460 results about "State space equation" patented technology

The invention relates to an estimation and implement method of state of charge (SOC) of battery for electric car, belonging to the field of electric car intelligent niformation processing technology. Said method utilizes the state space equation of battery which is formed from battery charge state equation based on ampere-hour metering method and measurement equation of battery load votlage, and uses the improved extended Kalman filtering equation to calculate and obtain the stale of charge of battery. Said invented advantage lies in that it has stronger adaptability, can eliminate initial error of SOC, and can raise the convergence of error or reduce speed, at the same time it can modify the change of SOC resulted frmo self-discharge of battery. Said invention is applicable to SOC estimation of cell monomer, module and battery.

The invention discloses a method for actively controlling multi-frequency vibration of a helicopter, including system identification, collecting the control voltage of the actuator and the acceleration response at the controlled point, using the recursive least squares algorithm for system identification, and finally obtaining the discrete state space of the secondary channel Equation; feedback controller design, using the secondary channel discrete state space equation obtained in step 1 to carry out discrete predictive sliding mode feedback controller design; reference signal and error signal acquisition, according to the helicopter rotor characteristics and speed characteristics, extract the rotor excitation frequency, synthesize reference signal, and collect the vibration response error signal at the controlled point; the feedforward-feedback hybrid control algorithm iterates, and uses the reference signal and error signal obtained in step 3 to iterate the feedforward controller and the feedback controller to obtain the feedforward-feedback Feedback the mixed control voltage signal; output the control quantity; use the mixed control voltage obtained in step 4 as the input signal at the next moment, drive the actuator to produce the required response, and return to step 3.

The invention discloses a modification-based state-of-charge estimation method for an RC battery model. The modification-based state-of-charge estimation method comprises the following steps: building an RC modification model; identifying parameters of the model; utilizing the extend Kalman filter (EKF) algorithm to obtain a continuous state space function of the battery RC modification model; according to the battery model parameter values of all points SOCs, acquired by the identification method, performing repeated curve fitting; then using the interpolation method to acquire the parameter values of the SOCs in the whole stage. The invention provides the EKF battery state-of-charge estimation method based on the RC modification model by the built RC modification model to overcome the defects of the classical battery state-of-charge estimation algorithm. Compared with a traditional method, the modification-based state-of-charge estimation method has the characteristics of favorable dynamic performance, insensitivity to initial value errors, excellent measurement noise inhibition property and the like.

The invention discloses a power lithium battery model parameter identifying and dump energy estimating method. The method includes the steps of firstly, establishing a state space equation of a two-order impedance-capacitance model of a lithium battery; secondly, establishing a regression model of the relation of an end voltage and open circuit voltage and a current; thirdly, conducting parameteridentifying based on an iterative learning recursive least-squares method on the regression model; fourthly, conducting identifying based on the iterative learning recursive least-squares method on experimental data meeting the discharge current conditions in the parameter identifying process in the third step to obtain prediction errors, and conducting converging through the prediction errors toobtain a nonlinear function in parameters of the state space equation and a parameter set; fifthly, verifying the battery model precision formed by the nonlinear function and the parameter set obtained in the fourth step; sixthly, collecting current and voltage data, and estimating the dump energy through expanded Kalman filtering. The model parameter identifying precision is high, the dump energyestimation value error is small, and good application prospects are achieved.

The invention discloses a power battery charge state estimation method and system. The method comprises the steps: building equivalent models for all kinds of power batteries based on laboratory environments and a standard battery second-order model, and fitting battery parameters; building a state-space equation of a battery output-input linear system according to the equivalent models; setting the initial SOC estimated value when a practical power battery system is started, determining the corresponding battery parameters according to the types of batteries in the practical power battery system, and setting a noise weight function needed by running of an H(infinity) filtering algorithm; and starting the running of the power battery system after the parameters are set, in the running process, collecting values of voltage, current and temperature of each battery at each sampling moment in real time, substituting the collected values of voltage, current and temperature of each battery at each sampling moment into the state-space equation and the noise weight function, and calculating to obtain the SOC estimated value at each moment in real time by adopting the H(infinity) filtering algorithm. According to the method, any assumption on outside noise is not required, so that the estimated accuracy is obviously improved, and the antijamming capability is greatly improved.

The invention discloses a brushless direct current motor electromagnetic torque observation method based on a self-adapting slip-form observer, belonging to the control field of permanent magnet motors. The observation method comprises the following steps of: constructing a state space equation of a brushless direct current motor in an alpha-beta coordinate system based on easily-observing signals, such as motor rotating speed, each phase current of a stator, each ground voltage of the stator, rotor position, and the like; observing a counter emf (Electromotive Force) of the brushless direct current motor in the alpha-beta coordinate system in real time by using the slip-form observer; meanwhile online identifying a stator resistance parameter of the brushless direct current motor to eliminate the influence of a resistance parameter error on counter emf observation; and finally calculating the electromagnetic torque of the brushless direct current motor in real time according to the counter emf, the current and the rotating speed. In the invention, when the stator resistance parameter is unknown or a stator resistance changes due to the influences of temperature and skin effect, the counter emf and the electromagnetic torque of the brushless direct current motor can be accurately observed, the problem of the traditional method influenced by the resistance parameter is solved, and an accurate torque feedback value can be provided for the torque close loop control of the brushless direct current motor.

A desired path active steering control method for a four-wheel independent drive vehicle, belongs to control field of unmanned vehicle. In order to solve the problem of active steering control by looking at the path, the main points are as follows: S1. A two-degree-of-freedom vehicle lateral dynamics model describes the lateral motion and pendulum motion of the vehicle, and discretizes the dynamics model to form a state space equation; 2, a prediction model is established by that state space equation, a rolling time domain optimization algorithm is implemented to plan the front wheel rotationangle, the control input vector is solved at the current time to obtain the front wheel rotation angle, and the expect trajectory is tracked by the vehicle active steering control, thereby improvingthe precision of the model and the safety of the vehicle driving.

The invention discloses a short-term wind speed prediction method of Gaussian process regression and particle filtering, thereby realizing on-line dynamic detection and correction of abnormal values and improving wind speed prediction accuracy. According to the method, an input variable set having the highest correlation with a wind speed value at a to-be-predicted time is determined by using a partial autocorrelation function, a state vector is determined, and a proper training sample set is constructed; a Gaussian-process-regression-based short-term wind speed prediction model is establishedin the training sample set and a fitting residue during the training process is given; on the basis of combination of the state vector and the Gaussian process regression model, a particle filteringstate space equation is established and state estimation is carried out on a current measurement value by using a particle filtering algorithm; and the estimation value and the measurement value residual of particle filtering are analyzed, determination is carried out based on a 3 sigma principle, and an abnormal value is corrected. According to the method provided by the invention, the abnormal value can be detected and corrected effectively; the short-term wind speed prediction precision is improved; and a wind speed prediction problem of the power system is solved.

The invention discloses a modular multi-level current converter passivity modeling and control method which mainly comprises the following steps: step 1, a modular multi-level current converter passivity mathematic model is established, and an equalization state-space equation within a switching period is obtained; step 2, a modular multi-level current converter energy forming method algorithm model based on the equalization state-space equation is established, and a passive system from an input state variable Xi to an output state variable X is defined; step 3, a current converter damping injection algorithm model is established; step 4, a current converter control model based on all module switching functions is established. The modular multi-level current converter passivity modeling and control method has the advantages that stabilized and balanced control over capacitance and voltages on the direct current sides of upper bridge arms and lower bridges arms and quick tracking control over currents on the alternating current sides of all the bridges arms can be realized rapidly, the defects of a traditional modular multi-level current converter control strategy are overcome, and a feasible means is provided for the design of a control strategy of a flexible direct current transmission system.

The invention discloses a gas transmission pipeline leakage detection system and method suitable for multiple state changes. The gas transmission pipeline leakage detection system and method suitable for the multiple state changes comprise a gas transmission pipeline leakage detection system (system for short) suitable for multiple state changes and a gas transmission pipeline leakage detection method (method for short) suitable for the multiple state changes. The system comprises an input module, a gas transmission pipeline transmission mechanism model, a neural network compensation model, a pipeline system signal collection module, a correction output processing module and an error obtaining module; the method aims at building a gas transmission pipeline mathematical model, obtaining a gas transmission pipeline state-space equation, processing flow signals of the gas transmission pipeline state-space equation, building the RBF neural network and verifying a detection model; the gas transmission pipeline flowing mathematical model and the detection method based on knowledge are combined, tail end loads, the pipeline diameter, pipeline bending, the environment temperature, the gas wave velocity, a compression coefficient, the pipeline hydraulic frictional resistance coefficient and the like are researched, and the gas transmission pipeline leakage is monitored; and a pipeline model is trained, and an accurate model suitable for the pipeline condition changes is obtained.

The invention relates to a power lithium battery state joint estimation method based on a multi-dimensional coupling model, and belongs to the technical field of battery management. The method comprises the following steps: S1, building an electric model of a battery, and building a battery thermal model and an aging model on the basis of the electric model; S2, recognizing model parameters with test data; S3, building an electric-thermal-aging coupling model of the battery; S4, estimating battery SOC in real time on the basis of the coupling model with a control method based on a state spaceequation, meanwhile extracting a battery center temperature value in the thermal model to serve as a SOT estimation value, and estimating the battery SOH with a capacity calculating method; and S5, setting a corresponding coupling framework in order to finish the joint estimation of the SOC, SOT and SOH of the battery. Through adoption of the method, the battery temperature and capacity changes are updated in real time in the SOC estimating process, so that the SOC estimation accuracy can be is improved greatly, and the SOT and SOH estimation accuracy are improved.

The invention relates to a method for adaptively and jointly estimating the state of charge (SOC) of a lithium ion battery and a model parameter. The method comprises the following steps of: 1) constructing an offline model including an open-circuit voltage module and an equivalent circuit model; 2) performing online joint estimation including: establishing an nonlinear state-space equation basedon a current integration method and the offline model; performing sensitivity analysis based on a circuit parameter function and establishing an augmented nonlinear state-space equation; establishingSOC and model parameter adaptive joint estimation model based on a moving horizon estimating strategy, and setting various algorithm parameters; jointly estimating the SOC and the model parameter by using the online estimation model based on detected voltage and current. Compared with the prior art, the method, by means of model parameter online estimation, improves the accuracy and reliability ofthe SOC estimated value of the lithium ion battery in an environment where operating condition property difference and an individual are inconsistent, and finally improves the overall performance ofa battery management system.

The invention relates to a power battery system parameter and stage of charge joint estimation method, which comprises the steps of firstly performing online data acquisition, and acquiring voltage, current and temperature of a battery in real time; then establishing an HF (H infinity filter) state-space equation, updating an HF state vector of the battery in real time by using an HF algorithm, wherein the HF state vector comprises ohmic internal resistance, polarization internal resistance and polarization capacitance of the battery; and finally establishing a UKF (Unscented Kalman Filter) state-space equation, updating the HF state vector in real time by combining the HF algorithm, updating a UKF state vector of the battery in real time by using a UKF algorithm, wherein the UKF state vector comprises a state of charge of the battery. Therefore, model parameters are identified online by using the HF algorithm, and the model parameters are transferred to the UKF algorithm to perform online real-time SoC (State of Charge) estimation, thereby tracking model parameter variations of the battery in real time according to different battery operating environments, and thus improving the SoC estimation precision.

The invention discloses an unmanned vehicle transverse motion control method based on GK clustering algorithm model prediction. The method comprises the steps of 1, acquiring the current state of a vehicle in real time; 2, collecting the surrounding environment of the vehicle, and planning an expected path in real time; 3, establishing a monorail vehicle model by using a GK clustering algorithm; 4, converting the monorail complete vehicle model obtained in the step 3 into a state space equation of a linear error model, and discretizing the state space equation; 5, using a linear time-varying model prediction control algorithm to establish a linear time-varying model prediction controller; taking the mass center speed under a vehicle coordinate system, the course angle, the yaw velocity andthe vehicle position as input of a model prediction controller, taking the front wheel rotation angle as the output of the controller, calculating the track points in the prediction time domain and acontrol sequence in the control time domain according to the current state and the target track, converting the problem into a quadratic programming problem to obtain an optimal solution, and updating the vehicle state; and 6, sequentially controlling the steering of the target vehicle according to the control quantity obtained by the model prediction controller.

The invention discloses a tracked unmanned vehicle trace tracking controller design method based on a kinematic model, and belongs to the technical field of vehicle control. The method includes the steps: firstly, building a state-space equation according to the kinematic model of a slip tracked vehicle; secondly, performing standard quadratic transformation according to a set target function; finally, calculating a new control sequence through the target function optimization process by a system in a next control period T, and repeatedly performing rolling optimization to realize trace tracking control. According to the method, a built controller has good tracking capacity for continuous and discrete point paths, particularly, running speed is stabilized near expected speed in the low-speed tracking process, linear tracking steady-state errors approach to 0, and running traces are smooth.

The invention discloses a flexible spacecraft active fault tolerant control method based on dynamic output feedback control, which belongs to the field of spacecraft attitude control. The method comprises the steps that a dynamic equation of a flexible spacecraft attitude control system is transformed into a general state space equation; a fault model with additive sensor measurement offset is established; a fault detection and identification module composed of an unknown input observer and a filter is established; real-time detection and on-line estimation are carried out on unknown sensor fault; and a fault tolerant controller based on dynamic output feedback is designed based on acquired fault estimation information. According to the invention, when a flexible spacecraft can normally reach an expected posture in the case of an additive sensor measurement offset fault; the influence of disturbance generated by modeling uncertainty and flexible accessory is considered in a design process; and a fault diagnosis and identification module and the fault tolerant controller can be independently designed, and can be easily realized by a project.

The invention provides a lithium ion battery health condition on-line estimation method applied to space, relates to the technical field of lithium ion battery health condition estimation, and solvesthe problems that in an existing battery health condition estimation method, most of methods for taking a capacity or an internal resistance as health factors cannot realize on-line estimation application, a data driving method overly depends on off-line test data set completeness, and dynamic change characteristics in the actual working process of a battery cannot be sufficiently considered. Theon-line test data of the battery is acquired, and an on-line health factor sequence is established according to the on-line test data of the battery, a mapping relation is established between SOH andan on-line health factor, based on a double-index degradation model, a state-space equation is established by the combination with the mapping relation between the established battery SOH and the on-line heath factor, and based on the state-space equation, estimation of SOH is estimated by the combination with a UPF algorithm, an SOH estimation result and the confidence region corresponding to theestimation result are obtained, and the battery health condition on-line estimation is completed. The lithium ion battery health condition on-line estimation method is suitable for on-line estimationof battery health conditions.

The invention discloses an online collaborative estimation method for the charge state and the health state of a ternary lithium battery. The method improves the Thevenin equivalent circuit model, obtains the state space equation of the improved Thevenin equivalent circuit model according to the circuit relationship, further obtains the mapping relation between OCV and SOC, obtains the specific values of the polarization resistance, the polarization capacitance and the internal resistance at each sampling point by acquiring the external voltage and current values and performing identification in real time by using the FFRLS algorithm, the parameter values of the Thevenin equivalent circuit model are updated in real time according to the running state of the BMS immediately before the calculation of the SOC and the SOH each time, so that the prediction of the SOC and the SOH by the BMS is more accurate, when the method achieves high-precision estimation of the SOC and SOH for the ternary lithium battery, the external circuit is simple, only the conventional BMS is used for acquiring the voltage, current and time values, and the method has extremely strong convergence, has high robustness to the initial error value, and still adopts the iterative loop calculation method, so the sampling precision requirement of BMS can be properly reduced.

Provided is a flexible satellite attitude control method based on three-stage path planning, relating to the technical field of satellite attitude control. A flexible satellites with pyramidal CMG group as the actuator is selected as the study object, a satellite attitude control method combing the three-stage attitude path planning and rolling optimization tracking is proposed, a nonlinear state-space equation including satellite attitude dynamics, kinematics and flexible attachment vibration is established for the accurate prediction of satellite attitude future information. Based on the three-stage path planning strategy, the flexible satellite attitude maneuvering time is optimally designed, the vibration of the flexible attachments can be avoided when the satellite attitude maneuvering target is completed. By means of the nonlinear model prediction method, the attitude angular velocity curve and attitude angle position curve obtained by planning are taken as the desired tracking targets to design the rolling optimization tracking control law of the frame angular velocity of the pyramidal CMG group, therefore, the attitude wide angle rapid maneuvering control for suppressing the vibration of the flexible attachments can be realized.

The invention discloses a particle filtering-based method of reconstructing static PET images, which comprises the steps of: (1) establishing a state space equation; (2) subjecting voxels to particle sampling; (3) computing a particle weight; (4) resampling the particles; (5) computing a particle concentration truth value and a particle weight truth value; and (6) computing a to-be-estimated concentration value of each voxel. By combining the particle filtering through a state space, the data statistic features and physiological property of PET are joined up to reconstruct a PET image, thereby the resolution and acutance of the image are improved, the true PET image is well restored, at the same time, a data model of noise in the PET is defined as Poisson distribution but not Gaussian distribution, which is more suitable for the actual condition of PET scanning, therefore, noises in the reconstruction process are more effectively filtered and optimized, and obtained reconstruction results are more approximate to the actual conditions of PET compared with that obtained by ML-EM (Maximum-Likelihood and Expectation-Maximization), FBP (Filtered Back Projection) and other conventional reconstruction methods, and the reconstruction effect is more excellent.

The invention discloses a method for controlling single-phase inverters by the aid of adaptive current models in a predictive manner, and relates to inversion control on current transformers. The method includes steps of 1), repeatedly acquiring currents of alternating-current sides of the inverters and acquiring measured current values by means of mean filtering; 2), constructing an augmented state space equation by the aid of the measured current values obtained at the step 1) and unknown deviation in circuits, designing Luenberger observers and observing unknown deviation values in the circuits; 3), compensating circuit equations with the unknown deviation values obtained at the step 2), controlling the single-phase inverters by the aid of the models in the predictive manner and accurately tracking currents. The measured current values obtained at the step 1) and the unknown deviation are used as state variables. The Luenberger observers are characterized in that unknown deviation values of the models are equivalent to a slowly varying variable, accordingly, dimensions of the Luenberger observers can be reduced, and the Luenberger observers are convenient to implement. The method has the advantages that accurate current tracking can be realized, current harmonic distortion is small, current robustness is high, transient time is short, and current stability can be fast restored from disturbance.

The invention discloses an on-line parameter identification simulation method for excitation inductance of a linear traction motor. The on-line parameter identification simulation method comprises thesteps of building a state space equation taking a primary current and secondary magnetic flux as a state variable, a primary voltage as an input variable and a primary current as an output variable of the linear traction motor under static coordinate according to a mathematical model of the linear traction motor; obtaining an all-order state observer model of the linear traction motor after statereconfiguration and state variable error feedback correction; and building an on-line parameter identification model reference adaptive system of the linear motor to obtain an on-line identificationalgorithm of the excitation inductance by taking the state space model of the linear traction motor as a reference model and the all-order state observer model of the linear traction motor as an adjustable model. The on-line parameter identification simulation method can be applied to different working conditions of running of the linear traction motor, the influence of a dynamic side edge effecton the linear traction motor is fully considered, and the accurate modeling on the linear traction motor is achieved.

The invention discloses an automatic driving vehicle transverse coordination control method based on extendable priority-degree evaluating. An upper-layer controller is designed, specifically, the transverse position deviation e

of a vehicle and a road center line, the course deviation phi<r> of the vehicle and the road center line, the vehicle heading angle speed dot{phi} and the curvature rhoof the road center line are obtained and input into a PID feedback controller and a PID feedforward-feedback controller correspondingly, front wheel rotation angles delta<f1> and delta<f2> of the vehicle are output correspondingly, output values are substituted into an evaluation index prediction model, two sets of corresponding vehicle-road prediction state quantities are obtained, the vehicle-road prediction state quantities are evaluated through a priority-degree evaluating method, the result with the high priority degree is selected, and the front wheel rotation angle corresponding to theresult serves as an output value to be input into a lower-layer controller; and the lower-layer controller calculates a correlation function K(S) through the transverse position deviation e

of thecurrent vehicle and the road center line and the curvature rho of the road center line, delta<f> is coordinated through the correlation function K(S), the coordinated front wheel rotation angle is output to a vehicle-road state spatial equation, and an actual front wheel rotation angle is obtained.

The invention provides a multi-level irrigation channel automatic control method based on a model prediction control algorithm, which belongs to the field of irrigation water transmission and distribution automatic control and agricultural water resource management. The design data and operation condition data of the multi-level irrigation channel are firstly acquired, and a channel control modelis established and converted to a state space equation form; the future output of the irrigation channel water transmission and distribution system is then predicted, and according to the operation conditions, a model prediction control algorithm object function and recognition constraints are established; and finally, the optimal control quantity is obtained by optimization solution, and safe andeffective automatic control on the multi-level irrigation channel is realized. the model prediction control algorithm is designed based on the design data and the operation conditions of the multi-level irrigation channel, known water intake changes and constraints in operation of the multi-level irrigation channel can be effectively handled, the method can be used for automatic control design onthe multi-level irrigation channel, safe and reliable water supply service can be ensured, and efficient management and utilization of water resources in irrigation areas can be effectively realized.