357 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 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 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 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 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 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 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 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 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 discloses a robot formation control method based on double-layer nonlinear model predictive control, and relates to the technical field of model predictive control. In order to solve the defects that in the prior art, a pilot vehicle is excessively dependent, and a navigator is difficult to replace quickly during communication packet loss or fault, the technical scheme provided by the invention comprises the following steps: collecting a reference track and initializing control parameters and initial poses of all vehicles; controlling a pilot vehicle according to the reference trajectory and generating an expected formation matrix and an orientation compensation angle; converting the expected formation matrix and the orientation compensation angle into a global target pose of the following vehicle; controlling the following vehicle to perform nonlinear model prediction tracking based on the global target pose of the following vehicle; and when detecting that the pilot vehicle is abnormal, replacing a new pilot vehicle and updating the formation. The method is suitable for unmanned vehicle or multi-robot formation control tasks requiring high-reliability, low-delay switching and real-time formation keeping in a complex dynamic environment.

The invention relates to the technical field of wireless positioning, in particular to a dynamic-dynamic TDOA (time difference of arrival) cooperative positioning method based on an unmanned aerial vehicle-mounted mobile base station. The method comprises the following steps: constructing a cooperative sensing network comprising at least four unmanned aerial vehicles carrying unmanned aerial vehicle-mounted base stations, and realizing microsecond-level high-precision time synchronization of the whole network through a bidirectional distance measurement technology in combination with an introduced clock error model. All the base stations receive signals transmitted by the moving target to be measured under the unified time base, the arrival time is recorded, and a TDOA measurement value is calculated with the main node base station as the reference. And based on the group of TDOA measurement values, tracking and positioning the to-be-measured moving target by adopting an extended Kalman filtering algorithm, and estimating and outputting the three-dimensional position and speed of the to-be-measured moving target in real time by linearizing the nonlinear model. The method effectively overcomes the technical problems of difficult time synchronization and serious error coupling in a dynamic-dynamic scene, and has the advantages of being independent of ground infrastructure, high in positioning precision, strong in robustness and remarkable in cooperative gain.

The invention relates to the technical field of data analysis, in particular to a newborn health data analysis system which comprises a physical sign data construction module, a trend recognition module, a weight distribution module, a nonlinear modeling module and a prediction curve output module. According to the method, the growth cycle of the newborn is subdivided into continuous stages with different physiological meanings, the change rates of the sign data between the adjacent stages are deeply calculated and compared, the dynamic trends such as acceleration, stability or slowing down of body weight and height changes can be accurately recognized, and then quantitative weights are given to the different growth trends; according to the method, the positive growth situation is more evaluated in subsequent analysis, the growth and slowdown situation is correspondingly adjusted, a nonlinear prediction model is constructed on the basis, the non-uniform-speed natural law of growth and development of the newborn can be fit, the staged trend can be dynamically fused into long-term change track prediction, and the prediction accuracy of the growth and development of the newborn is improved. And the health conditions of the newborn in different developmental window periods are disclosed.

The invention provides a cooperative heave-rolling compensation method and system for a double-ship lifting arm system, and the method comprises the steps: building a double-ship lifting arm system dynamic model considering heave-rolling coupling motion, and enabling the model to comprise disturbance terms caused by ship motion; based on the kinetic model, generating a nominal trajectory under input saturation constraint and state constraint, and generating an expected trajectory through prediction control of a tightening constraint nonlinear model; for an actual disturbed system, designing a nonlinear robust model prediction controller, and driving an actual state to track the expected trajectory; and the stability of a closed-loop system is guaranteed through a terminal cost function and a terminal constraint set, and cooperative heaving-rolling compensation of the double-ship lifting arm is achieved. According to the method, through dynamic modeling, nominal track generation, robust control and stability guarantee, the double-ship lifting arm cooperative compensation heaving-rolling coupling motion is achieved, multi-degree-of-freedom oscillation can be effectively restrained under the five-level sea condition, and the operation stability and precision under the complex sea condition are improved.

The invention belongs to the field of geological disaster prediction, and particularly provides a reservoir landslide displacement prediction method based on dynamic lag recognition and fuzzy entropy optimization, which comprises the following steps: acquiring and preprocessing landslide time sequence monitoring data; combining the distributed lag nonlinear model with the maximum information coefficient, dynamically analyzing the lag relationship between the displacement and the rainfall and reservoir water level through a sliding window, outputting a self-adaptive lag stage and constructing a lag feature set; adaptively decomposing the displacement sequence by using variation mode decomposition of fuzzy entropy optimization, determining the optimal mode number according to the minimum fuzzy entropy, and reconstructing the intrinsic mode function into trend, period and random items; the method comprises the following steps: extracting local features of a multi-lag feature space through CNN, inputting reconstructed displacement components into GRU to capture time dependence, introducing an attention mechanism to weight a key time step, and outputting a predicted value and a confidence interval through quantile regression; according to the method, dynamic lag capture, adaptive decomposition and CNN-GRU-Attention are fused, and high-precision and high-robustness prediction is realized.

The invention belongs to the technical field of hybrid pumped storage power station dispatching, and particularly discloses a multi-time-scale nested hybrid pumped storage power station dispatching method and device, and the method comprises the steps: constructing a multi-objective function with the maximum power station income and the minimum power grid residual load peak-valley difference as the objective; based on the multi-objective function, sequentially carrying out optimal scheduling on the hybrid pumped storage power station according to a plurality of time scales from large to small to obtain a system model; converting the system model into a mixed integer linear programming model; and solving the mixed integer linear programming model to obtain a scheduling result. According to the method, the economic benefit of power station operation and the load regulation capability of a power grid can be remarkably improved, the complexity of nonlinear model solution is effectively reduced, and the scheduling efficiency and accuracy are improved.

The invention belongs to the technical field of helicopter control, and provides a turboshaft engine identification and prediction control method based on MRR-KELM in order to solve the problem that a prediction model constructed for a turboshaft engine is poor in generalization, an MRR-KELM algorithm is combined with a Huber loss function and a self-adaptive regularization strategy, noise interference and abnormal value influence are restrained, and the prediction model is optimized. The prediction error of the constructed turboshaft engine prediction model is greatly reduced, and adaptive adjustment can be performed through Gaussian kernel dynamic mapping and working condition parameters; according to the algorithm, a regularization item and a robust loss function are introduced, the kernel function mapping capability is combined, the generalization performance and the calculation efficiency of the model under the complex working conditions of small samples, noise interference and the like are remarkably improved, further, the turboshaft engine prediction model is embedded into a nonlinear model prediction control framework, and the prediction accuracy of the turboshaft engine is improved. And high-precision stable control over the rotating speed of the power turbine is achieved through rolling optimization and feedback correction.

The invention relates to the technical field of electromagnetic measurement and state monitoring, and discloses a global state sensing system and method for an electromagnetic voltage transformer. The technical problems that in the prior art, data robustness is insufficient and actual working condition changes of a power grid are difficult to adapt due to the fact that accurate resonance circuit parameters are depended and a single calculation path is adopted are solved. According to the method, ferromagnetic resonance phenomena under various faults are simulated based on electromagnetic transient simulation, phenomenon data are obtained, and influence factors are analyzed to obtain influence rule data. And taking the rule data as input, and adopting a comprehensive solving method combining programming calculation, system structure chart calculation and electromagnetic transient software simulation to obtain a high-reliability state quantity time sequence. Based on the time sequence, a nonlinear model which takes loss into account and does not depend on precise resonant circuit parameters is derived, and explicit mathematical functions of primary current and transient overvoltage of the voltage transformer are obtained.

The invention discloses a detection and monitoring system for fault early warning in use of an insulating sling, relates to the technical field of electrical safety and intelligent monitoring, and is used for solving the problem that an internal progressive deterioration trend cannot be captured. A microwave resonance signal in the sling is obtained, the resonant frequency offset of each layer is extracted based on spectral decomposition, and a humidity distribution result is inversed and fed back in combination with a dielectric property database, and is used for setting a monitoring period and collecting operation data; a key area is screened based on a humidity diffusion coefficient, an evaluation area of which the risk level needs to be improved is identified by using a weighted average method, and the risk weight is calculated and the early warning time is set through a nonlinear model in combination with the physical attribute and the diffusion coefficient of the area, so that the early identification and dynamic early warning of the hidden fault of the sling are realized, and the safety and the monitoring precision are effectively improved. And the sudden failure risk is reduced.

The invention relates to an aiming point dynamic stabilization and automatic calibration method fusing inertial measurement unit data, and belongs to the technical field of aiming equipment dynamic control. The method comprises the following steps: acquiring attitude data of an inertial measurement unit, and constructing a multi-source data matrix in combination with real-time position data of an aiming point and environmental interference data; after time sequence synchronization and drift suppression are carried out on multi-source data, associated feature vectors are extracted through multi-modal feature fusion; inputting a self-adaptive extended state observer to estimate total disturbance and generate an anti-interference compensation amount, constructing a kinetic model in combination with a nonlinear model prediction controller, solving a multi-objective optimization problem, and generating an attitude compensation control sequence to realize dynamic stability of an aiming point; and finally, aiming deviation is calculated in real time to trigger double-closed-loop calibration, an inner ring corrects the drift error of the inertial measurement unit, and an outer ring optimizes parameters of the observer and the controller. According to the invention, environmental interference and inertial drift are effectively suppressed, the dynamic aiming stability is improved, and the long-term aiming reliability is guaranteed through closed-loop calibration.

The invention provides a hydraulic pressure control method for a wheel cylinder of a brake-by-wire system of an electric vehicle. The hydraulic pressure control method comprises the following steps: constructing a nonlinear model of an electronic hydraulic brake system of the electric vehicle; designing a self-adaptive expansion state observer based on an improved particle swarm optimization algorithm to estimate the initial hydraulic pressure of the wheel cylinder of the electric vehicle; taking the estimated initial hydraulic pressure of the wheel cylinder of the electric vehicle as input, and designing a sliding mode attitude controller based on a self-adaptive expansion state observer; the improved sliding mode control method is obtained by improving an objective function and a fitness function established by an alpha evolutionary algorithm, improving a sliding mode surface equation through the objective function and the fitness function, and introducing a chaotic disturbance term, an evolutionary compensation term and a self-adaptive buffeting suppression factor. And the hydraulic pressure of the wheel cylinder of the electric vehicle is controlled through an improved sliding mode control method. According to the invention, high-precision hydraulic pressure control can be realized, and the anti-interference capability is improved.

The present invention discloses a quadrotor unmanned aerial vehicle (UAV) obstacle avoidance system based on adaptive nonlinear model predictive control, the system comprising a ground mission management system, a sensing system, an adaptive nonlinear model predictive obstacle avoidance system, and a power system. The present invention also discloses a quadrotor unmanned aerial vehicle (UAV) obstacle avoidance method based on adaptive nonlinear model predictive control, the method comprising the following steps: step 1, obtaining UAV target trajectory information and obstacle information; step 2, obtaining the UAV's real-time status; step 3, obtaining control instructions based on the information in steps 1 and 2. The quadrotor unmanned aerial vehicle (UAV) obstacle avoidance method based on adaptive nonlinear model predictive control provided by the present invention can simultaneously solve the two major problems of fault handling and obstacle avoidance in quadrotor unmanned aerial vehicle flight control, and significantly improve flight performance.