519 results about "Linearization" patented technology

The invention relates to the technical field of power system automation, in particular to a climbing-frequency modulation coordinated optimization method and system considering energy storage capacity uncertainty, and the method comprises the steps: building an opportunity constraint model of an energy storage system based on the capacity uncertainty, and enabling the opportunity constraint model to be used for describing a reliable feasible region under the deviation of energy storage electric quantity; establishing an energy storage system linearization model considering a climbing-frequency modulation power unified constraint, and realizing power coupling and sharing constraint description of two types of adjustment services; aiming at the nonlinear characteristic of the gradeability of the thermal power generating unit, establishing a piecewise linearization model of the gradeability by adopting a piecewise linearization method; and establishing a thermal power-energy storage combined climbing-frequency modulation coordinated optimization model, and solving the optimization model by adopting a quadratic programming method to obtain the climbing power and frequency modulation reserve capacity of the thermal power generating unit and energy storage. According to the method, the actual available range of the energy storage system capacity can be dynamically reflected, the electric quantity out-of-limit risk caused by the uncertainty of the energy storage capacity is avoided, and the reliability of the scheduling result is improved.

The invention provides a bridge pier nonlinear earthquake random response analysis method based on a BWBN model, and belongs to the field of civil engineering structure random vibration analys.The method comprises the steps that discretization processing is conducted on a concrete bridge pier structure, and a finite element nonlinear analysis numerical model is constructed; constructing a BWBN model by combining linear and nonlinear differential equations, setting a hysteresis parameter value of the BWBN model, simulating by adopting an Euler-Bernoulli beam unit, and establishing a degraded beam finite element model; obtaining a unit hysteresis vector, constructing a system motion balance equation, solving an equivalent linearization coefficient, constructing a covariance matrix of random response of the concrete pier structure, performing random response analysis on the concrete pier structure, and exploring typical working conditions of random excitation. According to the method, the operation time is shortened, the calculation precision is improved, and meanwhile, the response characteristics of the reinforced concrete pier structure under nonlinear earthquake random excitation can be truly represented.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention discloses an application method of a deep learning model embedded based on a physical mechanism in urban flood ponding point identification. The method comprises the following steps: collecting and preprocessing basic data of a research area; taking the preprocessed basic data as input, and performing deformation processing, discretization and linearization processing by taking a Saint-Venant equation set as a hydrodynamic equation set; constructing a differentiable physical module based on the discretized deformation hydrodynamic equation set; on the basis of a differentiable physical module, a deep learning model is constructed, and the deep learning model comprises a backbone network, the differentiable physical module, an attention guidance fusion module, an iteration correction mechanism and a mixed loss function; pre-training the deep learning model, combining fine tuning training and constraint strengthening training, and adding adversarial training in the training process to simulate an extreme scene; and urban flood ponding point identification is carried out based on the trained deep learning model. According to the invention, high-precision real-time prediction of urban flood simulation is realized.

A robot kinetic parameter identification method based on double-layer iteration and friction compensation comprises the following steps: S1, establishing a kinetic model of a robot, and performing linearization processing on the kinetic model to obtain a linearization model represented by an observation matrix and an inertial parameter vector; s2, designing an excitation trajectory for the linearized model by adopting improved Fourier series of a quintic polynomial, and setting constraint conditions of joint positions, speeds and accelerated speeds; s3, the robot is controlled to move according to the excitation track, and joint state data of the robot are collected and subjected to noise reduction processing; and defining the noise-reduced driving torque as a measurement torque. According to the robot kinetic parameter identification method based on double-layer iteration and friction compensation, the physical feasibility of the robot kinetic parameters can be ensured, the friction model is improved to identify the friction parameters, the friction model is fitted by adopting the radial basis function neural network, and the precision of subsequent robot control is ensured.

The invention relates to the technical field of underwater robot cooperative control, and provides an elastic consistency control method and system of a multi-underwater robot system. The elastic consistency control method for the multi-AUV system comprises the following steps: discretizing and linearizing the multi-AUV system, introducing a performance index to describe the influence of communication topology on the dynamic behavior of the system, and calculating the elastic consistency of the multi-AUV system on the basis of a centralized optimal control method of a local decoupling system and a robust stability theory. And designing an elastic consistency controller and obtaining controller gain by solving a local algebraic Riccati equation so as to realize consistency control of multiple AUV system states. According to a sparse mode matrix spectrum of a distributed controller, a system stability condition is given, and efficient cooperation and stable operation of a multi-AUV system in a complex underwater environment are ensured. According to the method, the calculation and communication complexity is remarkably reduced, the method is suitable for a multi-AUV cooperative working environment, and a new technical solution is provided for cooperative control of the underwater unmanned system.

The invention belongs to the field of shield tunneling machine deviation rectification track planning, and discloses a shield tunneling machine deviation rectification track high-precision autonomous planning method and system, and the method comprises the steps: determining the minimum turning radius of a shield tunneling machine; carrying out coordinate calculation on the DTA to obtain the coordinate, the direction and the curvature of each point; projecting the starting point state and the end point state of the shield tunneling machine under the Cartesian coordinate system into the established Frenet coordinate system, establishing a correction curve parameterization equation based on a quintic polynomial line type, and converting the state of the shield tunneling machine into the Cartesian coordinate system; through segment pre-typesetting constraint, determining a deviation correction curve end point feasible region; the deviation degree of the deviation correction curve and the DTA and the length and smoothness of the deviation correction curve serve as optimization parameters, normalization processing is carried out according to the threshold value of each target, a comprehensive cost function is established, an improved particle swarm optimization algorithm is used for solving, and a global optimal deviation correction track is output. According to the method, real-time autonomous high-precision deviation rectification track planning of the shield tunneling machine can be realized, the track feasibility is improved, the pose deviation degree is reduced, and the tunnel quality and the construction efficiency are improved.

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

The ELM-based frequency safety constraint linearization optimization scheduling method comprises the following steps: establishing a fire-wind-light multi-energy complementary frequency dynamic response model, and deducing a time domain analytical expression of a frequency safety index by taking a frequency maximum change rate and a frequency maximum deviation as the frequency safety index of a system; considering a frequency lowest point security constraint and a frequency maximum change rate security constraint, and establishing a frequency security constraint-containing optimization scheduling model; a maximum bearable disturbance power linearization mapping method based on ELM is provided, nonlinear frequency security constraints are converted into a series of linear constraint sets, and linearization processing of the frequency security constraints is achieved; and piecewise linearization is carried out on quadratic terms in the established objective function containing the frequency security constraint optimization scheduling model, the overall model is converted into a mixed integer linear model, and a Gurobi solver is used to solve the linearly processed model. The scheduling method can ensure that the system frequency is safe and stable, so that the established optimal scheduling model gives consideration to both economy and frequency safety.

The invention relates to the technical field of power system operation and control, and discloses an improved model-free adaptive flexible low-frequency power transmission oscillation suppression method, which performs zero-phase lag filtering on noisy signals through a nonlinear tracking differentiator, effectively solves the problem of control divergence caused by signal noise, and improves the suppression efficiency. A tight-format dynamic linearization and linear expansion state observer technology is adopted, accurate capture of dynamic characteristics of a strong nonlinear system and real-time observation and compensation of internal and external full-dimensional disturbance are achieved without dependence of a physical model, and particularly, an adaptive estimation algorithm of an output variable quantity penalty factor is introduced. The problem that model parameter identification jumps severely under the broadband oscillation working condition is solved, the robustness of a control system is enhanced, and the self-adaptive optimality of a control strategy is guaranteed and interference of long-term online operation on steady-state performance is avoided by combining parameter optimization of a cuckoo search algorithm and an event triggering mechanism of time-frequency double criteria.

The invention relates to the technical field of aircraft path planning, and particularly provides an aircraft path planning method for a space non-cooperative target. The method comprises the following steps: constructing an aircraft dynamics model and constraint conditions, and obtaining a non-convex optimal control problem; performing discretization processing on a non-convex optimal control problem through a non-equal-interval pseudo-spectrum discrete optimization model, and converting the non-convex optimal control problem into a fixed-time nonlinear programming problem about discrete nodes; according to the method, a fixed time nonlinear programming problem is convexed to form a convex sub problem, a local optimal solution of a non-convex optimal control problem is obtained through sequence iteration, under the multi-state constraint, path planning of an aircraft approaching a space non-cooperative target is achieved, the problem of no feasible solution caused by non-accurate linearization is solved, and the method is suitable for being applied to a non-cooperative target. And the convergence rate of the algorithm is accelerated.

The invention discloses an aircraft active side lever force feedback control method based on feedback linearization. The method is suitable for pitch axial force feedback control and roll axial force feedback control of an aircraft active side lever. The method comprises the following steps: establishing a kinetic equation of a mechanical transmission system and a pitch axis motor or a roll axis motor, and establishing a state-space equation of an active side lever system according to the equation; designing a linear expansion observer, and estimating the thrust applied to the active side lever by the driver based on the linear expansion observer; and designing a feedback linearization control method, deducing a control law according to a state-space equation of the active side lever system, and introducing the estimated thrust applied to the active side lever by the driver into the control law to realize real-time compensation of external interference. Under the condition that the thrust of the driver cannot be measured, the thrust of the driver is obtained through the linear expansion observer, the thrust of the driver is compensated by designing the control law, and therefore the dynamic performance and the steady-state performance of the servo system are improved.

The invention discloses a deep neural network probabilistic load flow calculation method and system, and belongs to the field of power system analysis. The method comprises the following steps: acquiring power system parameters and confidence levels, and determining state variables and orders; establishing and linearizing a nonlinear power flow equation to obtain a Jacobian matrix; a node voltage vector is used as input, branch admittance is used as physical information to be embedded into a deep neural network model based on a multi-head self-attention mechanism and semi-invariant guidance, a Jacobian matrix is solved, and a sensitivity matrix is calculated; calculating and aggregating semi-invariants, and transmitting the semi-invariants to a state variable; and after standardization, obtaining a distribution function by using a six-order Cornish-Fisher series, and calculating a fluctuation interval in combination with a confidence level under opportunity constraint to realize quantitative evaluation. According to the method, the probability load flow calculation complexity is reduced, the limitation of a traditional method in processing the network topology complexity problem is solved, and quantitative evaluation of the probability load flow is realized.

The invention provides a transient stability analysis method for a grid-connected system. The method comprises the following steps: establishing a mathematical model of a heterogeneous new energy grid-connected system comprising a network construction type new energy unit, a network following type new energy unit, a synchronous generator and a receiving end power grid, and defining a state variable set of the system; solving a stable equilibrium point and an unstable equilibrium point of the system according to the operation condition of the system; and by taking the unstable equilibrium point as a starting point, calculating by adopting a numerical integration method or a numerical continuation method to obtain a stable manifold starting from the unstable equilibrium point. The method provided by the invention realizes global and accurate depiction of the transient stability domain, overcomes the defects that a traditional small signal linearization method can only analyze local stability and a traditional energy function method depends on an artificial construction function and is limited in applicability, fundamentally changes an analysis normal form of the transient stability domain, and improves the analysis accuracy of the transient stability domain. And a more effective analysis tool is provided for safe and stable operation of the high-permeability new energy grid-connected system.

The invention discloses an EMB system modeling and estimation integration method and device based on a Koopman operator, and relates to the technical field of automatic driving and intelligent chassis, and the method comprises the steps: obtaining an EMB system input and output offline data set, constructing a clamping force estimation high-dimensional linearization model through the Koopman operator, and carrying out the offline training of a clamping force estimation-oriented high-dimensional linear system matrix set; inputting the matrix set into a clamping force estimator based on a Koopman operator to obtain a real-time clamping force; the real-time clamping force is combined with a pre-established EMB system dynamic model to obtain a unified linearization model of the EMB system, and an adaptive estimation algorithm based on minimum recursion is adopted to obtain an estimation result of time-varying parameters of the EMB system. And dynamic online unified linear modeling and self-adaptive parameter updating of the electro-mechanical braking EMB system are realized.

The invention relates to a self-adaptive fault-tolerant generalization control method for an autonomous underwater vehicle, which comprises the following steps of: considering primary and quadratic term damping effects of water, unknown constant matrixes of a mass matrix and an inertia matrix of the autonomous underwater vehicle and external interference factors; an actuator effectiveness index matrix is introduced to model the autonomous underwater vehicle into an under-actuated model, and meanwhile it is ensured that the resultant force of gravity and buoyancy is zero, and kinematics and dynamics equations of the autonomous underwater vehicle are obtained; a controller is designed based on a backstepping method, a position error and a saturation function are defined, a Lyapunov function is selected, the position error is driven to be within a preset fixed constant range, an estimation rule and a projection function are introduced for solving the problem of unknown parameters to calculate thrust and control input, and high-precision trajectory tracking control over the autonomous underwater vehicle is achieved. Compared with the prior art, the method does not need linearization or conversion dynamics, can solve the problems of fault tolerance and time-varying interference, is better in performance, and effectively improves the control precision and stability of the autonomous underwater vehicle.

The invention discloses a high-proportion new energy power system frequency response modeling method and application. The method comprises the steps that all nodes in a power system are divided into active nodes and passive nodes; establishing a linear power flow model of the power system; reducing the linearized power flow model to obtain an equivalent active node power flow injection power expression; frequency-active response models of various power resources in the active nodes and the passive nodes are established respectively; combining an equivalent active node power flow injection power expression with a frequency-active response model, and constructing a unified linear state space model taking an active node phase angle and frequency as state variables; and calculating the frequency response of the power system based on the unified linearization state space model. The unified linear state space model constructed by the method can realize quicker and more accurate simulation and stability analysis of the frequency dynamic state of the high-proportion new energy power system, and is suitable for quick simulation and online analysis of a large-scale system.

The embodiment of the invention provides a fluid characterization method for fracture frequency change attribute inversion and related equipment, which can be used for detecting fluid in a reservoir with fractures and improving the fluid identification precision. The method comprises the following steps: determining omnibearing pre-stack common midpoint gather CMP data; converting the CMP data into target angle gather data; determining azimuth difference seismic angle gather data according to the target angle gather data; performing spectral decomposition on the azimuth difference seismic angle gather data to obtain seismic frequency division difference angle gather data; constructing a function between a background rock matrix parameter and a crack weakness parameter based on a reflection theory; determining pre-stack seismic data of different orientations based on the function; determining synthesized azimuth difference data according to the pre-stack seismic data; constructing an inversion problem, and carrying out linear processing on the azimuth difference data to obtain a linear equation; and establishing a target function based on the linear equation, and solving through damping least square to obtain an inversion model, so as to detect the fluid in the fractured reservoir through the inversion model.

The invention relates to the field of nonlinear system modeling, and particularly provides a global linear modeling method and system for a nonlinear system based on Lie derivative and sparse recognition, a terminal and a medium, and the method comprises the steps: obtaining an explicit nonlinear kinetic equation of a target system, calculating the Lie derivative of a state variable or an output variable of the system based on the kinetic equation, constructing a candidate observation function library directly associated with the physical mechanism of the system; performing screening and dimension reduction on the candidate observation function library by adopting a sparse recognition method to obtain a low-dimensional observation function set; and on the basis of the screened observation function and system operation data, a finite-dimensional global linear model of the target system in the dimension raising observation space is obtained through identification by using a data driving algorithm. According to the method, the online solving efficiency and the control real-time performance are improved, and efficient and reliable multi-target collaborative optimization control of systems such as a wind driven generator is realized.

The invention discloses a converter transient power angle stability analysis method and system based on a linear parameter change controller. Establishing a small-signal dynamic model of the power synchronization type grid-following converter, and constructing a mathematical model considering power filtering and current loop dynamics by orienting an output current to a d axis of a dq coordinate system; a linear parameter change controller is designed, control parameters are adjusted in real time through parameter vectors, decoupling control over active power and reactive power is achieved, and it is ensured that the system can stably operate in both the inversion mode and the rectification mode; based on the Lyapunov stability theory, the transient power angle stability of the system under large disturbance is analyzed in combination with a linearization method and a direct method. The method does not need to depend on a phase-locked loop and grid-connected point voltage information, effectively solves the problem of synchronous stability of a traditional grid-following type converter, remarkably improves the adaptability and operation reliability of a system under the condition of a weak power grid, and provides important technical support for safe and stable operation of a new energy power system.

The invention provides a data-driven multi-target control method, device and system based on model prediction, is applied to a three-phase three-level neutral point clamped converter, and aims to realize dual accurate control of switching frequency and neutral point voltage. According to the method, a switch weight factor and a neutral point voltage weight factor are used as inputs, and an average switch frequency in a fixed sliding window time and a neutral point voltage maximum value in another fixed sliding window time are used as outputs; and constructing an equivalent dynamic linearization data model under each dynamic working point of the closed-loop system by adopting a tight-format dynamic linearization method. Based on the data model, a data driving controller is elaborately designed to realize accurate regulation and control of the switching frequency and the maximum value of the neutral point voltage. The method has a high practical value and a wide application prospect in typical application scenes of power electronics and power transmission, such as grid connection and motor driving.

The invention discloses a shaft system alignment key process identification and optimization method based on an error flow model, and the method comprises the steps: constructing an error source set in a shaft system assembly process, and building a related space coordinate system based on an assembly topological relation; establishing a nominal pose transfer equation based on an infinitesimal rigid body coordinate transformation principle; constructing a joint surface contact error vector by combining the diameter of the flange on the basis of a flange opening value and a dislocation value measured on site; performing first-order linearization processing on the nominal pose transfer equation based on the joint surface contact error vector, and obtaining a sensitivity matrix; constructing a state equation for describing error accumulation according to the sensitivity matrix and the error source set; a sensitivity coefficient is constructed based on a state equation and is compared with a judgment threshold, key control characteristics are identified, accurate errors can be predicted by quantifying a transmission mechanism of each error source, and key procedures are accurately identified in combination with the sensitivity coefficient, so that a reasonable strategy is adopted for optimization, and the product quality is ensured to be qualified.

The invention discloses a multi-converter parameter collaborative optimization method for improving the small interference stability of an active power distribution network, and belongs to the technical field of power systems, and the method comprises the steps: building a differential algebraic equation model, and carrying out the linearization of a steady-state operation point, and obtaining a state space model; and a control parameter optimization problem taking the minimization of the spectrum intercept construction as a target is defined, and control parameters are a GFL converter phase-locked loop parameter and a GFM converter virtual damping parameter. First-order sensitivity and second-order sensitivity of eigenvalues to control parameters are calculated based on a matrix perturbation theory and are converted into local quadratic constraint sub-problems. And performing convexity solution by adopting a positive semidefinite relaxation method, adjusting the step length in combination with a line search mechanism, iteratively updating control parameters, and outputting an optimization result after a convergence condition is met. According to the method, the spectral intercept is reduced through multi-converter parameter collaborative optimization, and the weak stability characteristic is improved; the convergence and solvability are improved by utilizing matrix perturbation and positive semidefinite relaxation; and the stability is enhanced and optimized by adopting line search step length adjustment.

The invention discloses an offshore wind power plant regular layout optimization method considering submarine topography and turbulence intensity, and the method comprises the steps: carrying out the grid discretization of a target region, and defining a layout rotation angle; a mixed integer linear fractional programming model with the minimum leveling cost per kilowatt-hour as the target is constructed, a wake flow model based on an axisymmetric cosine function and a turbulence model based on an axisymmetric double-Gaussian function are comprehensively integrated, and piecewise linearization is conducted on a turbulence nonlinear term; the method comprises the following steps: pre-defining a layout mode and regular layout constraints, and matching with engineering constraints such as fan number, power and safety distance; and a bounded Dinkelbach algorithm is adopted to carry out iterative solution on the sub-problems decomposed according to angles, and a global optimal layout is obtained. According to the method, wake flow loss, turbulence fatigue and terrain-related cost can be effectively and synergistically optimized, and an economical and efficient layout scheme is provided for offshore wind plant planning.

The invention provides a civil aviation fleet-oriented maintenance plan and work package integrated optimization method, which comprises the following steps of: firstly, taking minimization of total maintenance cost as a target function; constructing a mathematical planning model for integrated optimization of the fleet maintenance plan and the work package by taking a maintenance item number constraint, a maintenance item and maintenance work package corresponding relation constraint, a maintenance resource constraint, a maintenance interval constraint, a variable relation constraint, a variable value constraint and the like as constraint conditions; then, auxiliary variables are introduced to carry out linearization processing on the established mathematical programming model; and finally, solving the linearized mathematical programming model by adopting a differential evolution-adaptive large-scale neighborhood search two-stage heuristic algorithm, namely, continuously iteratively solving through a designed two-stage interaction strategy, so as to achieve the purpose of integrated optimization. According to the method, the dual goals of reducing the total maintenance cost and improving the maintenance scheme making efficiency are achieved, and a scientific solution is provided for the complex optimization problem in civil aviation maintenance management.

The invention provides a floating fan strong wind resistance variable pitch control method based on an online optimization algorithm, and the method comprises the steps: describing main components of a floating fan structure, and building a high-fidelity nonlinear dynamic model of a floating wind generating set; carrying out linearization and discretization processing on the high-fidelity nonlinear dynamic model of the floating wind generating set near a cut-out wind speed working point, and giving a control-oriented linearization reduced-order model; constructing an augmented closed-loop control system model based on a standard proportional integral variable pitch control algorithm; performing residual error correction on the closed-loop control system model based on a Gaussian process; calculating a most unfavorable wind speed prediction value based on an online rolling optimization prediction control algorithm; calculating a minimum rotating speed reference derating value meeting security constraints; and calculating a final blade pitch angle instruction. According to the method, on the premise that the load does not exceed the safety upper limit under the strong wind working condition, the minimum necessary derating can be achieved through the online optimization algorithm, and the power generation capacity is kept to the maximum degree.

The invention relates to the technical field of hydroelectric engineering, in particular to a T-S fuzzy robust control method for a hydroelectric generating set adjusting system based on a linear matrix inequality, which comprises the following steps: establishing a system nonlinear mathematical model considering modeling uncertainty and external interference under a rigid water attack condition, and converting the system nonlinear mathematical model into an affine nonlinear state space form; a T-S fuzzy model is constructed, and precise approximation of global nonlinear characteristics is realized through linearization of a linear triangular membership function and representative working condition points; a fuzzy controller is designed based on a parallel distribution compensation strategy, and a robust control problem is converted into a linear matrix inequality form to solve a feedback gain matrix in combination with a Lyapunov stability theory and an H-infinity performance index. The method effectively improves the adaptability of the system to a complex operation environment, enhances the anti-disturbance capability, can significantly reduce the system oscillation, shortens the stabilization time, guarantees the safe and reliable operation of the hydroelectric generating set, and has important engineering application value.

The invention relates to the technical field of wind power generation control, and discloses a model prediction variable pitch control method, system and equipment based on an LPV model, and a medium, and the method comprises the steps: building a full-range nonlinear variable pitch dynamic characteristic model of a wind turbine generator; through a gap measurement theory, representative working condition points are selected from a plurality of linearization models, and a linear variable parameter fan variable pitch model is constructed by taking the wind speed as a scheduling variable; and a multi-objective function is determined, a model prediction control problem is converted into a constrained quadratic programming problem for online solving, a control input quantity is calculated, and rolling optimization is carried out. According to the method, the pitch angle change is reduced, meanwhile, the loads of the blades and the hub are further reduced, the MPC control algorithm based on the LPV model can better reduce the power fluctuation under the step wind condition, and the tower footing load is reduced.

The invention provides an oil immersed transformer inspection robot spectrum manifold modeling and control method, and relates to the field of data processing. The method comprises the following steps: acquiring an attenuation track of an oil immersed transformer inspection robot without control input, extracting a dominant singular vector to construct a spectrum manifold approximate tangent space, mapping a high-dimensional observation variable to a spectrum manifold dimensionality reduction coordinate system, and fitting an autonomous drift term and a control input influence term to construct a low-dimensional dynamic model; a control observation space is further constructed, a reverse re-parameter mapping function is established, a current observation variable is obtained in real time and mapped to spectrum manifold dimensionality reduction coordinates, a rolling time domain trajectory prediction optimization problem is constructed, a control input sequence is linearly solved through a sequence convex programming algorithm, and closed-loop control is completed. According to the technical scheme, the stability of the oil immersed transformer inspection robot can be improved conveniently.

The invention discloses a distributed electric vehicle motion planning method and system considering surrounding vehicle interactive perception, and relates to the technical field of electric vehicle motion planning. The method comprises the following steps: constructing a distributed electrically-driven automobile nonlinear dynamic model, and designing a motion tracking controller by adopting a feedback linearization method; based on the constructed vehicle closed-loop dynamics system, a regional polyhedron approximation method is adopted to carry out modeling and characterization on a safety forward reachable set of the vehicle; and in combination with a probability distribution model of interaction behaviors of surrounding vehicles, introducing a conditional value-at-risk constraint mechanism, converting an automobile motion planning problem into a trajectory optimization problem, and solving and outputting a final motion planning trajectory. According to the method, the advantage that four wheels of a distributed electric drive automobile are independent and controllable is fully utilized, the control freedom degree of the method is remarkably improved compared with centralized drive, a conditional value-at-risk constraint mechanism is introduced to be combined with a surrounding automobile interaction behavior probability distribution model, and safety and agility balance in an uncertain environment is guaranteed.