441 results about "Nonlinear model" patented technology

The invention provides a photovoltaic power station photovoltaic power prediction method and system. A cloud layer motion time sequence library is constructed based on a historical cloud picture, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and the aerosol density in real time, calculates the light intensity attenuation gradient of the shadow area in combination with the model, and predicts the remaining time of the cloud cluster reaching the core area. Dividing a power prediction confidence interval according to remaining time, inputting satellite data into a multi-scale meteorological fusion architecture, and adjusting a meteorological feature extraction path of an attention mechanism based on cloud density. And fusing the surface reflectance correction value calculated by the scattering data of the light sensor to generate a short-term power prediction sequence of the sudden change cloud layer scene. And inputting the sequence into an energy management system, and outputting an active and reactive double-target scheduling instruction in combination with a grid-connected point voltage deviation and a reactive compensation parameter constrained by a power grid rule. According to the technical scheme provided by the invention, the cooperative scheduling precision of the photovoltaic power station in the cloud abrupt change scene is improved.

The invention discloses an unmanned aerial vehicle trajectory planning and tracking method, system and device based on deep reinforcement learning and adaptive nonlinear model predictive control, and a medium. The method comprises the following steps: constructing various static multi-obstacle and dynamic multi-obstacle simulation environments; constructing a kinetic model of the unmanned aerial vehicle; constructing an adaptive nonlinear model predictive control (ANMPC) algorithm; constructing a reward function of the tracking performance of the unmanned aerial vehicle to the reference trajectory generated by the adaptive nonlinear model predictive control algorithm; constructing a network framework based on deep reinforcement learning and an adaptive nonlinear model predictive control algorithm; setting network parameters; training a network framework, and selecting an optimal weight file; outputting a test result; the system, the device and the medium are used for realizing the unmanned aerial vehicle trajectory planning and tracking method. The method can effectively cope with changes of targets and environments, shows strong obstacle avoidance capability and anti-interference performance when facing dynamic obstacles and wind noise interference, and embodies a high intelligent decision-making level.

The present invention relates to the technical field of oil and natural gas exploitation, and discloses a real-time monitoring and intelligent control system for hydraulic fracturing cracks in oil reservoirs, including: a real-time monitoring module, which is used to collect crack geometric morphology parameters and construction operation parameters in real time during crack expansion; a nonlinear modeling module, which is connected to the real-time monitoring module, and is used to construct a nonlinear dynamic model of crack expansion according to the collected crack expansion parameters and construction operation parameters, and output a predicted value of crack morphology; an optimization control module, which is connected to the nonlinear modeling module, and is used to calculate the optimized construction parameters according to the nonlinear dynamic model, including injection pressure, injection flow rate and viscosity of fracturing fluid. The present invention realizes precise control of crack expansion, intelligent optimization of construction parameters and efficiency improvement through the closed-loop linkage of real-time monitoring, nonlinear modeling, optimization control and feedback execution.

The invention discloses a road-oriented full-process digital collaborative management system and method, particularly relates to the technical field of road data management, and is used for solving the problem of abnormal multi-role collaborative conflict recognition. According to the method, dynamic linkage management of task states and role behaviors in the whole highway design process is achieved by building the task-driven atlas and the multi-role scheduling model, and the dynamic linkage management of the task states and the role behaviors in the whole highway design process is achieved by extracting access behaviors, task records and data version states, calculating role collaborative offset and recognizing and controlling the write-in permission of task conflict nodes. A priority factor is constructed based on a stage index, data dependence and a time urgency degree, a nonlinear model is adopted to generate scores, automatic reconstruction of a scheduling sequence and data ownership is driven, role permission and a collaborative view are synchronously updated, a data access control closed loop based on task state evolution is formed, and a three-dimensional responsibility chain is constructed by whole-process behavior traces. The traceable management of collaborative operation is realized, and the intelligence of collaborative scheduling and the accuracy of data management are improved.

The invention provides a building earthquake damage scene construction method and system based on an unmanned aerial vehicle remote sensing image, and relates to the technical field of urban earthquake resistance and disaster prevention, and the method comprises the steps: constructing a three-dimensional space model of a building through an urban earthquake damage simulation system, and determining the nonlinear model parameters of the building; carrying out earthquake damage scene construction by taking a single building as granularity by adopting an elastic-plastic time-history analysis method, and determining a building damage index; according to a mapping relation between a preset building damage index and a building earthquake damage risk level, determining an earthquake damage risk level of each building; and according to the building list, the anti-seismic performance of the building with the specified earthquake damage risk level is detected, and anti-seismic measures corresponding to the building with the specified earthquake damage risk level are generated. According to the geometric attribute information of the building, the three-dimensional space model is constructed in combination with the urban earthquake damage simulation system, the earthquake damage risk level of the single building is accurately divided, and the refinement level of urban earthquake resistance and disaster prevention is improved in combination with earthquake resistance measures.

The invention discloses a shield tunneling machine axis predictive control method and system, electronic equipment and a storage medium, the shield tunneling machine axis predictive control method is based on data driving modeling, system dynamic characteristics contained in shield tunneling axis control related data are fully excavated, the predictive control precision is high, and the predictive control precision is high. A GRU (Gated Recurrent Neural Network) model and an ARX (Autoregressive Regressive) model with exogenous variables are combined to construct a guide deviation dynamic characteristic model to predict the guide deviation, the advantage that the GRU is good at processing a long-interval and long-delay time sequence is fully utilized, and the correlation characteristics before and after the shield guide deviation data time sequence are fully excavated; the model not only has the global nonlinear description capability, but also has the capability of describing the time sequence correlation characteristics of the system, the dynamic characteristics of the system are more comprehensively described, and the model is a nonlinear model which is more in line with the guiding actual working characteristics of the shield tunneling machine, and is higher in generalization capability and higher in precision.

The invention relates to the technical field of ocean renewable energy power generation, and particularly discloses a floating type wind wave combined power generation system model prediction control method, which comprises the following steps: establishing a nonlinear mathematical model of a floating type wind wave combined power generation system; determining a steady-state working condition point of the system; carrying out linear processing on the nonlinear model; constructing a discrete time prediction model; designing a target function; defining a state and an input constraint condition; performing real-time rolling optimization solution; and dynamically feeding back a correction mechanism. Starting from a wind wave excitation mechanism, on one hand, response in a turbulent wind frequency band is reduced through fan variable pitch control, and on the other hand, response in a wave frequency band is reduced through wave energy power generation device PTO control, so that tower footing load is effectively reduced, and pitching motion of a platform is restrained. Global multi-objective optimization is realized by designing an objective function, defining constraint conditions and solving the objective function.

The invention discloses an intelligent control method and system based on pole-mounted circuit breaker equipment, and relates to the technical field of power grid control, and the method comprises the steps: collecting environmental data of a pole-mounted circuit breaker, employing a Kalman filtering fusion algorithm, outputting a fusion state vector, constructing time series data, employing a photon to calculate an accelerated time series neural network, and obtaining a time series neural network model; and performing complex field mapping and optical phase shift modulation on the extracted features, and predicting a future current value. According to the method, complex field mapping is carried out on features extracted by a time convolution layer, the expression ability of nonlinear features is enhanced, optical phase shift modulation is introduced, phase and amplitude information is further adjusted, the nonlinear modeling ability of time sequence features is improved, and multi-modal data fusion and multi-scale consistency verification of fractal dimensions are combined, so that the time sequence features of the fractal dimension are optimized. Time scale deviation of different data sources is eliminated, and effectiveness and consistency of multi-dimensional feature fusion are improved.

The invention discloses an in-situ hyperspectral water quality parameter inversion method based on deep learning. According to the method, a parallel network model is constructed by combining a convolutional neural network (CNN) and a Transformer, local features are extracted by using the CNN, global information is captured by using the Transformer, and comprehensive analysis of complex features and waveband information in in-situ hyperspectral reflectivity data is realized. In the model training process, the initial layer of the network is pre-trained by adopting a self-supervised learning strategy of random shielding and noise injection, and then the weight of the network is finely adjusted by utilizing labeled data, so that the understanding of the model on a data feature mode is enhanced. According to the method, the unlabeled data is fully utilized, and the training precision and generalization ability of the model are improved. Compared with a traditional explicit linear or nonlinear model constructed by depending on prior knowledge, the method has the advantages that the recessive correlation between spectrums can be captured, end-to-end water quality parameter inversion is realized, and the result is remarkably improved in the aspects of precision and generalization ability.

The invention relates to the field of aircraft intelligent control, and discloses a sensor fault resistant low-altitude aircraft multi-mode propulsion control method, which comprises the following steps: constructing a nonlinear model of a short-vertical aircraft hybrid propulsion system, taking deep exploration optimization reinforcement learning as a main control, and combining a fuzzy controller and a model prediction controller to form a strategy fusion group; the method comprises the following steps: introducing a residual perception mechanism and a GIRLS-EKF health estimation module, extracting residual signal features by using CNN and Transform, and carrying out real-time diagnosis and dynamic weight adjustment on an abnormal measurement state of a sensor in combination with generalized residual least square filtering and an extended Kalman structure; the controller fusion module outputs the optimal control quantity in a self-adaptive manner according to the health state and the flight mode, and the fault-tolerant performance and the energy efficiency scheduling capability of the multi-propulsion system under the complex working condition are improved; the method is suitable for low-altitude economy and urban air traffic task scenes, and has good engineering integration and intelligent control application prospects.

The invention relates to the technical field of industrial mechanism models, in particular to a shock absorber performance optimization control method based on model fusion, which comprises the following steps: extracting a low-frequency disturbance state variable and inputting the variable-topology industrial mechanism model to generate a nominal reference state trajectory; utilizing a depth state observation network fused with energy passivity constraint to calculate a non-linear model mismatch compensation amount and an adaptive weighting parameter; performing dynamic fusion on the nominal reference state trajectory and the compensation amount based on the adaptive weighting parameter to generate a generalized state estimation value; and executing dynamic multi-objective optimization based on the generalized state estimation value, and generating mixed mode control input acting on an execution end. According to the invention, through adaptive fusion of a mechanism model and a data driving method, physical consistency, calculation real-time performance and robustness of a control process are considered.

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.

Disclosed in the present invention are a data-driven linear MPC-based assessment method and apparatus for a frequency regulation capability of a wind farm, and a control method and apparatus. The assessment method comprises the steps of: offline acquiring historical operation data sets of wind turbines, and training a linear predictive control model, wherein the linear predictive control model is obtained by applying a dimensionality-increasing transformation process to a wind farm frequency modulation dynamic nonlinear model on the basis of a Koopman operator theory; acquiring real-time operation data of the wind turbines, and using the acquired real-time operation data to solve for a linear MPC optimization model, to obtain droop coefficients of the wind farm, wherein the linear MPC optimization model is constructed on the basis of the trained linear predictive control model and by taking the maximum droop coefficient of the wind farm as an objective and setting a safety rotational speed constraint condition; and assessing a frequency regulation capability of the wind farm on the basis of the obtained droop coefficients of all the wind turbines in the wind farm. The present invention has the advantages of ease of implementation, high assessment efficiency and accuracy, and strong scalability, and the like.

The invention provides a shale total organic carbon content logging prediction method and device based on a random forest algorithm. The method comprises the steps that logging curve data are collected and processed; constructing a TOC prediction model according to the collected logging curve data by using a random forest algorithm; training the prediction model by using a training data set of TOC values; and predicting the shale TOC by using the trained prediction model. According to the method, the mud shale TOC prediction precision is improved, the influence of human factors is eliminated, rapid processing and comprehensive analysis of a large amount of logging data can be achieved, key information related to TOC is extracted, the defects of a linear or nonlinear model and a single machine learning algorithm model are overcome, the mud shale TOC prediction precision is effectively improved, and the prediction accuracy of the mud shale TOC is improved. Powerful support is provided for exploration and development of shale oil and gas.

The invention relates to a fish and shrimp growth trend prediction method and device based on a growth model. The method comprises the following steps: determining target fish and shrimp types, culture conditions and day age range data; obtaining weight data of the target fish and shrimp species in each day from the day age range data under the determined culture condition; preprocessing the weight data to obtain preprocessed weight data; determining a nonlinear model with good fitting efficiency in fishes and shrimps; based on the preprocessed weight data, performing modeling through a nonlinear model to obtain a first model, a second model and a third model; obtaining key information based on the first model, the second model and the third model; determining an optimal model in the first model, the second model and the third model based on the key information; and performing growth trend prediction on the target fish and shrimp species under the current culture condition by using the optimal model. The method can assist in determining the growth and development state of the animal, and brings great convenience to related workers.

The invention discloses a model parameter identification method for hysteresis characteristics of a giant magnetostrictive material, and the method comprises the steps: describing the hysteresis characteristics through employing a nonlinear Jiles-Atherton model, and obtaining a hysteresis loop of the magnetostrictive material through constructing a mathematical model; the method comprises the following steps: performing parameter identification on hysteresis characteristics of a giant magnetostrictive material by using a longitudinal and transverse crossing strategy-based improved north pole sea-sparrow optimization algorithm, and improving the north pole sea-sparrow optimization algorithm by using a longitudinal and transverse crossing strategy to improve global optimization and local optimization capabilities of the algorithm; the model parameter identification method provided by the invention is simple in design and high in iteration efficiency, can replace a large number of manual parameter setting tests, can quickly obtain the optimal solution of the hysteresis model, and provides theoretical guidance for engineering application of the giant magnetostrictive material.

The invention discloses a tower crane motion planning method and device in a complex environment, and the method comprises the steps: building a nonlinear model for comprehensively describing the motion characteristics and transient behaviors of a crane-load system on the basis of kinetic analysis, and carrying out differential flat analysis; a BiRRT * algorithm based on direction bias is provided, a node expansion process is optimized by introducing a target bias mechanism of fusion region probability sampling and based on an improved potential field function direction guiding mechanism, and the efficiency and quality of path planning are improved. An improved random tree extension mechanism is combined with a depth deterministic policy gradient (DDPG) of reinforcement learning, and adaptive adjustment of sampling direction and step length parameters is realized. Path points obtained through path planning serve as profile value points of trajectory planning, system full-state constraint conditions such as load obstacle avoidance and shimmy reduction are fully considered, multi-target trajectory planning is carried out based on an NURBS curve, and a multi-target comprehensive optimal trajectory on the aspects of total hoisting time, operation energy consumption and load shimmy suppression is obtained.

The invention discloses a method and a system for evaluating the nonlinear influence of a greenbelt landscape pattern on a rainfall flood regulation service. The method comprises the following steps: step 1, constructing a basic database; 2, processing data variables; 3, constructing a nonlinear model: taking a green land landscape pattern factor as a core explanation variable, taking a rainfall flood regulation service index as a response variable, introducing a natural environment factor and a social economic factor as control variables, training the random forest model, and constructing a rainfall flood regulation service prediction model; 4, key factors are identified, wherein the top-ranked factors are regarded as key factors playing a role in rainfall flood adjustment service; step 5, analyzing a key threshold: performing visual analysis on a nonlinear relationship between the key factor and the rainfall flood adjustment service by adopting a partial dependency graph algorithm; and by observing positive and negative influence trends and change rates of the key factors on the rainfall flood regulation service indexes in different value intervals, identifying potential key thresholds.

The invention discloses an incremental model prediction control method and device for variant aircraft information, a medium and a product, and relates to the field of aircraft control, and the method comprises the steps: building a nonlinear model of a variant aircraft based on the variant aircraft information; the information of the variant aircraft comprises speed, angle of attack, pitch angle, height, thrust, resistance, lift force, pitch moment, mass and rotational inertia; classifying and simplifying the nonlinear model of the morphing aircraft according to a time scale separation principle to obtain an incremental equation; based on the incremental equation and an MPC framework, constructing an incremental model prediction controller under input and state constraints; and completing predictive control of information of the variant aircraft based on the incremental model predictive controller. The real-time control precision of the morphing aircraft can be improved while the calculation amount is reduced and the calculation efficiency is improved.

The invention relates to the technical field of meteorology and hydrology, in particular to a rainfall space reconstruction method, system and device based on terrain and meteorological multi-factor fusion driving and a medium, and the method comprises the steps: obtaining space terrain factors and meteorological observation data of a target area, and employing a BP neural network to progressively interpolate missing measurement values of meteorological driving variables in a layered manner; setting a missing detection elimination rule based on a wet season and a dry season to process precipitation data; constructing a 14-dimensional high-dimensional input feature system fusing a space terrain factor, a time sequence factor and a meteorological driving factor; nonlinear models such as an XGBoost model, a BP neural network model or an LSTM model are used for training, and finally the monthly scale precipitation space reconstruction of the grid is achieved. According to the method, the problems that a traditional interpolation method is poor in adaptability in a complex terrain area and insufficient in multi-factor driving relation description are effectively solved, and the precision and the physical consistency of precipitation space distribution are remarkably improved.

The invention discloses a low-coherence interference demodulation method based on modal decomposition and a radial basis function neural network, and the method comprises the steps: carrying out the empirical mode decomposition of a filtered low-coherence interference signal, extracting the effective time-frequency domain features of each IMF and the mathematical statistics time domain features of the low-coherence interference signal, and forming a low-coherence interference signal feature data set; constructing and training a radial basis neural network, establishing a nonlinear model between low-coherence interference signal features and pressure, setting an input layer to correspond to a fusion feature vector, setting a hidden layer to adopt a radial basis function as an activation function, realizing nonlinear mapping by using a Gaussian kernel function, and outputting a layer to correspond to a pressure value; the network is trained through the feature data set, a mean square error is used as a loss function, a gradient descent method is adopted to optimize the weight and threshold of the network, and iterative training is carried out until the error converges; and inputting a low-coherence interference signal acquired by an optical fiber Fabry-Perot pressure demodulation device, and demodulating the signal through the trained radial basis function neural network model to obtain a corresponding pressure value.

The invention provides a steering-by-wire vehicle variable transmission ratio design method and system based on a fuzzy neural network, and belongs to the field of vehicle steering-by-wire. According to a fixed gain method, the steering angle transmission ratio is too small at a low speed, and the sensitivity is low at a high speed; the existing intelligent algorithm excessively depends on the experience of a designer and the quality of a data sample. The method comprises the following steps: establishing a closed-loop driver-vehicle system, designing a multi-target evaluation method by using a quadratic cost function of a vehicle dynamic state, and obtaining a data relationship between a vehicle ideal variable transmission ratio characteristic and a vehicle longitudinal speed and a steering wheel angle; a nonlinear control model is established through a fuzzy RBF network, and a globally optimal solution is obtained based on nonlinear model learning. The robustness of the control system is improved, the stable and reliable transmission ratio can be provided under various vehicle conditions, and the problem that a steering system is light and flexible is solved, so that the experience feeling of a driver under various vehicle conditions is improved, and meanwhile the operation stability and safety of the vehicle are improved.

The invention discloses an air-ground cooperative system model prediction formation control method in an underground pipe gallery environment, and the method comprises the steps: constructing an air-ground cooperative system nonlinear model, and constructing an underground pipe gallery model; designing a high-order interference observer to estimate current unknown external disturbance, and establishing an interference prediction model to estimate future unknown external disturbance; a rolling optimization control strategy based on state prediction is designed, control signals of all followers are optimized according to the current state of the system and the predicted future state in each control period, and the tracking precision is ensured while the tracking precision is improved. Collision between followers, collision between the followers and the wall of the pipe gallery and obstacle avoidance between the followers and static and dynamic obstacles in the pipe gallery are achieved; and solving is carried out based on a distributed model prediction formation control algorithm combined with a Lyapunov stability theory, so that each follower keeps an expected formation configuration while tracking a leader trajectory. And the driving safety of the formation in the underground pipe gallery environment is ensured.

The invention provides a multi-dimensional data preferential analysis method and device based on a gradient boosting decision tree model, and relates to the technical field of data mining, in the method, a target gradient boosting decision tree model is adopted, the nonlinear modeling problem of strategy optimization in multi-dimensional data is solved, and the multi-dimensional data is optimized according to the target weight determined in the training process. According to the method, key features can be accurately identified, the processing efficiency and precision are high, compared with a traditional linear regression or single decision tree method, the accuracy and real-time performance of strategy optimization can be improved, high interpretability and robustness are achieved, an optimization strategy can be adjusted in a self-adaptive mode in a changeable environment, and the method is suitable for popularization and application. The method is widely applied to multiple fields of industrial optimization, intelligent decision making and the like.

The invention discloses a weighing monitoring early warning method and device for a smart scale, and relates to the technical field of weighing monitoring, and the method comprises the steps: activating a smart scale detection program to carry out the weighing record calculation of a target smart scale, and determining an initial weighing coefficient; building a weighing scene test parameter table, monitoring to obtain a real-time weight AD value set, and recording actual weight information of an object; carrying out partition regression fitting to generate a weighing nonlinear model; performing comparison calculation on the dynamic weighing coefficient set and the initial weighing coefficient in sequence, and determining a weighing coefficient deviation slope set; and constructing a weighing dynamic early warning mechanism, determining a weighing early warning strategy, and carrying out weighing early warning control on the target intelligent scale. The technical problem that weighing deviation and early warning hysteresis cannot be timely and accurately monitored due to the fact that weighing of an intelligent scale in the prior art is easily interfered by factors such as environment and long-term use is solved, and the technical effect of improving weighing accuracy of the intelligent scale and monitoring and early warning timeliness is achieved.

The invention provides a gradient coil model correction method for ultra-low field magnetic resonance, and relates to the technical field of gradient coil control in medical imaging equipment, and the method comprises the steps: collecting a real voltage signal d (n) and a real current signal x (n) of a gradient coil in real time, and n represents a sampling moment; a gradient coil model is constructed, the gradient coil model comprises a linear model constructed based on an adaptive filtering algorithm and a nonlinear model constructed based on a neural network algorithm, and a first prediction voltage signal # imgabs0 # output by the linear model and a second prediction voltage signal # imgabs1 # output by the nonlinear model are determined according to the real current signal x (n); determining a mixed prediction voltage signal # imgabs4 # of the gradient coil model according to the first prediction voltage signal # imgabs2 # and the second prediction voltage signal # imgabs3 #; and updating the weight of the linear model and the weight of the nonlinear model according to the real voltage signal d (n) and the mixed predicted voltage signal # imgabs5 # so as to adapt to gradient coil model correction under complex working conditions.

The invention provides a green electricity hydrogen production system optimization method and system considering multi-tank service life balance, and belongs to the technical field of new energy and electrochemistry, the method comprises the steps of obtaining market data, power grid data and downstream hydrogen load demand data in a preset scheduling period, and obtaining initial state data of a plurality of electrolytic tanks deployed in a hydrogen production station; constructing a mixed integer quadratic programming optimization model with the goal of maximizing the comprehensive benefit of the system; the model is solved, and the optimal scheduling strategy of the start-stop state and the operation power of each electrolytic cell in each time period in the scheduling period is obtained; and converting the optimal scheduling strategy into a control instruction, and issuing the control instruction to a controller of a hydrogen production station to complete collaborative optimization of the green electricity hydrogen production system. The method aims at solving the problems that in an existing green electricity hydrogen production system scheduling method, the calculation cost of a nonlinear model is high, the nonlinear model is difficult to apply to an actual large-scale system, and short-term market income and long-term equipment life cannot be effectively coordinated and balanced.

The invention discloses an intelligent switch planning method considering pre-disaster-middle-post multi-stage cooperation and disaster uncertainty, relates to the technical field of power grid intelligent switch planning, and is used for solving the problem that the uncertainty of disaster intensity is not considered in existing pre-disaster planning. By coupling available measures of a pre-disaster planning stage, a pre-disaster prevention stage and an in-disaster and post-disaster recovery stage, multi-stage coordination of determining the number of switches in the planning stage, completing switch site selection in the prevention stage and closing the switches to recover loads in the in-disaster and post-disaster stages is realized. In addition, the invention further provides a step-by-step hedging algorithm, the large nonlinear model is decomposed into a plurality of scene-related sub-problems to be solved, and the calculation burden is greatly reduced. A simulation result shows that compared with a pre-disaster-post-disaster two-stage planning model, the model provided by the invention not only can obtain a more flexible planning scheme, but also can reduce investment cost and load outage loss.

The invention discloses a controller optimization method and system for a tilting quad-rotor unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: building a nonlinear model of the quad-rotor unmanned aerial vehicle based on a tilting structure with a vehicle arm as an axis; the nonlinear model of the unmanned aerial vehicle is converted into an LPV model through a Jacobian linearization method, and a controller with parameters depending on state feedback is designed for the LPV model; a Lyapunov function suitable for a system is constructed, a group of LMI conditions are derived, control gains corresponding to the LMI conditions are obtained, and a gain scheduling controller which carries out gain scheduling according to variable parameters and meets system stability conditions is obtained. The problem of controller design under the condition that both the state matrix and the control matrix of the LPV model contain variable parameters is solved.

The invention relates to a morphing aircraft attitude control frequency domain analysis method based on active disturbance rejection control. The method comprises the following steps: establishing a six-degree-of-freedom nonlinear model and an aerodynamic force and aerodynamic moment model of a morphing aircraft; designing a variant aircraft attitude angle control law based on ADRC; the ADRC is converted into the LADRC; the LADRC is subjected to parameterization; performing Laplace transformation on the parameterized LADRC control law and system, equivalently converting a closed-loop system into a unit negative feedback system, and obtaining a disturbance transfer function and a system open-loop transfer function; analyzing the frequency domain characteristic of the disturbance transfer function to obtain the anti-interference characteristic of the ADRC to the internal and external disturbances of the system; and analyzing the frequency domain characteristic of the open-loop transfer function of the system to obtain the robust characteristic of the ADRC to the uncertainty of the model. According to the invention, attitude control of the morphing aircraft is realized through the active disturbance rejection control method, and attitude stability of the morphing aircraft before and after morphing and in the morphing process and accurate tracking of instruction signals are ensured.