39 results about "State space matrices" patented technology

The invention discloses a controller parameter setting method applied to high-voltage direct-current power transmission, a medium and equipment. The method comprises the following steps: firstly, acquiring network parameters and controller parameters of a high-voltage direct-current power transmission system; subdividing the system into sub-modules and describing the sub-modules by using a differential equation and an algebraic equation to obtain a state space model; linearizing the state space model to obtain a state space matrix A, an input matrix B and a small interference dynamic model; calculating characteristic values of the matrix A under different parameter values; screening the characteristic values, and determining a stability domain of the controller parameters; converting the small-interference dynamic model into a transfer function model, calculating a transfer function of a constant current controller or a constant voltage controller, and obtaining a unit step response ofthe transfer function; and calculating a dynamic time domain index according to the unit step response curve, and obtaining an optimal controller parameter according to an ITAE criterion. The methodcan effectively improve the operation characteristics of the high-voltage direct-current power transmission system, and improves the small-interference stability of the system.

The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used.

The invention relates to a method for estimating the risk of jellyfish injury, which includes: (1) storing the spatial position of the tourist with a state matrix hp, at the central position (i, j) of the tourist, hp (i, j)=1, and the rest The position is hp=0 (2) calculate the expected number of jellyfish in the research area; (3) use the state matrix fp to store the spatial position of the jellyfish, the grid (i,j) where the jellyfish appears, then fp(i,j)=1 ; The grid (i, j) where jellyfish does not appear, then fp(i, j) = 0; (4) Determine the local rules; (5) Determine the direction of the water flow and wind speed in the research area to point to the shore; (6) Determine the tourists The active area is the observation area H; (7) determine the update time; (8) determine the asynchronous update; (9) implement the algorithm in (1)-(8) based on MATLAB or Fortran, the frequency of jellyfish wounding is close to that of jellyfish (10) Give early warning instructions to bathing places according to early warning standards. The method of the invention can simulate the probability of jellyfish wounding, evaluate the risk of jellyfish wounding, and give bathing places early warning instructions according to early warning standards, thereby rationally managing bathing places and greatly reducing the probability of jellyfish wounding people.

The invention discloses a numerical-control machine tool feeding system modeling method based on an improved SVD (singular value decomposition)-Krylov algorithm. The method includes creating a state space equation model of a numerical-control machine tool feeding system according to a kinetic equation; acquiring an original system state space matrix, an original system and a transfer function model; setting the order of an order reduction system, and starting a multipoint moment matching SVD-Krylov algorithm for order reduction; outputting a state space matrix of the order reduction system, the order reduction system and a corresponding order reduction transfer function model; performing order reduction algorithm simulation verification according to a orthogonal experiment method and a time response method. Asymptotic stability of the model subjected to order reduction based on an order reducing and modeling algorithm can be guaranteed, and calculation efficiency is high; an iterative algorithm is adopted, moment matching quantity is multiplied, and order reduction accuracy is improved. The numerical-control machine tool feeding system modeling method has the advantages that order reduction accuracy and calculation efficiency are improved remarkably, and modeling and simulating speeds of the numerical-control machine tool feeding system are increased greatly.

The invention provides a weak and small target detection method under the sea clutter background based on the "multi-scale directed Lyapunov index". Hilbert transformation is performed on the radar echo data and the instantaneous phase angle theta is solved, first-order and second-order differential theta<.> and theta<..> are calculated and a state space matrix [theta, theta<.>, theta<..>] is constructed; and the state space is divided into sub-matrixes having no mutual intersection according to a certain scale, the covariance matrix of each sub-matrix is solved and eigenvalue decomposition isperformed, the main eigenvalue sigma<ip> and the eigenvector v<ip> are extracted, the inclined angle psi between the adjacent covariance matrix main eigenvectors is calculated, and (sigma<ip>, psi) is defined as the directed Lyapunov index of the radar echo data. The (sigma<ip>, psi) obtained according to different division length forms the multi-scale directed Lyapunov index, and the weak and small target under the sea clutter background can be accurately detected by the fluctuation characteristics of the index.