57 results about "State space equation" patented technology

The application belongs to the technical field of industrial process control and vehicle networking cooperative control, and discloses a vehicle networking cooperative vehicle fleet self-triggered formation control method under denial of service attack conditions, which comprises the following steps: constructing a state space equation of cooperative vehicles, and obtaining expected formation states and actual states of a leading vehicle and a following vehicle; step 2, modeling possible denial of service attacks in vehicle networking; step 3, designing a self-triggered control update strategy for each following vehicle in a time interval in which communication is normal and the communication topology of the fleet remains connected; step 4, based on steps 1, 2 and 3, constructing a fleet consistency controller, and under the condition that the denial of service attack satisfies the frequency and duration constraint conditions, the fleet system meets the control requirements. The application reduces the communication and calculation burden of the fleet, improves the safety and robustness of the system under the conditions of communication attack, limited bandwidth and unstable network, and is suitable for cooperative fleet formation control scenes under various vehicle networking topological structures.

The application discloses a kind of asymptotic stability attraction domain calculation method applied to thermal power combined market, the calculation method includes the following steps: based on game theory, establish energy supplier production strategy model, terminal user optimal energy purchase model and transaction center market clearing model;According to the optimal strategy model constructed above, design dynamic market transaction mechanism and based on KKT condition and gradient method depict the optimal economic behavior model of each market subject;Based on the optimal economic behavior model established above, the state space equation of thermal power combined market is constructed;According to Lyapunov stability theorem, derive the attraction domain calculation formula.The calculation method of the application describes economic behavior based on game theory, converts economic behavior into state space equation describing market operation, and then derives the explicit expression of the attraction domain, which shows the influence of system parameters on the size of the attraction domain, can quantify the robustness of the market to maintain stable operation, and has high academic value and engineering guiding significance.

The application discloses a kind of unmanned plane's attitude control method, device, electronic equipment and storage medium, is related to unmanned plane control technical field, the method includes: according to the attitude angular velocity of unmanned plane, rotational inertia, combined external moment and rotational angular velocity tensor, obtain the attitude control equation of unmanned plane;Wherein, unmanned plane is configured with multiple oblique propeller;According to the combined external force equation of unmanned plane, combined external moment equation, power distribution equation, centroid motion equation and attitude control equation, obtain the state space equation of unmanned plane;According to the state space equation of unmanned plane, obtain the actual flight attitude of unmanned plane or obtain the adjusting speed of each oblique propeller.The technical scheme of the embodiment of the application not only realizes the detection acquisition of unmanned plane flight attitude according to the real-time speed of each oblique propeller, but also obtains the adjusting speed of each oblique propeller according to the expected flight attitude of unmanned plane, to realize the accurate control of unmanned plane flight attitude.

The application provides a negative virtualization method for a high resonance mode flexible system, first, a flexible system model is established, a dynamic feedforward compensator is introduced, and a system state space equation after feedforward compensation is given; second, a set of positive definite matrices is selected for the compensated system, and a set of matrix inequality conditions are derived, so that the compensated system has a negative virtual property, and further solving the matrix inequality conditions can obtain the parameters of the feedforward compensator; finally, an improved negative virtual controller is designed for the system after negative virtualization to ensure the stability of the closed-loop system. The method of the application ensures that the high resonance mode flexible system with non-negative virtual property can use the negative virtual theory to design the controller, and expands the application range of the negative virtual theory in the flexible system. Through the satellite model simulation experiment, it is shown that under the condition of parameter perturbation or unmodeled dynamics, the designed system can simultaneously consider the response characteristics and robustness.

The application relates to the technical field of vehicle control, and discloses a control method and system for an active front wheel steering system based on an LQR algorithm, wherein the control method estimates a vehicle mass center side slip angle in real time through a Kalman filter observer, combines a two-degree-of-freedom vehicle dynamics model to construct a state space equation, and adopts an LQR controller to calculate an optimal front wheel steering angle compensation amount; the system compares the deviation of actual and ideal yaw angular velocities and mass center side slip angles, dynamically adjusts the front wheel steering angle, and realizes high-precision trajectory tracking and stable control of the vehicle under complex dynamic working conditions. The application has the advantages of high control precision, fast response, strong anti-interference capability and low energy consumption, is suitable for improving the vehicle control stability and driving safety, and is especially suitable for intelligent driving and an advanced auxiliary driving system.

The application provides an online adaptive model prediction method for an engine based on dimension reduction Kalman filtering, which comprises the following steps: S1, obtaining a state space equation of the engine; S2, performing dimension reduction on a health parameter by using a dimension reduction matrix V to obtain a state space equation after dimension reduction of the parameter; S3, performing offline optimization on the dimension reduction matrix V by using a nonlinear least square optimization algorithm to obtain an optimal dimension reduction matrix V; and S4, estimating and predicting the health parameter of the engine and an output vector of the engine by using a dimension reduction Kalman filtering algorithm to obtain a real-time updated and optimized health parameter, and substituting the real-time updated and optimized health parameter and the optimal dimension reduction matrix V into the state space equation of the engine to obtain a state space equation with minimum dimension reduction error and adaptive adjustment of the health parameter, namely, an adaptive estimation model of the engine. The application can give the best health parameter of the variable cycle engine according to the current working state of the engine by using the dimension reduction Kalman filtering algorithm.

This invention provides an adaptive admittance method and system based on the optimal control LQR theory, including constructing an environmental dynamics model and a robotic arm admittance control model, and deriving their interactive dynamics model; defining state variables and control inputs, and establishing a standard form state-space equation; constructing a quadratic performance index function, transforming admittance parameter optimization into an LQR problem; substituting the linear quadratic regulator framework into the quadratic performance index function, transforming the LQR problem into solving the Riccati matrix differential equation, and deriving analytical expressions for virtual optimal stiffness and virtual optimal damping; within the robotic arm control sampling frequency, inputting real-time collected environmental contact forces and the actual pose of the robotic arm end effector, along with set weighting coefficients, into the analytical expressions to calculate optimal virtual stiffness and damping; and then calculating pose corrections based on the admittance control model to correct the robotic arm's motion trajectory.

This application discloses a method, apparatus, device, and storage medium for estimating the state of charge (SOC) of a battery. The method includes: establishing an equivalent circuit model of the current battery and discretizing it into a state-space equation; constructing a process noise ellipsoid and a measurement noise ellipsoid based on the noise boundedness assumption; initializing state estimation parameters, including state vectors and ellipsoid boundaries, according to the state-space equations, the process noise ellipsoid, and the measurement noise ellipsoid; performing prior estimation based on the state vector and input current from the previous moment to obtain a predicted state value for the current moment, and expanding the ellipsoid boundary; correcting the predicted state value based on the voltage measurement value at the current moment, and shrinking the expanded ellipsoid boundary; and outputting the SOC estimation interval for the current battery based on the corrected predicted state value and the shrunken ellipsoid boundary. This achieves SOC estimation under conditions where only the noise boundary is known, and by alternately expanding and shrinking the ellipsoid boundary, the feasible set converges, resulting in an estimation interval with deterministic boundaries.

The application belongs to the automatic control technical field and is based on a sliding mode control method of a fast damping approaching law; the sliding mode control method comprises the following steps: establishing a state space equation of a second-order nonlinear system; designing a sliding mode variable matrix; designing a fast damping approaching law; analyzing the fast damping approaching law characteristics; and designing a sliding mode controller based on the fast damping approaching law; the application has the beneficial effects that: through the analysis of a traditional exponential approaching law and a power approaching law, a fast damping approaching law is designed, compared with the existing approaching law, the form is simple, the number of parameters to be adjusted is small, the time for the system state variable to reach the sliding surface is further shortened, the chattering amplitude of the control signal output by the controller is further weakened, and based on the designed fast damping approaching law, a sliding mode control method is proposed, the influence of the initial position of the system on the controller is reduced, and the saturation of the controller is effectively prevented.

The application belongs to the technical field of drilling trajectory control, and specifically discloses a drilling trajectory azimuth control method with drilling pressure fluctuation suppression, which comprises the following steps: establishing a nonlinear delay differential equation describing the evolution relationship between the drilling trajectory azimuth and the bottom hole assembly azimuth; separating and modeling the drilling pressure change term in the equation as a drilling pressure uncertainty term to obtain a trajectory azimuth dynamic state equation containing the drilling pressure uncertainty term; based on the state equation, a double-loop control system containing an inner loop and an outer loop is constructed, the inner loop comprises a state observer and an angle estimator, and the outer loop comprises an inner model controller and a state feedback controller; a Lyapunov function is constructed based on the state space equation of the double-loop control system, the system robust stability condition is converted into a linear matrix inequality through stability analysis, and the gain matrix of the state observer and the state feedback controller is solved to obtain the gain matrix of the state observer and the state feedback controller. The application can effectively suppress the influence of drilling pressure fluctuation on drilling trajectory azimuth control.

The application belongs to the technical field of electric digital data processing, and particularly relates to a digital processing system for adaptive depth of a land leveler. The system acquires a tool spindle torque signal sequence and a rack three-axis acceleration signal sequence in real time, extracts a frequency domain feature vector through fast Fourier transform of a sliding window, and inputs a classifier to output a soil dynamics parameter set. The dynamics parameter set is substituted into a state space equation, a torque fluctuation variance minimization and an effective soil breaking volume maximization are taken as double objective functions, a maximum response rate of a hydraulic system and an allowable maximum structural stress are combined to construct a nonlinear programming model. A sequential quadratic programming algorithm is called to iteratively solve and output an optimal loosening depth setting value, which is converted into a hydraulic proportional valve current control sequence through trajectory interpolation. The application suppresses depth control oscillation during operation of soft and hard alternating soil, reduces transient impact load of a transmission system, and balances loosening operation power consumption and soil breaking quality.

The application discloses a two-way ground positioning method based on three-dimensional coordinate rotation compensation and dynamic filtering, relates to the technical field of navigation positioning and space measurement and control, and comprises the following steps: collecting phase differences obtained by all ground stations during one two-way ground positioning; obtaining a spacecraft state space equation according to dynamic modeling, further obtaining a discrete form of a state updating equation according to the spacecraft state space equation, and predicting a current spacecraft predicted state according to historical information by using the state updating equation; obtaining observation values of the spacecraft, correcting the current spacecraft predicted state by using the observation values, and obtaining a current spacecraft estimated state, wherein the current spacecraft estimated state comprises a spacecraft position estimated value and a spacecraft speed estimated value; obtaining a transmission delay according to the spacecraft position estimated value, correcting a spacecraft coordinate into a geocentric geodetic coordinate system at a positioning time, and obtaining a three-dimensional position and speed of the spacecraft in the geodetic coordinate system at the positioning time after the coordinate system is corrected.

This invention discloses a dual non-fragile control method for non-cooperative spacecraft based on electromagnetic capture, comprising: S1, establishing a dynamic model of the relative motion of the non-cooperative spacecraft under electromagnetic capture based on the T-H equations; S2, considering comprehensive disturbances and rewriting the dynamic model into state-space form; S3, designing a dual non-fragile observer / controller to address the coexistence of additive / multiplicative gain perturbations in the observer and controller; S4, substituting the dual non-fragile observer / controller into the state-space equations in step S2 to establish a state-space model of the closed-loop electromagnetic capture system; S5, deriving sufficient conditions for the system to be uniformly eventually bounded based on the LMI method and Lyapunov stability theorem, and solving for the observer / controller parameters in step S3. This invention fully considers the complex situation of coexistence of additive / multiplicative gain perturbations in the observer and controller, and can solve the electromagnetic capture control problem of non-cooperative spacecraft under multi-source disturbances such as external interference, elliptical orbit eccentricity, and unknown mass.

The application provides a control method for output power of a hybrid energy storage system, a nonlinear state space equation is established by a switching function model of a reconfigurable bidirectional Buck / Boost converter, and a system output power prediction model is derived. On this basis, a differential algebraic coupling model of output power and inductor current is constructed, and the power prediction problem is converted into an inductor current control problem. According to the operation characteristics of the hybrid energy storage system, a collaborative control architecture is designed, in which the super capacitor preferentially responds to transient power, and the battery dominates steady-state power supply. A time-varying reference power trajectory is generated by real-time monitoring of the system operation state and fusion of the target function curve. The model predictive control algorithm is introduced to realize the rapid tracking of the reference power in a rolling optimization manner, and the power distribution timing of different energy storage units is effectively coordinated. The application improves the reliability of the system while realizing the optimization of operation economy, and provides an innovative solution for the collaborative control of the energy storage system in the smart grid.

This application provides a method, apparatus, terminal, and medium for calculating navigation heading angle based on a combination of GNSS and IMU, including: acquiring the real-time heading angle and real-time forward speed of a mobile device at the current moment to construct a motion model of the mobile device; obtaining the state-space equation of the motion model based on the state transition matrix, velocity increment matrix, and white noise sequence; obtaining a Kalman filter measurement model based on the state-space equation of the motion model and using GNSS navigation and positioning results as observation vectors; and obtaining the optimal navigation heading angle at the current moment based on the Kalman filter measurement model, Kalman gain, GNSS navigation and positioning results, and IMU kinematic calculation results. This invention effectively controls the influence of large GNSS noise, and the use of incremental data to calculate prior values ​​also avoids the cumulative divergence phenomenon of IMU random drift errors.

This invention provides an online dynamic load identification method based on a disturbance observer. First, a structural state-space equation is established based on the mass matrix, damping matrix, and stiffness matrix of the linearly loaded structure. Then, the state-space equation of the loaded structure is compared and combined with the model equations in the control domain to design and determine the form and parameters of the disturbance observer characterized by the state-space equation parameters of the system model. This observer is then applied to the real-time online identification of external dynamic loads on the structure. By observing the difference between the actual output response and the estimated output response of the system in real time, the observer gain k is gradually adjusted until the estimated value approximates the actual input dynamic load. This method eliminates the need for complex design and extensive calculations, enabling accurate real-time identification of random dynamic loads on single-point / multi-point linearly loaded structures. It solves the problems of large solution errors and slow computation caused by matrix inversion operations in existing dynamic load identification technologies.

This invention provides an adaptive hybrid state estimation method for vehicle active safety control systems. The method includes collecting sensor data during vehicle operation, including front wheel steering angle, lateral acceleration, side acceleration, yaw rate, longitudinal velocity, and longitudinal acceleration. The sensor data is processed using a deep neural network to output a virtual estimate of the vehicle's sideslip angle and its estimation uncertainty. A state-space equation is constructed based on a three-degree-of-freedom vehicle dynamics model, and model-based state estimation is performed using the virtual estimate. The process noise covariance matrix and measurement noise covariance matrix are dynamically adjusted using an adaptive optimization strategy. The output of the deep neural network and the model-based state estimation result are fused to output a high-precision vehicle state estimate. This method achieves high-precision estimation and strong robustness of vehicle state under non-ideal conditions.

The application discloses a kind of distributed drive intelligent vehicle control method based on transverse and longitudinal coupling model prediction, comprising: obtaining the vehicle multi-source data of distributed drive intelligent vehicle by vehicle-mounted sensor;Establish the transverse and longitudinal coupling vehicle state space equation considering yaw;Design adaptive weight controller based on fuzzy logic, generate optimal weight matrix;Design transverse and longitudinal coupling model predictive controller, generate target front wheel steering angle and target longitudinal acceleration;Based on target front wheel steering angle and target longitudinal acceleration, construct transverse and longitudinal collaborative decision and torque distribution layer, integrate and coordinate output instruction, to generate the final actuator control signal, realize the control of distributed drive intelligent vehicle.The application can improve trajectory tracking accuracy and vehicle control performance.

The application provides a shield segment disease evaluation method based on a state space equation and a physical information neural network, and comprises the following steps: S1, on the basis of shield segment structure state space analysis theory, introducing disease influencing factors, establishing a shield segment structure state space equation containing diseases, and using the equation to establish a sample database of segment responses containing diseases; S2, based on the control equation, node equation and boundary constraint equation in the state space equation, constructing a shield segment disease identification physical information neural network and training the same; S3, after the training of the physical information neural network is completed, identifying the disease segment and its degree according to limited segment displacement monitoring information. The method has the dual advantages of analytical solution and machine learning algorithm, and since the core is to solve the state space equation derived based on the energy equation, the physical variables have strong interpretability, and the output result also conforms to the physical law.

The application provides an intelligent file dense shelf system based on digital twinning and a prediction control algorithm, relates to the technical field of data processing, and realizes accurate mapping and dynamic monitoring of the running state of the shelf body by constructing a digital twinning model containing structural parameters, kinematics, energy consumption and safety constraints, and generating a state vector reflecting the real-time running state of the physical shelf body by using multi-source data fusion; on the basis, a state space equation is constructed based on the state vector, energy consumption prediction and safety constraints by the prediction control algorithm layer, a model prediction control algorithm is used to solve the constrained quadratic programming problem by rolling optimization, so that the system can predict the future running trend, generate an optimal control sequence under the premise of meeting multiple safety constraints such as speed, acceleration, shelf distance and the like, and issue the first control quantity for execution, thereby converting the traditional passive feedback control into active prediction and rolling optimization control, and significantly improving the stability and safety of the shelf body movement.

The application discloses a ball and link system control method based on fuzzy sliding mode and periodic event triggering, first constructs a state space equation of the system and carries out regular transformation to obtain a standard system model; then, a fuzzy sliding mode switching function is established in combination with a fuzzy membership function, and a periodic event triggering mechanism is designed; on the basis, a periodic event triggering fuzzy sliding mode controller is constructed, an actual sliding mode band is constructed by analyzing a periodic sampling error, state trajectory is ensured to enter the band in a limited time and remain in the band, and the reachability of the sliding mode surface is ensured; then, a controller gain matrix is solved based on stability analysis of the sliding mode, is brought into the controller to complete closed-loop control on the ball and link system, and a system sampling period is established. The application uses the fuzzy membership function to design the sliding mode surface, has lower conservativeness, and uses the periodic sampling to design the event triggering mechanism, theoretically eliminates the Zeno phenomenon, and provides an efficient control scheme for a system with limited network and communication resources.

This invention relates to the field of power battery monitoring technology, and specifically to a real-time streaming analysis method for power battery data based on Kalman filtering. The process includes the following steps: S1. Acquire real-time operating data of the battery, including cell voltage, current, temperature, and timestamp; S2. Construct the state-space equation of the battery based on a dual RC equivalent circuit model, and use the variable forgetting factor recursive least squares method to identify the model parameters online and update the coefficient matrix of the state-space equation in real time; S3. Set the initial parameters of the sunflower optimization algorithm, using the mean square error between the SOC estimate and the true value as the fitness function, and use the sunflower optimization algorithm to perform offline global optimization on the process noise covariance matrix Q and measurement noise covariance matrix R of the extended Kalman filter to obtain the optimal Q matrix and R matrix; S4. Extend the optimal Q matrix and R matrix obtained from the offline optimization into the Kalman filter, and perform SOC prediction based on the current current and historical state during vehicle operation, and perform Kalman filter correction in combination with the measured terminal voltage to output the real-time SOC estimate; S5. Based on the real-time SOC estimate and operating data, generate analysis results for range prediction, thermal management, and safety warning.

This invention relates to a method, apparatus, device, and medium for modeling the dynamics of electromechanical coupled gears. The method involves: determining the bearing type, motor parameters, and gear parameters in the gear system; based on the determined bearing type, motor parameters, and gear parameters, calculating the time-varying contact stiffness of each bearing, the time-varying meshing stiffness of each gear stage, the motor torque, and the unbalanced magnetic pull of the motor rotor; constructing the motor rotor dynamics equation and the electromechanical coupled gear dynamics equation; establishing a state-space equation by simultaneously solving the motor rotor dynamics equation and the electromechanical coupled gear dynamics equation; and solving the state-space equation to obtain the gear vibration displacement. The electromechanical coupled gear dynamics modeling method of this invention can more accurately calculate the output vibration displacement and vibration acceleration of the gear system in an electric drive assembly, thereby obtaining dynamic response characteristics of the gear system in an electric drive assembly that are more consistent with reality.

This invention belongs to the field of transformers and discloses a broadband electromagnetic transient modeling method, system, device, and storage medium, including the following processes: Based on the transformer's geometric dimensions and magnetization characteristics, a frequency-varying magnetoresistive topology network is established; the broadband equivalent capacitance matrix and part of the inductance matrix of the transformer windings are extracted and integrated to construct a broadband admittance matrix of the windings; the geometric steady-state resistance of the plasma arc column is calculated, and a dynamic differential equation for the time-varying arc resistance of the plasma arc column is established; an augmented state-space equation describing the dynamic evolution of the transformer before and after a fault is constructed; the above are integrated to construct a time-domain multiphysics joint solution model, and the time-domain multiphysics joint solution model is verified by time-domain joint solution and frequency-domain impedance scanning to obtain a broadband electromagnetic transient model. This enables accurate simulation of complex broadband transient processes inside the transformer.

The application provides a robust multi-fault synchronous reconstruction method for an uncertain quad-rotor unmanned aerial vehicle under external disturbance, and relates to the technical field of quad-rotor unmanned aerial vehicles. First, a dynamic model of the quad-rotor unmanned aerial vehicle is established, and is converted into a T-S fuzzy state space equation to construct an augmented system containing system states, process faults and sensor faults; for the obtained augmented system, a controllable order fuzzy observer is designed, and an error system is derived; the parameter matrix of the controllable order fuzzy observer is solved, and the stability of the error system is analyzed; based on the controllable order fuzzy observer, synchronous reconstruction of multi-faults of the quad-rotor unmanned aerial vehicle is realized. The nonlinear model of the quad-rotor unmanned aerial vehicle is approximated through T-S fuzzy technology, a controllable order fuzzy observer is designed, synchronous accurate reconstruction of multiple faults, effective suppression of external interference and complete decoupling of unknown inputs are realized, and the order of the observer can be flexibly adjusted according to actual requirements.

The application provides a permanent magnet synchronous motor active disturbance rejection control method and controller based on a cascade observer and an exponential approach law, and belongs to the technical field of motor control. The method comprises the following steps: constructing a state space equation based on real-time operation state data; constructing a cascade linear extended state observer; using a front-stage reduced-order extended state observer to obtain a rough estimated value of total disturbance of the system and injecting the rough estimated value into a rear-stage linear extended state observer to track residual disturbance and obtain an accurate observation value of the total disturbance of the system; constructing a comprehensive error variable, performing nonlinear compensation adjustment, and outputting a feedback control amount; taking the accurate observation value of the total disturbance of the system as a feedforward compensation, combining the feedback control amount, obtaining a final control input amount, and applying the final control input amount to a permanent magnet synchronous motor driving system. The application shortens a speed step response by 40% without overshoot, shortens an anti-disturbance recovery time by 82%, and improves dynamic response performance and control accuracy of the motor driving system.

The application belongs to the technical field of magnetic interference compensation coefficient estimation, and specifically discloses a kind of estimation method of magnetic interference compensation coefficient, comprising the following steps: obtaining the magnetic field measurement data and attitude measurement data when carrier motion;Geomagnetic vector measurement model is constructed and is rewritten as linear regression form, state space equation with compensation coefficient as state quantity is established, and single-time joint observation matrix and measurement vector are constructed according to state space equation;Regularization objective function with prior mean is constructed, the prior estimation value of compensation coefficient is obtained by solving ridge regression problem, and prior error covariance matrix is constructed according to training residual and information matrix;Prior estimation value and prior error covariance matrix are used as the initial state quantity and initial error covariance matrix of Kalman filtering, the prediction and update steps are executed, and the posterior estimation value of compensation coefficient is iteratively calculated.The application can effectively improve the robustness and compensation accuracy of the magnetic interference compensation method under different working conditions.

The application discloses a zero-sum game self-triggered anti-disturbance reinforcement learning method and device for unmanned surface vehicle, and relates to the technical field of autonomous control of marine unmanned system. The method comprises the following steps: constructing a state space equation of the unmanned surface vehicle; defining the control input and the disturbance input as target opposite game players, constructing an augmented state of the unmanned surface vehicle, constructing an HJI equation, and obtaining a Nash feedback strategy of both parties; designing a self-triggered sampling mechanism, and calculating a next trigger time; constructing an evaluation network, an execution network and a disturbance network; constructing an evaluation network target function based on an experience replay mechanism, and designing a weight update rate of the evaluation network; taking the strategy structure induced by the evaluation network as a reference, constructing a target function of the execution network and the disturbance network; constructing a time projection operator, and constructing a weight update rate of the execution network and the disturbance network; and outputting an approximate Nash equilibrium strategy of the execution network and the disturbance network. By using the application, online solution can be realized, the calculation cost is low, and the redundancy of on-board calculation and communication is reduced.