouse environment parameters and evaluate environment factors according to the characteristics of nonlinearity, large lag, complex greenhouse environment parameter changes and the like of greenhouse environment parameter changes, and therefore the accuracy of predicting and evaluating the watermelon greenhouse parameters is improved are effectively solved.

The invention discloses an air conditioner for an automobile, which is packed by an air conditioner housing, wherein the internal of the air conditioner housing is formed of an air current channel, and a centrifugal fan, an evaporator core body 3, a hybrid air door 5, an air mixer 7, a heater core body 6, an air outlet 20 and the like are arranged on the direction of air channel, the air mixer which is capable of changing the direction of cold / hot air current is arranged between the hybrid air door and the air outlet. Through the technical scheme of the invention, two air currents of different temperature are mixed fully, so that the temperature of the air current at the air outlet achieves high uniform, thereby improving the accuracy of temperature control and comfort of use. Being arranged for use, the air mixer has the advantages of small resistance, evident mixing effect, less adjusting parameters, and convenient modification, and can be widely used for air conditioners and other equipment required intensified heat exchange.

The invention discloses a fan gearbox performance detection and health evaluation method based on temperature parameters. According to the method, fan state detection data are analyzed, the operationcharacteristics of a wind turbine generator unit are combined, state detection and fault prediction are carried out on the temperature of a gearbox and the temperature of a gearbox bearing by adoptinga model established by combining KECA and GRNN, an evaluation decision is performed on the running state of the fan gearbox through ideas of threshold discrimination and statistical process control according to predicted residual error, and a gearbox health state result is obtained by adopting comprehensive fuzzy discrimination. When the method is applied to an actual wind turbine generator unitcase with small sample data, the health state of the wind turbine generator unit can be accurately predicted and evaluated. The method is applied to an actual wind turbine generator unit with small sample data, and the health state of the wind turbine generator unit can be accurately predicted and evaluated.

The invention discloses an intelligent tomato greenhouse temperature early-warning system based on a minimum vector machine. The early-warning system is characterized by being composed of a tomato greenhouse environmental parameter acquisition and intelligent prediction platform based on a CAN field bus and an intelligent tomato greenhouse temperature early-warning system. By means of the intelligent tomato greenhouse temperature early-warning system based on the minimum vector machine in the invention, many problems still in the environment in a closed tomato greenhouse due to the reasons ofunreasonable design, backward equipment, incomplete control system and the like in the traditional tomato greenhouse environment can be effectively solved; and furthermore, the control problem that the tomato greenhouse environment temperature is greatly influenced due to the fact that the existing tomato greenhouse environment monitoring system does not monitor and predict the temperature in thetomato greenhouse environment according to the characteristics of nonlinearity and large lag of tomato greenhouse environmental temperature change, large tomato greenhouse area, complex temperature change and the like can be effectively solved.

The invention provides a method of inhibiting a PAPR (Peak-to-Average Power Ratio) in an OFDM (Orthogonal Frequency Division Multiplexing) system based on a BA (Bat Algorithm). The method comprises the steps of: first, in an OFDM system, the information source binary system bit stream of a transmitting terminal forming a plurality of paths of low speed data stream Dm through mapping and serial-parallel conversion, and the data stream Dm modulating signals to subcarriers through IFFT (Inverse Fast Fourier Transform) to obtain a signal IFFT (Dm); second, employing a BA to optimize the initial phase of the subcarriers generated in the first step; and third, employing the optimal phase factor theta m generated in the second step to optimize the signal IFFT (Dm) generated in the first step to obtain an optimized signal IFFT (Dm)*e<jo>m, performing parallel-serial conversion, adding cyclic prefix, and digital to analog conversion on the optimized signal to generate an OFDM signal, and sending the OFDM signal to a channel, thereby realizing high PAPR inhibition in the OFDM system.

The invention discloses a wireless sensor network node positioning method of an ecological niche grey wolf optimization DV-Hop algorithm. The method comprises: a beacon node broadcasting a beacon to anetwork, the beacon comprising position information and hop count information of the beacon node, and the beacon being spread out in a flooding mode in the network; after obtaining the position information and the hop count, each node estimating the average distance of each hop between the nodes, and then estimating the distance between the beacon nodes; the beacon nodes refining the average distance per hop by using a ecological niche grey wolf optimization algorithm, and changing the fitness value of the beacon nodes by comparing the distances between the beacon nodes with the ecological niche radius; calculating the position of the unknown node through the beacon node, recording the coordinate of the unknown node, and converting the calculated position information of the unknown sensorbeacon node into the position information of the known sensor beacon node. The method has the advantages that the global search capability can be improved, the positioning error is reduced, and the positioning precision is improved.

The invention discloses an air route planning method based on guide speed field in threat environment, and belongs to the field of air route planning. Subsistent threat affecting is fully considered, the threat is equivalent to a circular obstacle in a two-dimensional plane through the calculation, then traction field and avoidance field environment is established, the guide speed field is specially designed, controllability of the algorithm is improved, and the fast advantage of artificial potential field method is maintained. According to the method, planning speed can be directly obtained by calculation, the problem of incapability of reaching or entry barriers of the artificial potential field method can be overcome, and a smooth two-dimensional obstacle avoidance air route can be planned in real time. The air route planning method has the advantages of simple terrain modeling and small amount of calculation, and is easy to realize.

The invention discloses a community self-organizing detection method for power network fault diagnosis. The method comprises the steps of firstly, collecting network characteristic parameters of power networks, then describing the power networks as weighted network models, constructing local fitness and global fitness functions, starting from grouped solutions of the power networks, which are generated randomly, calculating local fitness of each power node, sequencing the local fitness, selecting the nodes with the poor local fitness according to an expansion evolution probability distribution function, transferring the nodes with the poor local fitness to another group of networks to generate new solutions, comparing global fitness values of the new solutions and the current solutions, reserving the best solutions in the new solutions and the current solutions, enabling the new solutions to serve as initial solutions for the next iteration to repeat above optimization processes until preset end conditions are met, and finally, analyzing and outputting community self-organizing detection results which are used for power network fault diagnosis. Compared with conventional methods, the method has the advantages of being a few in adjusting parameter, simple in detection process, easy to implement and high in detection efficiency and detection precision.

The invention discloses a local shadow photovoltaic array MPPT control method based on an optimization algorithm of the molecular kinetic theory. Output current of a photovoltaic array serves as particles of the algorithm, the power of the photovoltaic array serves as an algorithm fitness function, and through the novel and rapid overall search technology, namely the optimization algorithm of the molecular kinetic theory, the maximum power point of the overall photovoltaic array is efficiently and accurately found out, the voltage of the maximum power point is output through the algorithm, the difference value between the voltage of the maximum power point and the actual output voltage of the photovoltaic array is obtained, a Boost circuit is controlled through a PWM module, and tracking of the maximum power point is achieved. The problem that under the local shadow condition, the photovoltaic array MPPT control having multi-peak output characteristics is easy to fall into interference of local maximum power points, the overall maximum power point can be accurately found out, tracking speed is high, and steady-state oscillation is small.

The invention discloses a disturbance suppression and high-precision tracking control method for a brushless direct current motor servo system. The method comprises the steps of S1 establishing a disturbance-containing brushless direct-current motor servo system state space model according to a voltage balance equation and a torque balance equation; S2 designing a reduced-order extended state observer according to the state space model of the brushless direct current motor servo system, and realizing real-time estimation of non-matching total disturbance f (t, xp, Mc) and armature current i (t); S3 designing an improved repetitive controller according to the periodic characteristics of the reference input, and constructing a periodic reference input signal generator; and S4 constructing acomposite controller based on a state feedback controller, a disturbance compensator, a repetitive controller and a feedforward compensator, and meanwhile, realizing effective suppression of the servosystem on non-matching disturbance and accurate tracking on periodic reference input. The method has the advantages of simple control implementation, high tracking precision, good robustness and thelike.

The invention relates to a method for restraining the torsional vibration of a rotor of a motor closed-loop speed control system. The method comprises the following steps: (1) collecting a revolving speed signal of the motor rotor through a signal collecting unit, and enabling the revolving speed signal to pass through a band pass filter unit to extract a torsional vibration component signal of the rotor; (2) enabling the torsional vibration component signal of the rotor to pass through a lead-lag unit to regulate the phase position of the torsional vibration signal, and obtaining an electromagnetic torque component signal being phase-opposite with the torsional vibration component signal of the rotor; and (3) regulating the gain amplification times of a gain amplification unit, adding the electromagnetic torque component signal into a revolving speed feedback point through an amplitude limiting unit to generate an electromagnetic torque component being phase-opposite with the torsional vibration signal of the rotor, and therefore effectively restraining the torsional vibration of the motor rotor and power vibration. According to the method, the divergent torsional vibration of the rotor can be restrained, meanwhile the power vibration due to the torsional vibration of the rotor can also be restrained, and the signal collection is simple and convenient. The method has the advantages of simple structure, lower cost, less regulation parameters, easiness in realization and the like and has a larger promotional value.

The invention discloses a DFIG back-stepping-control-based stator harmonic wave current suppressing method in a distorted power grid condition. Based on the Lyapunov stability theory, the method performs a back-stepping control algorithm design in a unified mathematical model of DFIG ideal and harmonic wave power grid voltages under a static two-phase coordinate system to achieve active and reactive decoupling control. Defects that conventional vector control dynamic performance is not good enough; complex coordinate transformation is required; strict field orientation projects are difficult to realize; the stable state performance of direct power control through a conventional table lookup method is not good enough are overcome. By means of the method, excellent dynamic and static performance can be achieved in an ideal power grid condition.

The invention relates to a network platform data resource value evaluation method. The method comprises the following steps: 1, constructing a data resource value evaluation index system in a networkplatform transaction environment; 2, determining an evaluation index weight based on an entropy correction G1 method; 3, pre-screening the transaction data resources of the platform based on a grey correlation analysis method; screening out transacted data resources of which the association degree with the pre-evaluated data resources is greater than or equal to a threshold value to form a model sample set T; 4, selecting a random forest model as a basic model of network platform data resource value evaluation, and constructing a data resource value evaluation model by utilizing the sample setT; and inputting each evaluation index of the pre-evaluated data resource into the data resource value evaluation model, and taking the average value of the output value of each regression tree as the data resource value evaluation result of the data resource value evaluation model. According to the method, the accuracy of data resource value prediction can be remarkably improved, the calculationamount of the RFR model can be reduced, and the training efficiency of the RFR model is improved.

The invention relates to the technical field of risk control of the Internet financial industry, in particular to a CS-PNN-based customer credit risk assessment method and system. Compared with BP andRBF neural networks, the PNN fuses a Bayesian decision theory and density function estimation on the basis of a radial basis function, the method has the advantages of simple network structure, few adjustment parameters, short operation time, no local minimum point and the like; compared with GA, PSO, ACO and other optimization algorithms, the CS algorithm searches for a global optimal solution by simulating the combination of the parasitic propagation behavior of the valley bird nest and the Levy flight search principle, has the advantages of being few in parameter setting, high in convergence speed, high in universality and robustness, easy to implement and the like, and can efficiently balance local search and global search of the algorithm; the CSPNN model obtained by optimizing the smoothing factor of the PNN by the CS has the advantages of simple network structure, high convergence rate, good fault tolerance, high robustness, high classification accuracy, strong sample appendingcapability and the like, and can meet the requirement of real-time credit risk assessment of a loan system.

The invention relates to a method for detecting an unknown modulation mode of radar signals based on a generative adversarial network, and belongs to the field of electronic recognition. The method comprises the following steps of collecting all radar signals sequentially from a radar signal database; obtaining time-frequency images of radar signals through Cui-Williams distribution and short-timeFourier transform by using time-frequency transform; performing parameters training on the generative adversarial network with the time-frequency images, combined with deep learning, and preserving discriminator parameters; constructing discriminators based on the discriminator parameter, and discriminating the radar signals by combining the respective discriminators; optimizing the weight parameters of each discriminator by using the improved crow search algorithm and combined with the idea of the group intelligent optimization algorithm to obtain the best result of discrimination; finally retaining the parameters of the discriminators and the weight parameters, and detecting the received radar signals. The method provided by the invention combines time-frequency transform, deep learningand the improved crow search algorithm to realize stable, fast and high-precision detection of radar radiation source signals with unknown modulation mode, and has broad application prospects.

The invention discloses an intelligent monitoring system for multiple oil and gas concentration sensors based on Internet of things. The system consists of a gas station tank area environmental parameter acquisition platform based on a ZigBee network, and a gas station tank area environmental oil and gas concentration sensor monitoring subsystem. Through the adoption of the intelligent monitoringsystem, the problems that the gas station tank area environmental oil and gas concentration is not detected accurately and that an early warning is not given according to the characteristics of non-linearity, large lag and complex change of the gas station tank area environmental oil and gas concentration change in an existing gas station tank area environmental monitoring system is effectively solved, and the gas station oil and gas concentration prediction accuracy and robustness are improved.

The invention discloses a design method for a marine robot trajectory tracking control structure. The structure comprises a rolling time domain optimization control module, a dynamics estimation module, a kinematics estimation module, a vertical transformation control module, a swaying direction conversion control module and a marine robot. According to the invention, on-line rolling optimization and feedback correction are carried out on the dynamics of the marine robot through the rolling time domain optimization control module, which prevents the dynamics of the marine robot from exceeding the constraint range; the control performance of the entire trajectory tracking structure is improved; tracking errors are reduced; the output dynamics of the marine robot is efficiently controlled; the estimation modules are used to estimate the lumped uncertainties composed of the uncertainty of marine robot dynamics modeling and external disturbances caused by the environment, and then the estimated values are used as compensation for offsetting to eliminate disturbances; the calculation load of the control structure is effectively reduced; the anti-interference ability of the control structure is improved; and the control performance is good.