94results about How to "Reduce the amount of online calculations" patented technology

The invention discloses a KNN-based text classification method which is suitable for nuclear safety software verification and reliability verification. The KNN-based text classification method comprises a training process processing step and a test process processing step: representing training sample data sets by information of original texts and all the titles in the texts; and constructing two DBM models according to characteristic hierarchical structures in the texts from shallow to deep, extracting and storing deep characteristics with low dimensionality and high discrimination, and determining the categories of to-be-tested texts through considering the contribute brought from text titles to the similarity calculation via proper weights in the test process. According to the method disclosed in the invention, information of the text titles are fully utilized, the classification performance, relative to the condition of taking the shallow characteristic vectors as training sets, is remarkably improved, and meanwhile, the storage demand and online calculation amount are reduced at the same time, so that the problem of characteristic vector high-dimensionality disaster is solved and the classification correctness is improved; and the method can be used for the matching of rules in safety level software reliability evaluation analysis and the establishment of a failure mode library.

The invention discloses an asynchronous motor model prediction direct torque control method, and relates to the technical field of motor control. Two control methods are improved through the mode of partition prediction and table lookup on the basis of comparative research of the performance of two control methods. According to the method, the system performance can be further improved, and the transient and steady-state torque ripple, stator flux linkage and current fluctuation of the motor can be significantly reduced. The simulation result proves that the torque, the flux linkage and the current fluctuation of the conventional DTC can be effectively reduced by the novel DTC strategy, the advantages of rapid response and simple control of the conventional DTC can be maintained and the control performance of the conventional DTC can be improved; meanwhile, vector selection is performed through combination of the partition table so that the dependence of finite model prediction controlon the model parameter accuracy can be reduced, and online computation of finite model prediction control can also be greatly reduced.

The invention discloses a method for intelligently calculating, predicting and controlling steam temperature of a power station boiler. The method comprises the following steps: step one, determining a sampling time interval Ts of prediction and control; step two, identifying local models Gm described by an input-output difference equation form of a steam temperature system of a unit operating under M typical load conditions; step three, tacking, simulating tracking control of square-wave signals of all the local models Gm by using model prediction control; step four, training an intelligent calculation model by using control simulation data of all the local models GM to form an intelligent prediction controller IPC; step five, identifying a nonlinear inverse model of a regulation valve; and step six, taking the trained intelligent prediction controller IPC as a real-time controller to obtain a temperature-reducing water valve opening degree actual command u(t) which can be implemented at current sampling time. The method has the advantage that the problem of low control performance of steam temperature caused by the change of steam temperature object with long time delay, great inertia and time-variant characteristic along with the loads of the unit can be avoided.

The invention provides a dual-vector model forecast control method of a linear induction motor. Relatively good current quality can be obtained by employing the dual-vector control method. During thestarting and braking process of the motor, the current probably and instantaneously becomes large to generate overcurrent in order to obtain rapid dynamic response speed. A current constraint condition is considered, and the overcurrent is prevented from being generated during the running process of the motor; and meanwhile, under considering the current constraint condition, the vector selectionprocess of a dual-vector model forecast control algorithm is simplified, and the calculation quantity of the algorithm is reduced.

The invention relates to a method for rapid predicting controlling a model which includes steps as follows: 1) matrix converting an inequality constraint aggregate; 2) selecting compression matrix parameter of the inequality constraint aggregate; 3) constructing the inequality constraint aggregate compression matrix; 4) mapping changing of the constraint aggregate based on compression matrix; 5) rolling horizon strategies of model predicting optimization problem; 6) applying of rapid control model predicting control rule. The invention only convert to the constraint aggregate and dose not refer to any conversion of waited optimization variable that can reduce scale of MPC optimization control problem efficiently, decrease the on-line calculating amount, increase calculating efficiency, and does not effect calculation inputted by optimization control, can ensure MPC optimization control function. The method has few waited parameter, simple construction method, is easy to on line debugging, and is suitable for industry process, for example petrochemical engineering, and also is suitable for high real-time control field, for example car and robot.

The invention provides an energy management method of a hybrid electric vehicle based on power spectrum self-learning prediction. The energy management method comprises the following steps: step 1, calculating current driving demands of the vehicle, and identifying current calendar time, moment and the like; step 2, receiving the information about a future running route and a future running direction of the vehicle, climatic information and the information about whether the current day is a workday or not; step 3, making similarity judgment on future running conditions and corresponding records in a self-learning power-spectrum database; step 4, executing an energy management strategy based on rules; step 5, performing power-spectrum prediction, and power control and energy management of the hybrid electric vehicle based on a self-learning power spectrum; step 6, establishing a power-spectrum database based on self-learning; and step 7, outputting a control-instruction rotating speed or torque to a power supply module so as to realize power control and optimal energy management. The energy management method overcomes the defects existing in a conventional energy management strategy of a hybrid electric vehicle, realizes the self-adaption optimal energy control for the working conditions of the hybrid electric vehicle, and is liable to be applied to a real vehicle.

The invention relates to a control method for removing the overshoot of a displacement generated by the soft start of a vibrating meter under three-parameter control. Based on the three-parameter control, the method provides an overshoot-correcting arithmetic of an off-line time domain: the method obtains an overshoot theory solution generated by the three-parameter control arithmetic by solving a three-parameter control differential equation in the time domain; and in order to directly remove a transient state component, and by taking trigonometric function and Fourier transform as tools, the method provides a correction method of the overshoot under any input. Compared with the conventional soft start in a windowing way, the method reduces the overshoot, and greatly reduces the on-line calculating amount, thereby improving the operating efficiency of a controller when removing the overshoot of the displacement of the vibrating meter.

The present invention provides a controlling method of a multivariate nonlinear system prediction function based on the hammerstein model, characterized in that the method includes following steps: (1) establishing the hammerstein model according to the process characteristic and the input output data; (2) solving the prediction function control rate of the multivariate linear subsystem according to the hammerstein model linear part model parameters, set values and practical process output of; (3) solving the equation V(k)=F(U(k)) to obtain optimal control law U(k) according to the hammerstein model nonlinear part model parameters and the multivariate linear multivariate nonlinear system prediction function control rate; (4) solving and implementing the optimal control law according to multivariate nonlinear system prediction function controller. The invention realizes one prediction function control of multivariate nonlinear system, realizing that the control problem of the nonlinear system is transformed to the linear system control problem and the solving problem of the nonlinear equation, improving the solving speed of the optimal control law, reducing the on-line computational complexity, ensuring the real-time requirement of the control system.

The invention discloses an intelligent calculating prediction control method of a thermal power generating unit coordination control system. The method comprises the following steps of step1, determining a sampling time interval Ts of prediction control; step2, through an unit characteristic test, acquiring a local model Gm described by an input-output difference equation form of a boiler steam turbine system under M typical load conditions; step3, using model prediction control to carry out square wave signal control simulation on all the local models Gms in the step2; step4, using control simulation data of all the local models Gms to train an intelligent calculating model so as to form an intelligent prediction controller IPC; step5, taking the intelligent prediction controller IPC acquired from the step4 through training as a real-time controller, collecting needed input signals and sending into the intelligent prediction controller IPC, wherein the intelligent prediction controller IPC automatically calculates and acquires a control increment instruction delta u (t) needed for adjusting the boiler steam turbine under any conditions. In the invention, accuracy is high, a reaction speed is fast and an on-line calculated amount is small.

The invention discloses a prediction control method and system for a magnetic levitation system. By historical data of the magnetic levitation ball system and through adopting a system identificationmethod, an autoregressive model which takes a Gaussian radial basis function network as a coefficient and has an exogenous variable is established to describe nonlinear dynamic characteristics betweenan input voltage of an electromagnetic winding and a position of a steel ball. An ARX model coefficient is fit by an RBF neural network, so that an RBF-ARX model can well depict the nonlinear dynamiccharacteristics of the magnetic levitation ball system. Then, on the basis of a principle of a predictive functional control algorithm, a structure of a control input is determined so as to completerolling optimization and error correction. The predictive functional control algorithm has higher tracking capacity and higher robustness than PID control, and has a smaller online calculation amountand a higher control speed than conventional model prediction control; and when a system set value is a step signal, the control method can implement unbiased tracking.

The invention discloses a quick non-linear fuzzy predictive control method for a speed regulating system of a pumped storage unit. The quick non-linear fuzzy predictive control method includes the three steps of fuzzy PID parameter self-adaption, online rolling prediction and control law computing. By the adoption of the control method, the function that the fuzzy PID control parameters are adjusted automatically along with the working conditions is achieved, the future system state is predicted through a pumped storage unit speed regulating system non-linear model built in a controller by means of the online rolling prediction method, and the future system state quantity deviation information is considered when a real-time control law is set. By the adoption of the quick non-linear fuzzy predictive control method, the requirements of the control process of the pumped storage unit under different working conditions can be met, the control precision of the unit is improved effectively, and the transient state performance of the operating process of the unit is improved.

The invention discloses a triggering-type predictive control-based aircraft control method. The method includes the following steps that: the optimal control sequence of a model predictive control algorithm at each sampling time point is determined; the condition triggering equation of a condition triggering mechanism is determined through the output and state variable of a system; and the condition threshold value of the system is set according to control requirements, whether the system should perform condition triggering is judged according to the comparison result of the value of the condition triggering equation and the threshold value at each sampling time point. According to the triggering-type predictive control-based aircraft control method of the invention, the model predictive control algorithm and the condition triggering mechanism are combined according to the problem of large on-line computation amount of a traditional model predictive control algorithm, and therefore, the computation amount of the model predictive control algorithm can be decreased, and the on-line implementation efficiency of the model predictive control algorithm can be improved.

The invention discloses a prediction-function-based variable-domain fuzzy PI control method of an alternating current servo motor. A PI controller, a variable-domain fuzzy controller and a predictionfunction controller are arranged. The PI controller is used as a main controller to adjust the rotating speed of the servo motor; the prediction function controller predicts the future state of the system according to a current input instruction, a control signal and a feedback output of the system; the variable-domain fuzzy controller uses prediction information as input to adjust the gain of the PI controller in an on line manner. Meanwhile, a variable-domain telescopic factor is designed, the discourse domain of the fuzzy controller is adjusted online in advance by utilizing prediction information, and fuzzy rules for adjusting PI control gain are indirectly increased. Compared with traditional PI control and fuzzy PI control, the variable-domain fuzzy control method inherits the advantage of high precision of variable-domain fuzzy control and the dynamic response performance and disturbance resistance are enhanced; and the method has more outstanding advantages for application occasions with uncertain disturbance and large nonlinearity.

The invention discloses a double-layer control system for load tracking for a supercritical thermal power unit turbine-boiler system. The double-layer control system comprises an upper-layer controller and a lower-layer controller, and is characterized in that the upper-layer controller is an economic linear model predictive controller (ELMPC), the lower-layer controller is an L1 adaptive controller; the ELMPC builds a local linearized model with disturbing items of the turbine-boiler system, plans an optimal state and an output track of the system in a future period of time by taking the optimal economy in the future period of time as a target, acquires a corresponding optimal control variable, and transmits the optimal control variable, the corresponding optimal state of the system and the output track to the lower-layer controller; and the lower-layer controller is an L1 adaptive controller, and the L1 adaptive controller adds an adaptive correction value on the basis of the control variable planned by the upper-layer controller according to the optimal state and the output track which are transmitted by the upper-layer controller and an actual unit state and an output measurement value so as to be used for updating a prediction model of the upper layer.

Provided is an inversion range migration method for a synthetic aperture radar (SAR) large-scale scene deception jamming. The method comprises steps of: 1, constructing a jammer deception template according to a false electromagnetic characteristic which is planned to be generated by a jamming party in a radar image; 2, computing equidistant sampling values of a complementary focusing filter HHdiff (k, kx) on a two-dimension frequency domain (k, kx) plane; 3, computing equidistant sampling values of a consistent focusing filter HHbulk (k, kx) on a two-dimension frequency domain (k, kx) plane, and multiplying the equidistant sampling values of the consistent focusing filter HHbulk (k, kx) on the two-dimension frequency domain (k, kx) plane by the equidistant sampling values of the complementary focusing filter HHdiff (k, kx) on the two-dimension frequency domain (k, kx) plane acquired in the step 2; and 4, performing azimuth inverse Fourier transformation on a result acquired in the step 3 and multiplying a transformed result by the equidistant sampling values of a jammer blanking filter Hhelim (k, x) on a distance frequency domain (k, x) plane. The invention provides the SAR large-scale scene deception jamming method for overcoming defects in the prior art. The method computes jammer system frequency response accurately and high efficiently in order that a radar, when having high resolution, a large squint angle, and a long synthetic aperture, may satisfy computational complexity and focusing effects.

The invention provides an online planning method for reentry trajectory of an aircraft based on a particle swarm optimization algorithm. The method comprises the following steps of 1, determining an envelop range of an initial condition of reentry according to an reentry task requirement of the aircraft; 2, determining the sizes of piecewise constants of a heeling angle and the maximum number of inversion times of a heeling angle symbol according to the envelop range of the initial condition of reentry; 3, dividing the envelop range of the initial condition of reentry into grids, and ensuringthat an original envelop peak is a sub-envelop peak; 4, using each sub-envelop peak as the initial condition of reentry, and using the particle swarm optimization algorithm to design an reentry standard trajectory of the aircraft offline; 5, verifying validity of envelopment range division and the reentry standard trajectory via offline digital simulation; and 6, statistically analyzing a simulation result, and if the result does not meet an expected design requirement, returning to the step 3 to redivide sub-envelops; and if the result meets the expected design requirement, ending design. Themethod provided by the invention has the advantages of being small in online computing amount and high in placement accuracy of standard trajectory generated online.

The invention discloses a pipe network big data detection system which is composed of a pipe network parameter acquisition platform and a pipe network safety big data processing subsystem, the pipe network parameter acquisition platform detects pipe network parameters, the pipe network safety big data processing subsystem classifies pipe network safety, and pipe network operation benefits and reliability are improved; the method effectively solves the problems that an existing pipe network has no influence on pipe network safety according to nonlinearity and large lag of pipe network parameter changes, large pipe network area, complex parameter changes and the like, pipe network parameters are not predicted, and pipe network safety is not pre-warned, so that reliable operation and intelligent management of the pipe network are greatly influenced.

The invention discloses a dynamic matrix control method for a networked system having time delay and packet loss, comprising steps of choosing updated controlled quantity according to a time delay sequence when the time delay, pack loss and disturbed sequence exist in the same time, obtaining step response coefficients under various time delay through analyzing the time delay and combining with a controlled object model, and designing a controller based on a dynamic matrix control algorithm. The invention provides the dynamic matrix control method for the networked system which can effectively solve the time delay, the pack loss and the disturbed sequence.

The invention relates to a quick multivariable predication control method for an ethylene cracking furnace. The method has an offline link and an online link. In the offline link, a dynamic mathematic model and a static mathematic model of a production process of the ethylene cracking furnace are obtained through an analysis or recognition method and control parameters are set. In the online link, a prediction value is calculated and output according to a measurement value of an ethylene cracking furnace control system (DCS), a predication error is calculated based on the difference between the predication value and the measurement value and accordingly a static output predication value is corrected; and then a static control input predication value is calculated through static optimization or a direct inversion method; and finally a control input value of a current moment is calculated according to a given control parameter, the dynamic performance of the control input value is corrected through a lead-lag link and the corrected value is then transmitted to the DCS. According to the invention, the calculation complexity for multivariable prediction control is simplified and the control period of multivariable prediction control is reduced distinctively, and control problems in quick reaction processes of the ethylene cracking furnace and the like can be solved effectively.

The invention discloses a rectifying tower abnormal state identification method based on brief kernel principal component analysis, and aims to improve the efficiency of detecting the abnormal state of rectifying tower equipment in real time by using a KPCA method. Specifically, it is better for training a KPCA model by using typical representative sample data rather than by using as many sample data as possible. The method has the advantages and characteristics that part of representative data is screened out through edge point indexes to train the KPCA model, so that the online calculation amount of a kernel vector is greatly reduced. Besides, although part of data is screened, the unselected data is not denied completely, and the data is considered in online monitoring in a detection index construction mode. Finally, through a specific embodiment, it is verified that the calculation efficiency of the method is improved, and meanwhile, the identification capability for the abnormal state is not reduced.

The invention relates to a prediction control system and method for a polyethylene quality index system. The method comprises the following steps of establishing a state space model of the system by using the Hammerstein model; linearizing the state space model by using the inverse function and the polytopic description method; sending the trigger information to the controller when there is data packet dropout; extracting a corresponding prediction model of the data packet dropout environment by a controller; using the optimal performance index function to online solve the infinite time domain optimization problem with a free control effect to obtain the control sequence of the next period of time, and exerting the first control sequence on the controlled object; and repeating the above steps. The invention adopts the strategy of real-time optimization. When there is data packet dropout, the controller in the networked prediction control module can propose the prediction model corresponding to the packet dropout environment according to the situation of data packet dropout, so as to compensate the control deviation caused by the data packet dropout and to solve the data packet dropout problem in the network environment.

The invention discloses a livestock and poultry house environment big data detection system, which comprises a livestock and poultry house environment parameter acquisition and control platform and a livestock and poultry house environment big data processing subsystem, and is used for detecting and adjusting livestock and poultry environment parameters and predicting the yield. The system effectively solves the problems that an existing livestock and poultry breeding environment has no influence on the yield of the livestock and poultry breeding environment according to nonlinearity and large lag of livestock and poultry breeding environment parameter changes, the livestock and poultry breeding environment is large and complex in area, the yield of the livestock and poultry breeding environment is not predicted, and livestock and poultry breeding environment parameters are not accurately detected and adjusted; therefore, yield prediction and production management of the livestock and poultry breeding environment are greatly influenced.