57 results about "Chaotic particle swarm optimization" patented technology

The invention relates to a chaotic particle swarm optimization-based orthogonal frequency division multiplexing (OFDM) system resource allocation algorithm and belongs to the technical field of system resource allocation. The invention provides a utility function maximized framework-based resource allocation algorithm aiming at the power allocation problem in a wireless multi-user orthogonal frequency division multiplexing (OFDM) system. In an actual network environment, the optimized algorithm is non-convex and a classical optimization method is difficult to solve the problem. Therefore, a particle swarm method in intelligent optimization is applied to non-convex optimized algorithm design. Logistics chaotic searching is embedded into a PSO algorithm aiming at the problem that particle swarm optimization easily has a local extremum point, and the chaotic particle swarm algorithm is provided. Compared with like algorithms, the novel algorithm effectively solves the non-convex problem, can make the system have better performance, and can effectively prevent the particle swarm algorithm from having a local solution.

The invention provides a method for forecasting a hybrid neural network and recognizing scenic spot meteorological elements. The method includes the steps of firstly, collecting and conducting normalization processing on data banks of meteorological stations; secondly, determining the number of RBF network hidden nodes established by the main meteorological elements of the meteorological stations through a subtractive clustering algorithm according to the data banks of the n meteorological stations; thirdly, obtaining RBF network model parameters of the m meteorological elements established by the n meteorological stations respectively through chaotic particle swarm optimization algorithm; fourthly, forecasting future meteorological element values of an assigned number of days of the n meteorological stations through optimum RBF network prediction models of the elements obtained by the n meteorological stations; fifthly, conducting autoregression adjustment on soft factor information of a certain scenic spot according to the n meteorological elements and forecasting the meteorological element values of the scenic spot; sixthly, establishing an ART2 network to recognize and record weather phenomena of the scenic spot. The method has the advantages that the hybrid neural network prediction models have good generalization performance, are high in accuracy for forecasting the weather in the scenic spot and have application value.

The invention relates to a multi-object optimized space manipulator trajectory planning method. Aiming at trembling produced in the moving process of a space manipulator and disturbance to a platform,optimal path planning is conducted; the method adopts a multi-object chaotic particle swarm optimization algorithm to optimize joint trajectory parameters in the manipulator moving process, so that reaction of movement of the manipulator to the platform is reduced, the peak value of control moment is reduced, and the disturbance of the platform and the trembling of the manipulator are reduced. The planned manipulator trajectory is smooth and continuous, the multi-object optimized space manipulator trajectory planning method can be applied to a multi-degree-of-freedom space manipulator. The multi-object optimized space manipulator trajectory planning method has the characteristics of high universality, good robustness, high convergence speed and the like.

The invention discloses a particle swarm improved algorithm based on chaotic backward learning. The algorithm mainly comprises a backward learning strategy and chaotic particle swarm optimization. The basic principle of the backward learning strategy includes: the backward learning strategy generates a corresponding backward solution for each initial candidate solution, and selects the solutions with short distance (high fitness) from two kinds of solutions (candidate solutions and corresponding backward solutions) as members of an initial population so that the convergence rate in an optimization process can be increased. In order to maintain the diversity of the population and enable individuals of the initial population to be uniformly distributed as much as possible, the initial population is generated by employing the backward learning strategy.

The invention discloses a robust fault-tolerant control method for small unmanned aerial vehicle flight control system. According to the discrete system with parametric uncertainties and time-varying delays in the case of actuator failure, a simulated-integral sliding mode prediction model is constructed. The model can ensure the global robustness of an entire dynamic process and deal with the influence of the fault incurred from the parametric uncertainties and time-varying delays on the progressive stability of the sliding mode. Through the use of the improved chaotic particle swarm optimization (PSO) algorithm to improve the rolling optimization process, the method can effectively avoid the problems that the traditional particle swarm algorithm is easy to fall into the local extreme point in the excellence seeking process and that the convergence rate is slow. In the invention, a new reference trajectory is proposed, which can reduce the influence of system uncertainty and time-varying delay to an acceptable range through compensation, and can also suppress the buffeting phenomenon of the sliding mode obviously. The invention is used for the robust fault-tolerant control for a discrete system with parametric uncertainties and time-varying delays in the case of actuator failure.

The invention provides a transmission and transformation project construction cost assessment method and device. The transmission and transformation project construction cost assessment method comprises the following steps: input historical sample data of a transmission and transformation project are received; iterations, inertia weight, learning factors, particle velocity of a chaos particle swarm and the population size of the particle swarm are initialized to build a chaos particle swarm model; according to the chaos particle swarm optimization, parameters of the chaos particle swarm model are optimized; according to the historical sample data and the optimized chaos particle swarm model, optimal values of the iterations, inertia weight and learning factors of the chaos particle swarm model are determined; according to the determined optimal values of the iterations, inertia weight and learning factors, penalty coefficients, insensitive coefficients and kernel function parameters of a least square support vector machine model are determined respectively to build the least square support vector machine model; input actual sample data of the transmission and transformation project are received; according to the actual sample data of the transmission and transformation project and the built least square support vector machine model, a construction cost assessment result of the transmission and transformation project is generated.

The present invention relates to a method for predicting the load of a dedicated bus in large-scale industrial enterprises. The fuzzy clustering method of the SOM network is used to select similar days to be predicted, and the load data of the similar days are decomposed, denoised and reconstructed by db4 wavelet. As the training sample of the later prediction model; the penalty parameter and kernel function coverage width of the least squares support vector machine are optimized by using the chaotic particle swarm optimization algorithm, and the bus load forecasting model of the least squares support vector machine optimized by the chaotic particle swarm optimization is constructed. The method of the invention aims at the characteristics of many types of bus loads, uneven distribution, weak regularity and the like in large-scale industrial enterprises, and predicts the load of the bus, which can effectively improve the prediction accuracy of the bus load.

The invention provides a binocular calibration method based on a chaotic particle swarm optimization algorithm. A plurality of sets of dot array planar calibration board image pairs with different poses are simultaneously photographed through two image cameras. On condition that distortion is not considered, initial values of inner parameters and outer parameters of a left image camera and a right image camera are obtained by means of a Zhang's planar template linear calibration method. Then on condition that a two-order radial distortion and a two-order tangential distortion are considered, a three-dimensional reprojection error is minimized by means of the chaotic particle swarm optimization algorithm, thereby obtaining final inner parameter and final outer parameter of the two image cameras. In an iteration optimization process, a global adaptive inertia weight (GAIW) is introduced. A particle local neighborhood is constructed by means of a dynamic annular topological relationship. Speed and current position are updated according to an optimal fitness value in the particle local neighborhood. Furthermore chaotic optimization is performed on the optimal position which corresponds with the optimal fitness value in the particle local neighborhood. The binocular calibration method effectively settles a problem of low calibration precision caused by a local extreme value in a previous particle swarm optimization algorithm, thereby improving binocular calibration precision and ensuring high precision in subsequent binocular three-dimensional reconstruction.

The invention relates to an input shaper closed-loop control method based on a chaotic particle swarm optimization algorithm and belongs to the technical field of methods for drive control in the coaxial transmission machine start process. In order to solve the buffeting problem in the coaxial transmission machine start process, closed-loop control is performed on a transmission mechanism with the control method, and the effectiveness and the feasibility of the control method are proved by experimental results. In an offline state, a double-pulse input shaper is optimized through the chaotic particle swarm optimization algorithm, optimization parameters of the double-pulse input shaper are obtained, and then a PD closed-loop actuating mechanism is controlled through the optimized input shaper. The input shaper parameter self-tuning control algorithm based on chaotic particle swarm optimization is provided for solving the buffeting problem in the coaxial transmission printing machine start process. Due to the control, a system can respond fast without vibration while torsional vibration of the system is substantially suppressed.

The invention discloses an adaptive CPSO (Chaos Particle Swarm Optimization) integrated navigation satellite selection method, and the method comprises the following steps: 1, extracting visible satellites observed by a receiver at a current moment according to a navigation message and an observation file, numbering the visible satellites, grouping the satellites according to the number of selected satellites, and randomly selecting N combinations from all groups to form an initial population; 2, determining a fitness function for particle evaluation; 3, continuously updating the speed and position of particles through a speed-displacement model in a PSO (Particle Swarm optimization) algorithm till a visible satellite combination meeting a condition is searched. The method is advantageousin that 1, the integrated navigation satellite selection method based on the CPSO quickly achieves the BDS / GPS integrated navigation satellite selection, and the result meets the requirements of the receiver for the positioning precision; 2, the PSO algorithm is a potential neural network algorithm, needs fewer adjustment parameters, and is easy to implement. The PSO is applied in the satellite selection process of the receiver, thereby providing a new idea for the subsequent multi-constellation satellite selection research and application.

The invention discloses a reconfigurable-calculation hardware and software task partitioning method based on a chaotic particle swarm optimization algorithm. The method is on the basis of a multi-objective optimization method of DFG diagram hardware and software task partitioning, factors, such as the operation execution time of modules and delay and resource consumption caused by data communication of the modules are considered comprehensively, a multi-objective optimization objective function Ffitness is designed, the maximum of the objective function can be searched by the chaotic particle swarm optimization algorithm when the predetermined constraint conditions are met, and the optimal solution is figured out.

The invention provides a least square support vector machine (LSSVM) periodic weighting prediction method based on wavelet and chaos optimization. The method utilizes the wavelet decomposition technology to enable selected sample set data to be decomposed into components of different frequencies. A component phase space is reconstructed on the basis of the chaos theory, the reconstructed components are trained by using LSSVM models, parameters of LSSVM prediction models are optimized through chaos particle swarm optimization, and finally wavelet reconstruction is performed on prediction components obtained by the LSSVM models to whole periodic weighting load prediction waveforms. Through wavelet decomposition of a periodic load, reconstruction of a load phase space on the basis of the chaos theory, construction of a least square support vector machine, and chaos particle swarm optimization of the parameters, the LSSVM periodic weighting prediction method based on the wavelet and the chaos optimization can predict periodic weighting load waves at some period of reconstructed waves when load time sequence has certain chaos characteristics and can be widely used for prediction of periodic weighting load to be born of a support.

The invention discloses a distributed computing method for smart grid hybrid network architecture. The method includes the following steps: establishing system model; The Basic Parameters of Initializing Immune Chaotic Particle Swarm Optimization; Np particles are randomly generated to calculate the processing delay of the whole task under the current task assignment. Calculating a fitness value of each of the current Np particles; Judging the number of particle iteration, if the number of particle iteration reaches the set immune interference step size e, performing immune mutation operation,otherwise updating the individual optimal fitness value of each particle and the global optimal fitness value of all particles; Judge whether the maximum number of iterations of immune chaotic particle swarm optimization algorithm is reached, if so, output the particle corresponding to the global optimal fitness value of the algorithm as the optimal scheme of task allocation, otherwise continue to iterate. The invention can calculate the optimal scheme of task distribution and improve the processing capability of the smart grid for high concurrency and low time delay tasks.

The invention provides a method for identifying carbonate rock fluid based on a fuzzy C mean cluster in oil exploration. According to the method, chaotic quantum particle swarm optimization (CQPSO) and a fuzzy C mean (FCM) algorithm are organically bonded, chaotic particle swarm optimization is utilized to initialize a membership matrix, the problem that a traditional fuzzy C mean algorithm is sensitive to initialization can be effectively solved, high capability for searching global optimal solution is possessed, and fuzzy classification capability is remarkably improved. The method is introduced into carbonate rock fluid identification, the problem that rock physical analysis results and seismic inversion results are not matched due to frequency dispersion of seismic data can be effectively solved, and identification accuracy of the carbonate rock fluid is improved. Besides, by means of the method, probability of properties of various fluids can be calculated, evaluation on indeterminacy of fluid identification can be conducted so that exploration risks can be effectively reduced, and a new research thought for fully utilizing various prestack elastic information to achieve carbonate rock reservoir fluid identification is provided.

The invention provides a multi-target reactive power optimization method based on a chaotic particle swarm algorithm, and relates to a multi-target reactive power optimization method. The multi-targetreactive power optimization method aims to solve the problem that the multi-objective reactive power optimization control variable may fall into a local optimal solution and the speed of solving theoptimal value is low. The multi-target reactive power optimization method includes the steps: 1) inputting raw data of the particle swarm to an adaptive chaotic particle swarm algorithm program; 2) according to the magnitude of the fitness values, preferentially selecting the first m particles as the initial position of the particle swarm; 3) obtaining the inertia weight w of each particle by calculating the inertia weight coefficient formula, and preferentially selecting the first M optimal particles to perform chaotic optimization calculation; 4) according to the particle swarm reactive power optimization algorithm, updating the velocity and the position of the particles, that is, the iterative correction and the value of the control variable; and 5) determining whether the iterative termination condition is satisfied, that is, completing the multi-target reactive power optimization method based on the chaotic particle swarm optimization algorithm. The multi-target reactive power optimization method based on a chaotic particle swarm algorithm is applied to the electric power system field.

The invention belongs to the technical field of multi-hop networks, and discloses a distance-corrected chaotic particle swarm multi-dimensional scaling wireless sensor network positioning method. During a diversity matrix building stage, all node pairs adopt recursive measurement and RSSI to perform weighted correction on distance, rather than utilization of 1 to replace the distance between the node pairs when the distance between the nodes cannot be measured, a node hole problem is avoided, and a distance estimation error is reduced. During a coordinate conversion stage, a chaotic particle swarm optimization algorithm is used for optimizing a conversion parameter, the problems of local optimal solution of the particle swarm and misconvergence are avoided, influence caused by the parameter is reduced, and node positioning accuracy is effectively improved. A simulation result shows that the distance-corrected chaotic particle swarm algorithm can significantly improve node positioning accuracy, and has better robustness and adaptation to irregular networks.

The invention relates to the field of photovoltaic power generation, in particular to an adaptive inertial weight chaotic particle swarm optimization (ACPSO) algorithm. The algorithm utilizes segmented Logistic mapping with higher efficiency than general Logistic mapping to generate a chaotic sequence to initialize the positions of particles, so that the diversity of global search is ensured; then, a particle swarm algorithm is optimized by adopting an adaptive inertia weight, so that the tracking speed of the maximum power is increased; and finally, if the judgment algorithm falls into precocity, performing extreme value disturbance on the particle optimal position and the global optimal position at the same time. Compared with single extreme value disturbance, the method can enable the algorithm to jump out of local optimum faster. According to the algorithm, the maximum power point can be tracked more quickly and effectively in real time according to sunlight changes, the system works near the maximum power point, meanwhile, the oscillation phenomenon of the system at the maximum power point is reduced, and the utilization rate of a photovoltaic array is increased.

The invention provides an ultra-short-term prediction-based smooth new energy power generation control method for an energy storage system. The method comprises the following steps: reading related operation data of new energy and the energy storage system; building a target function on the basis of an ultra-short-term prediction power and a charged state of the energy storage system; optimizing six control variables in a control strategy by an adaptive chaotic particle swarm optimization algorithm according to the target function; obtaining a power command value of the energy storage system on the basis of the optimal solution of the control variables and carrying out power limitation on the power command value of the energy storage system; updating the control variables in a rolling manner according to the characteristic that ultra-short-term prediction forecasts once every 15 minutes; and outputting the power command value of the energy storage system to an energy storage control system to execute control on the energy storage system, and achieving a smoothing function of new energy output. By the ultra-short-term prediction-based smooth new energy power generation control method for the energy storage system, the charged state of the energy storage system is kept in an appropriate level; the continuous charging and discharging capabilities of the energy storage system are improved; and cooperative optimization of the smoothing capability and the performance index of the energy storage system is achieved.

The invention discloses a weak signal detection method of stochastic resonance based on an adaptive chaotic particle swarm algorithm. Firstly, the stochastic resonance problem is converted into a multi-parameter synchronous optimization problem of a second-order Duffing system, and the multi-parameter optimization of the system is completed by using the adaptive chaotic particle swarm algorithm. The second-order Duffing system is transformed into a second-order chaotic system, and all the particles are optimized according to the ergodicity of chaos. In the process of optimization, the inertiaweight is adaptively adjusted according to the particle optimization ability, particle velocity and position are updated, the maximum value of the updated particle fitness is judged, the optimal parameters of the second-order Duffing system are found accurately. The optimal system structure parameters are substituted into the second-order Duffing oscillator stochastic resonance system, Stochasticresonance is realized. When weak signal, Gaussian white noise and second-order Duffing nonlinear system produce synergistic effect, part of energy of noise is transferred to weak periodic signal at low frequency, the maximum signal-to-noise ratio is output, and weak signal under Gaussian white noise background is detected.

The invention provides a photovoltaic containing power distribution network voltage drop detection compensation method using UPQC implementation; the method comprises the following steps: 1, collecting voltage signals; 2, conditioning acquisition signals; 3, calculating a voltage drop compensation order; 4, using a multi-agent chaotic particle swarm optimization algorithm to set control parameters of a PI control unit; 5, using the set optimal PI control parameters to form and output PWM control signals; 6, outputting a compensation voltage so as to apply voltage compensation for the power distribution network. The method uses the multi-agent chaotic particle swarm optimization algorithm to set the PI control parameters of the UPQC, determines and uses the optimal PI control parameters to realize PI control, thus applying voltage compensation on the power distribution network, improving the UPQC voltage drop detection and compensation application precision and accuracy, and providing a novel method using the UPQC to manage the electric energy quality of the photovoltaic containing power distribution network.

The present invention provides a coal blending cost optimization method and device, and a storage device. A gray relative analysis method and an SVM are employed to establish a coke quality predictionmodel, a dynamic timing sequence matching method is employed to register routine ERP data, experiment data and lithofacies data, establishing a coal blending cost optimization model, and employing anelite learning chaotic particle swarm optimization method to solve a method for optimization of coal blending cost and an optimal coal blending scheme so as to achieve the purpose of reduction of thecoal blending cost and full utilization of the coal blending materials. The coal blending cost optimization device, and the storage device are used to achieve a coal blending cost optimization method. The coal blending cost optimization method and device, and the storage device can improve the accuracy, the stability and the calculation precision of the coal blending cost optimization method, andmaximally reduce the coal blending cost while ensuring that coke quality meets the enterprise requirements.

The invention discloses a steam-driven draught fan full working condition online monitoring model modeling method based on a CPSO-LSSVM. The steam-driven draught fan full working condition online monitoring model modeling method based on the CPSO-LSSVM comprises the following steps that firstly, design data from a draught fan factory are analyzed and processed, and actual working condition points are selected as training data; a chaotic particle swarm optimization algorithm is used for optimizing modeling parameters and supporting modeling on a least square support vector machine to obtain a static model; a full working condition model of a steam-driven draught fan is obtained through training based on the existing static model by combining rotating speed variables; the established full working condition model is published in an online website mode by combing a webpage programming technique, so that the working points of the draught fan can be determined on line; finally, the actual operating data of the stem-driven draught fan are obtained by combining an SIS to carry out real-time online correction on the established model. Through the model obtained by using the method, the full working condition operating characteristics of the steam-driven draught fan can be accurately reflected, online correction can be achieved, the draught fan can operate correctly even after the characteristics of the draught fan are changed, and guidance is provided for actual operation.

The invention relates to a method for designing a fully randomized silicon-based waveguide optical grating on the basis of a chaotic particle swarm optimization algorithm. Uniform optical grating design parameters are made inhomogeneous, the overall variation of the optical grating is designed as the change of each periodic block, and a coupling efficiency value under the case of each parameter isan adaptation degree in the particle swarm optimization algorithm; when a particle swarm evolves to the next generation, each particle updates itself by tracking two optimal solutions including pbestand gbest; when a boundary position value is taken as a particle value, chaotic variable processing is conducted, and a global search function is achieved; an initial value is provided for a chaoticvariable, and a group of random sequences with ergodicity and pseudo-randomness are generated through iteration of a chaotic iterative equation; a coupling efficiency standard required for the opticalgrating is set, when a particle swarm optimization result reaches the required standard, the process stops automatically, and the particle value corresponding to the adaptation degree is the demandeddesign parameter value. Systematic design of the fully randomized optical grating is achieved, and design parameters and the coupling efficiency of the optical grating can be effectively and quicklyobtained.

The invention relates to the field of Internet information processing, and provides a monitoring safety prediction method and system based on a chaotic particle swarm optimization algorithm optimization prediction model, equipment and a medium aiming at the defects of high randomness, large calculation amount, low speed and low efficiency of a parameter adjustment method in an existing data safety monitoring model. A sample data set is divided into a plurality of training subsets and a test set, parameter optimization of a constructed prediction model algorithm is performed by using the plurality of groups of training subsets according to a chaos particle swarm optimization algorithm, and a plurality of groups of optimal parameters are acquired, so that a model with optimized parameters is trained and is utilized to complete prediction of monitoring data. The optimization process is added to the modeling process to achieve parameter optimization, model inaccuracy caused by randomness is avoided, the optimization process is improved in combination with the chaos thought, the parameter optimization effect is improved, the parameter optimization speed is increased to optimize model efficiency, model quality and reliability are guaranteed, and then data safety monitoring accuracy and judgment efficiency are improved.

The invention discloses a dynamic time-varying frequency spectrum allocation method based on an improved chaotic particle swarm optimization algorithm, and solves the problem of frequency spectrum allocation taking maximum network benefit as a criterion in a cognitive radio network. In the strategy, chaotic mapping is introduced to carry out traversal optimization on an initial population and eachgeneration of particle positions, so that the global optimization performance of particles is improved, a dimensionality reduction spectrum distribution mathematical model is established, the computational complexity of the algorithm is reduced, and the time cost is reduced. The improved chaotic particle swarm optimization algorithm is applied to the spectrum allocation problem, available spectrums can be allocated to users more efficiently, the total benefits of the system are maximized, and the spectrum utilization rate is further improved.

The invention discloses an urban impervious surface coverage rate remote sensing estimation method. According to the urban impervious surface coverage rate remote sensing estimation method, medium resolution remote sensing images high in cost performance are taken as the main data sources, chaos particle swarm optimization support vector regression is adopted for estimation of urban impervious surface coverage rate; improvement on parametric optimization algorithm in support vector regression is carried out, an improved support vector regression UISP remote sensing estimation method applied tomedium resolution remote sensing images is promoted so as to increase precision of support vector regression in medium resolution remote sensing image UISP estimation. The performance of UISP estimation taking the medium resolution remote sensing image as the main data sources is improved further, and more accurate urban impervious surface data is provided for correlation research taking urban impervious surface information as input data.

The invention discloses a visible light positioning method and system based on chaotic particle swarm optimization. The system is composed of a transmitting terminal subsystem, a transmission subsystem and a receiving terminal subsystem. The transmitting terminal subsystem consists of a modulation module and an LED module; ID location information of an LED light source is modulated to send out the information in a visible light signal manner; the visible light signal is transmitted to the receiving terminal subsystem by the transmission subsystem; a photoelectric detection device in the receiving terminal subsystem carries out detection to obtain a light intensity attenuation factor; and the light intensity attenuation factor is inputted into a data processing module. The data processing module includes a chaotic particle swarm optimization algorithm; an early-maturing detection mechanism is introduced into the chaotic particle swarm optimization algorithm; when a particle swarm matures early, disturbance is carried out on the particle swarm based on the chaotic algorithm to generate a new particle swarm; a global optimal solution is obtained, wherein the obtained global optimal solution is a positioning point coordinate; and then a display module outputs a three-dimensional physical coordinate of the positioning point.

The invention protects a Chinese spam recognition method based on a chaos particle swarm optimization CNN (convolutional neural network). According to the method, first, a word segmentation device isused to perform word segmentation, stop word removal and other preprocessing on a Chinese spam dataset; second, a Word2vec model is adopted to acquire word vectors, and text vectors of Chinese spam are obtained by solving the sum and the mean value of the word vectors; third, the chaos thought is introduced into a particle swarm optimization algorithm to train network parameters of the CNN; fourth, a Chinese spam classification model is established based on the chaos particle swarm optimization CNN; and last, a test set is adopted to realize spam classification through the established model, and the classification correct rate is calculated. Through the method, quick convergence can be realized through the model established according to optimization parameters of the chaos particle swarm optimization algorithm, good robustness and stability are achieved, and meanwhile the classification recognition rate of Chinese spam can be increased.

The invention provides an AC servo motor driver control method. The method comprises steps that S1, a AC servo motor is controlled for start, and a given rotation speed is set; S2, a rotor position signal and a phase current of the AC servo motor are detected; S3, the phase current is transformed to obtain the d-axis current, the d-axis reference current is set, and a d-axis reference voltage is obtained through a current inner loop PI controller; S4, an angular velocity deviation value is calculated according to the rotor position signal and the given rotation speed, a q-axis reference voltage is obtained by a velocity outer loop PI controller and the current inner loop PI controller, and the velocity outer loop PI controller adopts the multi-agent chaotic particle swarm optimization algorithm for parameter setting; and S5, an SVPWM signal for driving the AC servo motor is generated according to the d-axis reference voltage and the q-axis reference voltage. The method is advantaged inthat the method not only can quickly respond to the current and suppress current spikes, but also can make an AC servo motor drive system have strong anti-interference, and improves system stability.