13 results about "Time-variant system" patented technology

The invention discloses a rapid calculation method and device for the sensitivity of a linear periodic time-varying system based on a matrix sparse technology, and belongs to the technical field of modeling analysis of the linear periodic time-varying system.The rapid calculation method comprises the steps that an LTP system is converted into an equivalent LTI system, and the eigenvalue, the left eigenvector and the right eigenvector of the LTP system are obtained; based on a functional derivative method, obtaining a sensitivity analytical expression of the LTP system characteristic value to the parameter; according to the influence mechanism of the parameters on the system matrix, obtaining an analysis form of the influence of the parameters on the LTP system matrix in the sensitivity analysis expression; and converting a partial derivative of a system matrix to a steady-state trajectory into sparse calculation of a vector field Hessian matrix by using a matrix sparse technology, thereby obtaining a calculation result of the characteristic value sensitivity of the LTP system. According to the method, the sparse characteristic of the system matrix in the PEPS is utilized, the calculated amount and the storage requirement of the system matrix on trajectory partial derivative solving are reduced, and therefore calculation of the LTP characteristic value sensitivity is accelerated.

The invention discloses a parameter time-varying characteristic considered LTP system characteristic value sensitivity analysis method for power system stability analysis, and belongs to the field of linear period time-varying system analysis control. According to the method, the influence relation of the change of a time-varying parameter alpha (t) on a dominant characteristic value lambda i is analyzed by utilizing the partial derivative of the dominant characteristic value lambda i of the power system to the time-varying parameter alpha (t), and the size and direction of the influence of the change of the time-varying parameter alpha (t) on the dominant characteristic value lambda i are quantified according to the actual time-varying parameter change. Analyzing and depicting the influence degree of changes of time-varying operation parameters, time-varying control parameters and the like on characteristic values in the power system, so as to realize stable analysis and control of planning, scheduling optimization, parameter optimization, controller design and the like of the power system; wherein the functional relationship between the LTP system matrix and the time-varying parameter is considered, and the functional derivative of the system matrix to the time-varying parameter is adopted to represent the partial derivative of the system matrix to the time-varying parameter.

The invention discloses a self-tuning delay method and system for suspension load vibration control, and relates to the field of mechanical structure vibration control. According to the method, a parameter observation function is introduced into a self-tuning delay control algorithm, so that the delay parameter can be adaptively adjusted in real time according to the changing rope length, and the control algorithm can still keep robust and effective vibration suppression performance under the condition that the system parameter (such as the rope length) is continuously changed; the problem of performance degradation of a traditional control method in a time-varying system is solved; in the core time lag parameter design of the self-tuning time lag control algorithm, the inherent closed-loop time lag of the system is directly integrated, and the use of a traditional time lag compensator (such as a Smith predictor and model predictive control) which is complicated and depends on a high-precision model is avoided, so that a control loop is basically not influenced by the system feedback time lag, and the control precision is improved. And an effective and simple solution is provided.

The invention discloses a data driving control method based on a sliding window and feature point selection in a time-varying scene, and relates to the technical field of adaptive control of data driving, and the method comprises the steps: collecting input and output signals of a time-varying system in real time through a sensor; dynamically updating an input / output track data structure based on a sliding window mechanism to ensure that the data structure always reflects the current dynamic characteristics of the system; feature point selection is carried out on the trajectory data structure through singular value decomposition, data information is quantized, dimension reduction processing is carried out, and a dimension reduction data structure is generated; constructing a control optimization objective function based on the dimension reduction data structure, solving a decision variable and generating a control instruction; and balancing the data structure dimension and the information completeness through an adaptive order adjustment mechanism. According to the method, the data is updated in real time through a sliding window mechanism, and the data dimension is selected and optimized based on the feature points of singular value decomposition, so that the timeliness, the calculation efficiency and the control precision of time-varying system data driving control are remarkably improved.

The embodiments are directed to an inferential sensing system, methods and computer program product of an estimator for estimating parameters of complex nonlinear time-varying systems from scarce system output measurements. The estimator comprises a two-step process to accurately estimate the time-varying parameters of the time-varying system based on the input and output sample of the time-varying system. First, multiple filters in the high frequency processing loop, operating independently and concurrently process the input and output samples of the time-varying system to generate a hypersurface comprising time series objects. Each filter is restricted to adapt only a subset of the modeled time-varying parameters. The hypersurface comprising the time series objects is aggregated over several iterations of the high frequency processing loop. Second, the hypersurface is passed through a neural network in the low frequency processing loop to infer estimates of the time-varying system parameters.

The application discloses a kind of four-rotor hanging transport control methods based on double time-varying interference estimator, comprising the following steps: S1, the dynamics model of four-rotor unmanned aerial vehicle and hanging load is established;S2, the model is simplified using feedback linearization technique, and the nonlinear coupling system is decoupled into two subsystems of anti-swing and trajectory tracking;S3, according to the swing model of the goods being hung, time-varying uncertainty and interference estimator are constructed in the anti-swing controller, and time-varying interference is dynamically aggregated to eliminate the swing of heavy object;S4, according to the kinematics model of four-rotor, time-varying uncertainty and interference estimator are constructed in the trajectory tracking controller, to realize the accurate trajectory tracking of four-rotor unmanned aerial vehicle;S5, the stability of estimation error system is proved using linear time-varying system theory, and the interference estimation performance is optimized by adjusting auxiliary parameter;S6, the stability of anti-swing subsystem and trajectory tracking subsystem is proved, and the interference estimation performance is optimized by adjusting auxiliary parameter.

The embodiments are directed to an inferential sensing system, methods and computer program product of an estimator for estimating parameters of complex nonlinear time-varying systems from scarce system output measurements. The estimator comprises a two-step process to accurately estimate the time-varying parameters of the time-varying system based on the input and output sample of the time-varying system. First, multiple filters in the high frequency processing loop, operating independently and concurrently process the input and output samples of the time-varying system to generate a hypersurface comprising time series objects. Each filter is restricted to adapt only a subset of the modeled time-varying parameters. The hypersurface comprising the time series objects is aggregated over several iterations of the high frequency processing loop. Second, the hypersurface is passed through a neural network in the low frequency processing loop to infer estimates of the time-varying system parameters.

This invention proposes a method and system for obtaining the trajectory of a periodic time-varying system based on Chebyshev placement, belonging to the field of power electronics technology. The method includes: establishing a periodic boundary value problem model of the periodic time-varying system, determining the system period and the number of placement points; performing time-scale mapping on a single-period interval, selecting Chebyshev placement points and constructing a spectral differential matrix; applying system dynamic constraints at each placement point, discretizing the differential equations into a system of nonlinear implicit algebraic equations; introducing periodic closure constraints to form a closed nonlinear algebraic equation system; and using an internal and external iterative strategy to solve the closed equation system, outputting the steady-state periodic discrete trajectory. This invention, by placing Chebyshev points within a single-period interval and constructing a spectral differential matrix, directly transforms the differential equations into a system of algebraic equations, avoiding long time-domain integration, and possesses advantages such as high computational efficiency, fast convergence speed, good numerical stability, and strong engineering feasibility.

The embodiments are directed to an inferential sensing system, methods and computer program product of an estimator for estimating parameters of complex nonlinear time-varying systems from scarce system output measurements. The estimator comprises a two-step process to accurately estimate the time-varying parameters of the time-varying system based on the input and output sample of the time-varying system. First, multiple filters in the high frequency processing loop, operating independently and concurrently process the input and output samples of the time-varying system to generate a hypersurface comprising time series objects. Each filter is restricted to adapt only a subset of the modeled time-varying parameters. The hypersurface comprising the time series objects is aggregated over several iterations of the high frequency processing loop. Second, the hypersurface is passed through a neural network in the low frequency processing loop to infer estimates of the time-varying system parameters.

The application discloses a dynamic stability analysis control method, device and system of a linear periodic time-varying system, and belongs to the technical field of system stability analysis. i The partial derivative of the concerned parameter alpha with respect to the dominant eigenvalue lambda i The sensitivity of the concerned variable alpha to the dominant eigenvalue of the linear periodic time-varying system is analyzed, the influence size and direction of the system control concerned parameter on the dominant eigenvalue can be accurately described, the technical defects that the classical dominant eigenvalue sensitivity analysis method is only applicable to linear time-invariant systems are made up, and theoretical support is provided for the dominant eigenvalue sensitivity analysis of the linear periodic time-varying system. In addition, the system matrix with complex time-varying characteristics does not need to be time-invariant, and the operation is simpler.

The invention discloses a left feature vector calculation method, system and device suitable for an LTP system, and a storage medium. The method comprises the following steps: deducing based on a Floquet theory to obtain a time domain expression of a left feature vector of a linear periodic time-varying system; converting the left feature vector of the linear periodic time-varying system of the time domain into a frequency domain, and establishing a relation between the left feature vector of the linear periodic time-varying system of the frequency domain and the left feature vector of the harmonic state space; calculating a harmonic state space left feature vector, and converting the harmonic state space left feature vector to a time domain to obtain a time domain linear periodic time-varying system left feature vector; the method does not need to depend on the inversion of the ill-conditioned STM, avoids the demand of the inversion of the ill-conditioned STM, obtains the more reliable LTP left feature vector through calculation, can improve the solving reliability of the LTP left feature vector, and lays a foundation for the modal analysis of an LTP system.

The invention discloses a digital time-varying convolution method for time-varying system analysis. The digital time-varying convolution method comprises the following steps: setting an estimated state according to application requirements; according to different pre-estimation states, pre-processing an input signal to obtain a digital input sequence; finding a sampling moment index value corresponding to an adjacent time-varying stage of any input moment index n; calculating a real-time time-varying coefficient at the nth moment according to the sampling moment index value of the adjacent time-varying stage at the nth moment and the corresponding stage time-varying coefficient; and calculating a time-varying system output value at the nth moment according to the real-time time-varying coefficient at the nth moment. The calculated time-varying coefficient and the time-varying coefficient of the adjacent different time-varying stages have a continuous change relation in time, and the obtained output result also has good continuity and is more in line with the response result of a real time-varying system.

The application belongs to the technical field of system analysis, and discloses a participation factor analysis method and device suitable for a piecewise linear periodic time-varying system, which comprises the following steps: reconstructing a nonlinear periodic time-varying system with switching behavior into a piecewise linear periodic time-varying system, solving the eigenvalues, and solving the eigenvectors based on the eigenvalues; when solving the eigenvectors, the eigenvectors of the current continuous interval are solved first, then the end eigenvectors of the current continuous interval are made to pass through the switching time based on the jump matrix to obtain the eigenvectors at the start of the next continuous interval, the eigenvectors of the next continuous interval are solved, and the above steps are sequentially repeated until the eigenvectors in one period are obtained, and then the participation factors are calculated through integration. The above analysis method can accurately calculate the participation factors of the piecewise linear periodic time-varying system, and the obtained participation factors can be used to analyze the deep reasons for system instability.