50 results about "Nonlinear dynamic systems" patented technology

The invention discloses a strong tracking Kalman filter method for target tracking, belongs to the field of target tracking, and relates to a maneuvering target method based on a strong tracking Kalman filter. The method comprises the steps of building a disperse nonlinear dynamic system model; carrying out system initialization; carrying out time updating, and introducing a time varying and fading factor [lambda k]; measuring and updating; and finally carrying out filtering updating. According to the method, the time varying and fading factor is introduced into a Kalman filter, therefore, the method has the advantages that the Kalman filter is simple to realize and high in filter precision; meanwhile, the strong tracking Kalman filter has a real-time tracking capacity on the sudden change of the system.

The invention discloses a robot simulation learning method based on a dynamic system model. Simulation of teaching movement of the robot is achieved through learning, specifically, the demonstration motion is modeled into a nonlinear dynamic system model through a gaussian mixture model, in addition, the stability of the motion model is guaranteed through the method with the additional stability constraint conditions, the parameter learning problem of the motion model is converted into a constraint optimization problem, so that a complete description of the motion model is obtained, and finally, the motion model obtained through learning is used as a control strategy to guide the robot to imitate the teaching motion. The method is used for teaching motion of target point fixation, the method has good stability, all the generated motion trails are converged to a target point, and has good expression capacity for simple and complex teaching movement, the generalization ability of the motion model is good, a motion track which is smooth and can be converged to a target can be generated outside the teaching movement range.

A neural network learning process provides a trained network that has good generalization ability for even highly nonlinear dynamic systems, and is trained with approximations of a signal obtained, each at a different respective resolution, using wavelet transformation. Approximations are used in order from low to high. The trained neural network is used to predict values. In a preferred embodiment of the invention, the trained neural network is used in predicting network traffic patterns.

The invention discloses a threat estimation and situation assessment method based on a genetic fuzzy logic tree. The method comprises the following steps: reasoning target characteristics and attributes by adopting a fuzzy logic method to obtain a threat estimation result; inputting the threat estimation result and the environmental influence factors into a fuzzy inference device of situation assessment, performing situation assessment and obtaining a preliminary situation assessment result; and according to the real-time change of the environmental influence factors of the situation assessment, continuously optimizing the fuzzy inference device by combining a genetic algorithm, and outputting to obtain a final situation assessment result. According to the invention, a cascaded double-layer fuzzy logic tree is formed; fuzzy logic techniques are adopted, the method is suitable for processing a complex and nonlinear dynamic system which cannot be mathematically described; the method is combined with a genetic algorithm, the capability of updating a knowledge rule base is achieved, accordingly, intelligent threat estimation and situation evaluation can be realized, the computational complexity can be greatly reduced while fuzzy logic adaptive force and robust performance can be reserved, and the timeliness of threat estimation and situation evaluation can be improved.

The invention discloses a coordination control method based on the modeling of an actuator saturation multi-intelligent system and relates to the field of multi-intelligent systems. The method comprises the following steps of firstly, establishing an object subsystem mathematical model that meets the production scheduling requirements and the information flow rule constraints of the production process; secondly, establishing an information interaction network among all subsystems through statistical analysis, determining a leader system and a tracker system, and obtaining a self-adaptive coupling weight among network parameters and subsystems by utilizing relevant indexes; thirdly, respectively constructing a low-gain controller and a high-gain controller through solving out the feasible solutions of controllers; finally, applying low-high gain adaptive control laws to each subsystem to realize the tracking control of the leader system for the tracker system. According to the technical scheme of the invention, the obtained controller can be applied to various nonlinear dynamic systems and nonlinear input variables. Meanwhile, the method can also be directly applied to existing distributed control systems. Based on the method, the control cost is reduced, and the control precision is improved.

The invention provides a state estimation method for a nonlinear dynamic system under non-Gaussian noise. According to the method, a nonlinear dynamic system model comprising a process noise signal and a non-Gaussian measurement noise signal is adopted; the real-time information of the system state is estimated based on the nonlinear dynamic process model and the measurement feedback signal to obtain a state estimation signal, and output feedback is performed; and the state estimation signal is used as the controller input at the next moment, so that the influence of the non-Gaussian measurement noise signal on the system performance is weakened, and the accuracy of state estimation is improved. According to the method, the model prediction information and the non-Gaussian signal measuredby the sensor are combined, and the current optimal state of the system is estimated by adopting the newly proposed extended Kalman filtering algorithm based on the dynamic data correction technology,so that the limitation on the measurement noise distribution in the existing Kalman filtering technology is broken through; and a selectable scheme is provided for solving the state estimation problem of the nonlinear dynamic system under the condition of non-Gaussian measurement noise.

The invention proposes a data tracking based recommendation system security detection method to overcome the shortcomings of long time for user profile injection, poor attack effect, incapability of adapting to big data processing and the like in conventional collaborative filtering recommendation system detection. According to the method, a score state of a project is tracked and predicted by using a characteristic that extended Kalman filtering (EKF) can be applied to a time nonlinear dynamic system, and then users with abnormal scores in the project are subjected to clustering analysis by utilizing linear discriminant analysis (LDA), so that attack users in the project and the profiles of the users can be determined. With the adoption of the EKF method, the detection of a large amount of unrelated data is reduced, so that the detection efficiency is improved and the system robustness is enhanced. A tracking algorithm is used for recommendation system security detection and can realize online continuous system detection, so that the error detection rate is reduced. The LDA method can perform dimension reduction on multi-characteristic users, so that the profile injection attack of malicious users is effectively detected and the detection rate is increased.

The invention belongs to the field of image processing, and particularly relates to a learning-based nonlinear dynamic system deblurring method. The method comprises the steps that firstly, for an image to be deblurred, the kernel estimation energy is controlled by a learnable non-linear dynamic system; secondly, after continuous iteration of a latent image and a fuzzy kernel, great fuzzy kernel estimation is acquired; and finally, the problem of blind deblurring is transformed into the problem of non-blind deblurring, and various existing non-blind deblurring methods can be used for solving.The method has the advantages that 1 a new principle for deblurring is provided, and the learnable dynamic system instead of manually set regularization is used to control kernel estimation; 2 a new structure is designed to learn components in the dynamic system, and a suitable and flexible deblurring system is acquired through the structure; and 3 the method involves a residual network proposed recently, which brings new ideas to image processing and deep learning.

The invention provides an improved Gaussian particle filter algorithm (KLD-GPF) based on KLD, belongs to the technical field of signal processing, and relates to nonlinear filtering, and the method provided by the invention is suitable for state estimation of a nonlinear dynamic system. The algorithm can adaptively adjust the number of particles, and has a remarkable effect under the conditions that noise obeys Gaussian distribution and the statistical property of the noise is suddenly changed. According to the filtering algorithm, the Kullback-Leibler (KL) distance between a discrete probability density function (PDF) of particles and a real posterior probability density function is calculated online in the sampling process, and the size of a particle set is adjusted online according to the KL distance, so that the algorithm has relatively good robustness. The KLD-GPF can maintain a good estimation effect under the condition of sudden change of noise statistical characteristics. Compared with a KLD improved particle filtering algorithm (KLD-PF), although some filtering precision is lost, the filtering speed is greatly improved.

The invention discloses a chaotic circuit with coexistent asymmetric multi-attractors. The circuit contains two nonlinear terms described by an absolute value function and is a third-order chaotic circuit with a degraded Jerk form. The circuit is composed of a reversal, addition and integration operational module based on an operational amplifier and a resistor or a capacitor and a nonlinear operational module composed of an analog multiplier and an operational amplifier. Under a certain initial condition, the circuit can display a coexistent phenomenon, namely a multi-stability phenomenon of the asymmetric multi-attractors. As a result, a great degree of freedom is provided for engineering application of a nonlinear dynamic system, and meanwhile the circuit has important theoretical physical significance and engineering application value for research about realization of the coexistent multi-attractors and a hardware circuit comprising the coexistent multi-attractors.

The invention discloses a topological parameter hybrid optimization method for a nonlinear dynamic system structure of a high-speed light load mechanism. According to the method, a linear structure optimization iteration of a standard ESLM (equivalent static loads method) is defined into a single modification, so that a change of an inertial load caused by a structure modification is immediately reflected into an optimization model; a structure with the minimal equivalent load change is obtained; and aiming at a problem that a topological structure tending to 0 or 1 cannot be obtained by the single modification, the standard ESLM is further introduced to obtain a clear topological optimization structure, so that the hybrid optimization of the high-speed light load mechanism on the basis of a single or maximum iteration ESLM is achieved. According to the topological parameter hybrid optimization method, equivalent loads are considered into a function of a design variable; the optimization model is reconstructed; a corresponding determining method is provided; the residual amplitude can be lowered to the maximal extent under the condition of meeting optimization conditions; compared with standard equivalent static loads, the amplitude under the same motion condition can be lowered by a half; the properties of the high-speed light load mechanism are greatly improved; and different design requirements of the high-speed light load mechanism are met.

The invention discloses a nonlinear analysis method for steering stability of an electric automobile. A nonlinear dynamics model capable of describing steering of the electric automobile is established, a nonlinear differential equation is constructed, then on the basis of an adiabatic elimination principle, order reduction processing is performed on the constructed nonlinear differential equation for steering of the electric automobile, in this way, fast relaxation parameters in a system are eliminated, and a simplified nonlinear differential equation is obtained; finally, on the basis of an isoclinic line principle, a multi-step recurrence method is adopted for drawing the phase trajectory of the nonlinear dynamics system for steering of the electric automobile, the balance state, stability precision and stability of the system are visually reflected, and the stability of the electric automobile in the steering process at different speeds and turning angles is analyzed.

The invention relates to a coordination control method for a rigid multi-robot generalized system. A rigid multi-robot motion system is set up at first, a rigid multi-robot nonlinear dynamic system isestablished through a T-S fuzzy method, and model transformation is conducted under a framework of the generalized system. In view of difficult acquisition of the centroid speed of a multi-robot base, an output feedback coordination controller is further designed, and thus the multi-robot motion system works stably. Multi-robots are modeled through the T-S fuzzy method under the framework of thegeneralized system, through the designed output feedback coordination controller, the multi-robot control system can stably work, and the coordination control method has broad market application prospects.

The invention discloses a method for carrying out structural topological optimization on nonlinear dynamic systems of high-speed light-load mechanisms. According to the method, the number of linear static structure optimization iterations in an ESLM (equivalent static loads method) is restricted according to the minimum number of iterations required by the structural topological optimization, so that the modifier in the optimization is restricted, the model error caused by equivalent load is reduced, and the structural topological optimization of the high-speed light-load mechanisms on the basis of structure restricted modified ESLM. According to the method, the equivalent load is considered as a function of a design variable, the optimization model is reconstructed, and a corresponding solving method is proposed, so that the residual amplitude is reduced as much as possible while the optimization condition is satisfied, compared with the standard equivalent static load, the amplitude under the same movement condition can be reduced by a half, the performance of the high-speed light-load mechanisms can be greatly improved and different design requirements of the high-speed light-load mechanisms can be satisfied.

A system and method for efficient stochastic simulation of dynamic systems is described. Since analytic solutions cannot usually be found for stochastic differential equations, complete analysis requires numerical simulations. These simulations are most commonly done with first-order Euler-type algorithm. The efficiency of these algorithms is improved by removing algebraic loops in the simulation. An algebraic loop occurs when an output variable of the system of equations is also in an input variable to one or more of the equations describing the system. In one embodiment, the algebraic loops are removed by formulating a simulation wherein an output variable that gives rise to an algebraic loop is integrated to produce an integrated output. The integrated output is later provided to a differentiator to reconstruct the output variable as needed.

The invention discloses a high-order extended Kalman filter design method of a strong nonlinear dynamic system. The method includes, firstly, modeling a state model and a measurement model of a strong nonlinear dynamic system based on a multi-dimensional high-order polynomial; secondly, defining a high-order polynomial in the state model as a hidden variable of the system, and equivalently rewriting the state model into a pseudo-linear model based on combination of an original variable and the hidden variable; thirdly, taking the high-order hidden variables as additive parameters of each order of the system, and establishing a dimension expansion linear state model combining the state and the parameters by performing random dynamic modeling on the high-order hidden variables; then, processing the measurement model correspondingly, and modeling an original system into a dimension expansion linear measurement model based on combination of states and parameters; and finally, designing a novel high-order extended Kalman filter aiming at original state estimation based on a dimension-extended linear system. According to the invention, the validity of the new filter is verified through digital simulation.

The invention discloses a Gaussian process model-based predictive control method for a multi-variable nonlinear dynamic system model, belongs to the technical field of predictive control of the multi-variable nonlinear dynamic system model and aims at solving the technical problem of providing improvement of the Gaussian process model-based predictive control method for the multi-variable nonlinear dynamic system model. The technical scheme adopted for solving the technical problem is as follows: the predictive control method comprises the following steps of (1) building an external dynamic PLS framework; (2) predicting output data and obtaining a plurality of single-input and single-output systems in hidden space through decoupling of a dynamic GP-PLS model; (3) carrying out control by using the dynamic GP-PLS model and designing a model predictive controller in each single-input and single-output system; (4) obtaining an optimum control action through minimizing an objective function; and (5) reconstructing the model predictive control result in the hidden space back to original space and controlling the original space. The Gaussian process model-based predictive control method is applied to the multi-variable nonlinear dynamic system model.

The invention aims at providing a marine gas turbine parameter estimation and performance optimization method based on extended Kalman filtering. The marine gas turbine parameter estimation and performance optimization method comprises the following steps: establishing a three-axis gas turbine mathematical model; estimating the gas path state of the marine three-axis gas turbine by adopting an extended Kalman filtering method and utilizing the output of observable data of the marine three-axis gas turbine with the gas path fault in the operation process; adopting a Newton-Raphson iteration method to solve the pressure ratio, the flow and the efficiency characteristic of each part of the three-axis gas turbine under the variable working condition, and adopting a Runge-Kutta method to solvethe variable working condition dynamic process of the three-axis gas turbine; and establishing a performance optimization model by adopting a sequential quadratic programming algorithm, and solving anoptimal steady-state working point for keeping the output power of the marine gas turbine stable. According to the method, when the marine three-axis gas turbine has a gas path fault, the gas path health state can be accurately estimated, and parameter optimization is carried out. The method for solving the nonlinear dynamic system is wide in application range and also has reference significancewhen other systems are applied to Kalman filtering.

A method for actuating a motor including: separating feed-forward signals corresponding to motion independent components of a required actuating force / torque from motion dependent components; filtering the motion dependent components of the feed-forward signals to at least reduce high frequency signals generated due to feedback signals; and either not filtering or filtering with a low pass filter having a higher cut off frequency the motion independent components of the feed-forward signals to at least reduce higher frequency noise and components; wherein higher frequency components of electronic power amplifier signals corresponding to the motion independent components of the actuating forces / torques are not eliminated by the low pass filter, thereby ensuring that the reaction forces / torques are provided to actuate the motor.

The present invention discloses a nonlinear delay dynamic system model intelligent identification method. The method includes the following steps that: an NARX neural network model difference equation is assumed; nonlinear dynamic system delay in a set NARX neural network model is determined; the input and output order of a nonlinear dynamic system is determined; the number of hidden layer neurons of a three-layer single-output NARX neural network is determined; a three-layer NARX neural network model is determined; and finally, the validity of the three-layer NARX neural network model is verified, if the validity of the three-layer NARX neural network model is successfully verified, the method terminates, otherwise, the input and output order of the three-layer NARX neural network model is adjusted. With the method of the invention adopted, the problems of high complexity and instability which is caused by severe fluctuation of the switching process of a plurality of local linear identification methods of an existing nonlinear delay dynamic system identification method which adopts the plurality of local linear identification methods to perform identification can be solved.

The embodiments of the invention provide an autonomous underwater robot and path following control method and device and relate to the robot technology field. The method comprises the following stepsof obtaining a built autonomous underwater robot motion system; generating a nonlinear dynamic system of the autonomous underwater robot motion system according to a physics principle; receiving a real-time position acquired by the positioning module of the autonomous underwater robot, and rewriting the real-time position according to a dynamic model; and generating a nonlinear robust inverse stepcontroller based on a disturbance term of the autonomous underwater robot, the real-time position and a preset input-state stability theory, and controlling the autonomous underwater robot motion system so that the autonomous underwater robot autonomously and independently completes precise path following operation and achieves stable operation.

The invention discloses a high speed light-load mechanism nonlinear dynamic system structure evolution optimization method. The method aims at a nonlinear kinetic analysis to directly calculate sensitivity information of element strain energy and directly obtain the element strain energy at each time point, the strain energy at all the time points is superposed after normalization processing, the comprehensive sensitivity for measuring all discrete points is obtained, structure modification is performed by adopting an evolutionary structural method, then the structure is updated, the nonlinear analysis is performed again, and the process is repeated until an optimal object is realized. According to the method disclosed by the invention, the equivalent load calculation is omitted, and the change on inertial load by structural modification is directly reflected into a model to obtain a more optimal optimization result. According to the method disclosed by the invention, the equivalent static load is considered as a function of a design variable, and a corresponding calculation method is proposed, compared with a standard equivalent static load, the amplitude in the same movement condition can be lowered by half, and the performance of the high speed light-load mechanism is greatly improved.

The invention provides an industrial process operation index intelligent forecasting method, device and equipment and a storage medium. The industrial process operation index intelligent forecasting method comprises the steps of establishing an operation index dynamic model according to the characteristic that the change of an operation index depends on the dynamic characteristics of an industrial process control system, wherein the operation index dynamic model comprises an identifiable model and an unmodeled dynamic model; estimating parameters of the identifiable model in the operation index dynamic model; combining the identification error of the parameter of the identifiable model in the operation index dynamic model and the unmodeled dynamic state in the operation index dynamic model into an unknown nonlinear dynamic system; establishing an online intelligent forecasting model of the unknown nonlinear dynamic system; and obtaining a forecast value of the operation index according to output of the identifiable model in the operation index dynamic model and output of the online intelligent forecast model of the unknown nonlinear dynamic system. Aiming at the problem that industrial process operation indexes are difficult to forecast, a system identification method based on a mechanism model is combined with a deep learning method based on big data, an intelligent forecasting method of the industrial process operation indexes is provided, and the problem of forecasting of the industrial process operation indexes is solved.

The invention provides a reactive power optimization method and system based on a weak point of a power grid. The method comprises the following steps: analyzing and evaluating the dynamic stability characteristics of a power system and a basic value of a system margin through employing a nonlinear dynamic system, and obtaining a weak link of the power grid based on the basic value; calculating a PV curve after the fault aiming at the weak link of the power grid; and based on the PV curve, respectively controlling elements related to active power distribution and elements related to reactive power and voltage to optimize a voltage reactive power control scheme. The reliability, the accuracy and the precision of the power grid weak link analysis method are verified, and the reliability and the accuracy of a reactive power optimization scheme implemented for weak points under the conditions of different operation modes, different load conditions and the like are verified.

The invention provides an RTK Kalman filtering optimization method for unmanned aerial vehicles, which comprises the following steps: an acquisition terminal located on an unmanned aerial vehicle acquires a plurality of geodetic coordinates of the unmanned aerial vehicle under a WGS-84 coordinate system, a state equation and an observation equation of Kalman filtering under a nonlinear dynamic system are established, and a Kalman filtering model is established according to the state equation and the observation equation of Kalman filtering; an initial state of the nonlinear dynamic system is determined, namely, a state parameter initial value, a variance matrix initial value and a dynamic noise initial variance matrix of an initial epoch are confirmed; and based on the state equation and the observation equation of the initial epoch, the state parameter initial value, the variance matrix initial value and the dynamic noise initial variance matrix are filtered by a Kalman filtering recursion equation to obtain a filtering value. The positioning precision of unmanned aerial vehicles can be improved.

The invention discloses a modeling method for a noise-uncertainty complicated nonlinear dynamic system. The method includes the steps: 1), collecting data during industrial process to acquire data (XMN, Y); and 2), calculating noise statistical value of known input data and output data by means of Gamma Test to acquire precise information of system noise. The modeling method for the noise-uncertain complicated nonlinear dynamic system has the advantages that the best ideal point for increasing production and saving energy is searched, and optimal value of technological parameters is determined; and practical production guide is performed according to the optimized optimal value of the technological parameters.

The invention provides a fast Gaussian particle filter data fusion method based on artificial fish swarm optimization, belongs to the technical field of signal processing, and is mainly used for solving the problems of huge calculation workload and low precision of a particle filter in a multi-particle state. According to the method, Gaussian particle filtering is used as a framework, an artificial fish swarm algorithm is fused, and foraging behaviors and clustering behaviors are used for optimizing weights. According to the method, traditional sampling is replaced by linear transformation, the weight is optimized according to the measurement value and the weight calculation formula, the calculation speed is guaranteed while the calculation precision is improved, and the method is suitablefor application occasions such as state estimation of a nonlinear dynamic system.