183 results about "Non linear dynamic" patented technology

The invention belongs to the field of intelligent monitoring, and discloses a photovoltaic flexible support load deformation monitoring system and method, and the system and method achieve the safe and precise management and control of a structure through the deep fusion of multi-source sensing, mechanism modeling and data driving. Structural deformation parameters are synchronously obtained by combining a laser displacement sensor and a double-axis tilt angle sensor, and the limitation that a traditional monitoring means is single in parameter and lack of dimensionality is broken through. After data is gathered in real time through a wireless transmission module, a physical constraint-data learning hybrid framework is adopted for processing, a physical model constructs a deformation calculation equation based on a moment balance and deflection theory, a theoretical lower limit conforming to an engineering mechanics law is provided, and an LSTM neural network captures nonlinear dynamic association such as wind load sudden change through time sequence learning. A prediction result with both theoretical compliance and data adaptability is generated, and the operation reliability and maintenance efficiency of the photovoltaic flexible support in a complex environment are improved while high real-time performance is maintained.

The invention discloses a dissolved oxygen closed-loop regulation and control method used in a sewage treatment process. The method comprises the following steps: acquiring real-time dissolved oxygen concentration in a reaction tank through a distributed sensor array, and performing temperature compensation; an online water quality analyzer is used for collecting water inlet flow, chemical oxygen demand, ammonia nitrogen and water temperature to construct a comprehensive input feature vector; establishing a nonlinear dynamic mapping model based on a deep neural network, and outputting a target dissolved oxygen set value changing along with working conditions; an aeration adjusting instruction is generated by adopting a feedforward-feedback composite adaptive fuzzy PID control algorithm; dynamically adjusting the air volume of an air blower and the opening density of a partition aeration disc to match the aerobic rate of microorganisms; and the control performance is evaluated regularly, and a model parameter online optimization mechanism is triggered to continuously improve the setting precision. According to the method disclosed by the invention, the dissolved oxygen can be accurately controlled, so that the aeration energy consumption is reduced by not less than 18%, the control precision is within + / -0.3 mg / L, and the robustness, the automation level and the low-carbon operation capability of the system are remarkably improved.

The invention belongs to the field of distributed system control, and particularly relates to a vehicle system distributed control method, system and device based on reinforcement learning. Cooperative and competitive dynamic characteristics of a multi-vehicle system are described through a second-order nonlinear dynamic model in combination with a signed directed graph, unknown nonlinear dynamic and external disturbance existing in the vehicle system are considered, and the whole system is modeled as a second-order strict feedback nonlinear system. In order to get rid of dependence of convergence time on initial conditions and optimize transient performance, a preset time performance function is introduced, and a three-part neural network architecture is constructed; and designing a control law cooperating with a preset time parameter, and ensuring the actual preset time boundaries of the multi-vehicle system under the unknown dynamic and external disturbance influence. According to the method, the convergence time can be accurately set, the calculation complexity of a control algorithm is remarkably reduced, and the transient performance and robustness of a vehicle system are improved.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm predictive control method which comprises the following steps: defining the state of a mechanical arm and modeling; collecting data to construct a Koopman linear lifting determinacy prediction model; carrying out probability representation on the error by utilizing Gaussian process regression; constructing a random prediction model by combining a deterministic model and an error model, and deducing a state distribution evolution equation; and converting the joint constraint into probability opportunity constraint, carrying out deterministic equivalent conversion, and solving a rolling optimization problem. According to the method, dependence on a precise dynamic model can be abandoned, precise approximation of nonlinear dynamics is achieved through data driving, high-precision trajectory tracking of the tail end of the mechanical arm is achieved on the premise that it is guaranteed that high probability meets safety constraints, and control conservative property is reduced.

The invention discloses a reinforcement learning-power hardware-in-the-loop training system and method for a power converter, and belongs to the technical field of power electronic control and artificial intelligence crossing. The system comprises a real-time digital simulator, a power amplifier, a controlled power converter and an AI accelerator. The method comprises the steps of S1, system initialization and environment configuration; s2, performing safe online training; and S3, strategy convergence and solidification. Through the power hardware-in-the-loop architecture, the risk of reinforcement learning exploration is completely avoided, and hardware damage caused by overvoltage and overcurrent is fundamentally eradicated in combination with a real-time hardware protection strategy. According to the method, the real object power converter controller is incorporated into a training closed loop, so that an intelligent agent strategy directly learns and adapts to the nonlinear dynamic state of real hardware, the gap from simulation to reality is thoroughly filled up, and it is ensured that a training result can be directly applied to an actual system. According to the invention, a full-real-time training system is constructed, and synchronous concurrent processing of data acquisition, strategy execution and model updating is realized.

The invention relates to the technical field of computers, discloses a digital twinborn monitoring method and system for multi-process coupling operation of an assembly type sewage plant, and aims to solve the problem that a traditional model is difficult to capture multi-process coupling nonlinear dynamic characteristics and improve the intelligent optimization operation level of the sewage plant under complex water quality and water quantity changes. According to the method, real-time data are collected and preprocessed, a hybrid digital twinborn model fusing a mechanism and data driving is constructed, state synchronization, prediction simulation, intelligent optimization decision and fault diagnosis early warning are carried out, and a control instruction is output. The system comprises a data acquisition preprocessing module, a digital twin modeling module, a simulation prediction module, an intelligent monitoring diagnosis module, an optimization decision module, a control instruction output module, a man-machine interaction module and the like. Physical digital bidirectional high-precision interaction and hybrid modeling are realized, the prediction and diagnosis capability is remarkably improved, the operation cost is reduced, the effluent is ensured to reach the standard, and the intelligent management of the sewage plant is comprehensively improved.

The application discloses a kind of anti-network attack's multi-vehicle safety platoon control method and system, the method first establishes multi-vehicle queue dynamics model;Second, design fuzzy state observer to estimate vehicle state and unknown nonlinear dynamics;Then, introduce funnel function to build time-varying performance boundary, and the double constraints of vehicle anti-collision and keep communication are converted into funnel constraints of spacing tracking error;Then, based on backstepping method and funnel performance error, design virtual controller and adaptive law, and obtain adaptive safety controller to obtain control input;Finally, through Lyapunov stability analysis, the tolerance threshold condition that system can still maintain final consistent bounded stable after suffering replay attack is quantitatively obtained.The application simplifies design through funnel control, avoids complex barrier lyapunov function, and for the first time clearly provides quantified anti-interference ability to replay attack, enhances the network security and practicality of multi-vehicle queue system.

This invention relates to the field of thermal power generation cost accounting technology, and proposes a dynamic modeling method and system for the cost per kilowatt-hour based on the forward and reverse balance method. The method includes: real-time acquisition of data on instantaneous flow rate of the coal feeder, coal pile volume, density, coal analysis upon entering the furnace, unit operation, and fuel price; calculation of dynamic coal quality data using a multimodal fusion algorithm; calculation of forward fuel cost based on flow rate, coal quality, and price data; calculation of reverse balance theoretical cost by inferring the theoretical standard coal quantity based on unit operation data; comparison and verification of forward and reverse costs to determine the final fuel cost; and calculation of dynamic cost per kilowatt-hour by combining variable cost and power generation data. This invention achieves a nonlinear dynamic mapping from multi-dimensional real-time data to accurate cost per kilowatt-hour, enabling high-precision, real-time, and automated dynamic modeling of the cost per kilowatt-hour for thermal power generation enterprises.

The present application belongs to the technical field of trajectory tracking control of nonholonomic wheeled mobile robots, and specifically discloses a trajectory tracking control method and system for nonholonomic wheeled mobile robots. In the trajectory tracking process of the nonholonomic wheeled mobile robot, the present application method converges the position tracking error and the attitude tracking error to an arbitrarily small compact set near the origin within a limited time through steps such as designing an adaptive neural state observer, a finite-time command filter, an error compensation mechanism, a virtual control input, and an actual control signal. The present application not only avoids the calculation complexity explosion problem caused by repeated derivation of the virtual control signal in the traditional backstepping method, but also guarantees that the trajectory tracking error of the nonholonomic wheeled mobile robot converges to an arbitrarily small neighborhood within a limited time under the conditions of unknown nonlinear dynamics and unmeasurable speed, and all signals in the closed-loop system are ultimately bounded.

The invention discloses an anti-parameter disturbance bidirectional converter non-linear control method and system, and the method comprises the following steps: building a three-order non-linear dynamic model of a three-phase voltage source converter, defining the reciprocal 1 / Rc of an AC side resistor R and a DC side load resistor in the non-linear dynamic model as an unknown parameter, and calculating the parameter disturbance of the three-order non-linear dynamic model; obtaining a system dynamic equation with parameter uncertainty of the three-phase voltage source converter; designing a state transformation function, linearizing a system dynamic equation, and mapping a state variable x; s30, a tracking error variable is defined as a difference value between the converted state variable and a reference track of the state variable, and the difference value is used for quantifying the deviation between the actual state and the expected state of the system; defining and designing a parameter updating law of the three estimated values, and calculating and updating unknown parameters on line according to the tracking error variable; and designing and calculating a control input law based on the tracking error variable and the three estimated values, and proving the asymptotic stability of the closed-loop system through a Lyapunov function.

This invention discloses a control method for a direct air carbon capture system based on model prediction iterative learning, comprising the following steps: determining the controlled variable and the manipulated variable; constructing a prediction model; designing an MPC module to obtain the MPC control quantity; designing an ILC module to calculate the ILC feedforward control quantity based on the bias correction; synthesizing the total control quantity by superimposing the MPC control quantity and the ILC feedforward control quantity to obtain the total control quantity, which is then applied to the VTSA-DAC system; and performing closed-loop iterative execution, whereby after the end of the current operating cycle, the actual system output is obtained, the periodic error is calculated, the bias correction is updated, and the result is passed to the controller for the next cycle. The above steps are repeated to achieve cross-cycle performance iterative optimization. This invention can solve the problems of strong multivariate coupling, nonlinear dynamic characteristics, and model mismatch in VTSA-DAC systems during periodic operation, and improve the system's anti-interference capability under external disturbances.

The application discloses a kind of atmospheric time delay calculation method based on ray tracing, the present application relates to atmospheric time delay calculation technical field, the specific content of the present application is: obtaining multi-source meteorological data, extracting temperature, specific humidity, three kinds of key meteorological parameters such as air pressure, by optimizing interpolation to complete vertical layering blank and correct grid deviation, to construct three-dimensional atmospheric parameter field of time and space continuity;Based on atmospheric physical characteristics and electromagnetic wave propagation principle, calculate atmospheric refractive index and its gradient;With station location as starting point, set the angle of incidence of electromagnetic wave, adopt nonlinear dynamic step length strategy to track propagation path, and compare actual and vacuum propagation time to obtain time delay result.The present application considers data integrity and timeliness, avoids experience model deviation, and improves the calculation precision of atmospheric time delay.

The invention discloses a control method of a multi-level inverter, a motor driving system and a readable storage medium. The method comprises the following steps: acquiring a system state of the multi-level inverter at a moment k; according to the system state, approaching the nonlinear dynamic state of the inverter system through a neural network; according to the non-linear term prediction value, combining a prediction model of the inverter system to perform system state prediction, and obtaining current prediction values corresponding to different inverter switching states at the k + 1 moment; according to the current predicted value at the k + 1 moment, a current predicted value corresponding to the k + 2 moment is obtained through a system control law; and based on the current predicted values at the k + 1 moment and the k + 2 moment under different inverter switching states, combining the current reference value at the k + 1 moment, evaluating all possible inverter switching states by using a cost function, selecting an optimal switching state which enables the cost function to be minimum, and applying the optimal switching state to the multi-level inverter. According to the method, high-precision control of the multi-level inverter without depending on a precise model is realized.

The present application relates to the technical field of data processing, and provides a task execution optimization method and system based on an environmental protection law enforcement inspection intelligent terminal. The method comprises the following steps: obtaining core performance indexes of the environmental protection law enforcement inspection intelligent terminal, and calculating a comprehensive performance score by using a nonlinear dynamic evaluation algorithm; obtaining ambient light intensity and the temperature of the environmental protection law enforcement inspection intelligent terminal, and calculating a saturation degree dynamic adjustment amount of the screen; calculating a screen contrast dynamic gamma correction amount according to the comprehensive performance score and the ambient light intensity, so as to adjust the brightness and contrast of the screen of the environmental protection law enforcement inspection intelligent terminal; obtaining a task queue to be executed, and evaluating the estimated resource consumption, subsequent dependency degree and historical risk coefficient of each task to be executed, so as to calculate the comprehensive priority weight of each task; and preferentially executing tasks with high comprehensive priority weight.

The invention discloses an electric energy meter early warning method based on phase-space reconstruction, and relates to the technical field of intelligent electric meter detection, and the method comprises the following steps: carrying out the phase-space reconstruction of voltage time sequence data, and obtaining a phase-space trajectory; extracting at least two dynamic characteristic parameters representing nonlinear dynamic behaviors in the voltage measurement loop based on the phase-space trajectory, and constructing a dynamic characteristic vector; state estimation is carried out based on the dynamic feature vectors, the deviation degree of the running state of the electric energy meter is evaluated, and the range of abnormal components is determined based on the estimated values of the feature parameters representing the complexity of the phase space structure; and when the deviation degree meets a preset condition, generating early warning information including an abnormal component range and fault remaining time. The problems that in the prior art, event detection is emphasized, the positioning capacity is insufficient, residual life prediction is difficult to carry out, and robustness is insufficient under complex working conditions are solved.

The invention discloses a method for estimating the state of charge of a lithium ion battery in a low-temperature environment, which belongs to the field of state estimation of the lithium ion battery, and comprises the following steps: collecting charge and discharge data of the lithium ion battery in the low-temperature environment, and constructing a time sequence sample; constructing a data driving model to learn nonlinear dynamic characteristics of the battery, and obtaining a first terminal voltage predicted value; constructing a physical equivalent circuit model to perform online parameter identification, obtaining model parameters and calculating a second terminal voltage predicted value; calculating a fusion weight through a temperature sensing EWMA-Softmax weight distribution algorithm, and performing weighted fusion on the first terminal voltage predicted value and the second terminal voltage predicted value according to the fusion weight to obtain a fusion terminal voltage; and taking the fusion terminal voltage as an observation equation, combining with a state equation formed by an ampere-hour integral method, and inputting into a self-adaptive unscented Kalman filter to carry out SOC closed-loop estimation to obtain a target SOC estimated value. According to the method, the closed-loop accurate estimation of the SOC in different temperature environments can be realized.

The invention discloses a parameter optimization method of a screw propulsion machine for accelerated drainage consolidation of tailings, and belongs to the technical field of drainage consolidation regulation and control of tailings ponds.The method comprises the steps that in-situ tailings samples of the tailings ponds.The in-situ tailings samples of the tailings pondsare collected, and basic physical parameters such as the density, the saturated moisture content and the particle size grading of the tailings are obtained through testing; then respectively constructing interaction mechanical models of the screw propulsion machinery and the tailings under the working conditions of the high-concentration tailings and the low-concentration tailings, analyzing interaction rules of normal bearing, shearing action and driving resistance, and establishing stress balance equations in the vertical direction and the horizontal direction; then, a parameter optimization model taking the mechanical model as a constraint is constructed, differential objective functions for reducing the mechanical subsidence amount and improving the mechanical operation speed are set for the high-concentration tailing working condition and the low-concentration tailing working condition respectively, a PSO algorithm is improved, and Logistic chaos initialization, self-adaptive inertia weight and a learning factor nonlinear dynamic adjustment strategy are fused; and then, an improved PSO algorithm is called for iterative solution to obtain an optimal structure parameter combination of the screw propulsion machinery.

The application relates to a kind of nonlinear system safety-relevant control method and system based on reinforcement learning, method includes the following steps: the affine nonlinear dynamic model with safety constraint is established to target nonlinear system;According to affine nonlinear dynamic model, the safety guarantee control input is calculated based on Liapunov type control barrier function;Unknown system dynamics of target nonlinear system is estimated using neural network identifier, and the estimated value of unknown system dynamics is obtained;The nominal tracking control input is calculated according to the estimated value of unknown system dynamics using reinforcement learning architecture composed of actuator and judge;Safety guarantee control input and nominal tracking control input are combined to generate composite control strategy, and the approximate optimal trajectory tracking of target nonlinear system under safety constraint is realized.Compared with prior art, the comprehensive performance and reliability of safety-relevant control in complex nonlinear system are significantly improved.

The invention discloses an optimal tracking control method for a multi-mode automotive suspension system, and belongs to the technical field of intelligent control of vehicles. Aiming at the problems of dynamic jump, strong uncertainty and multi-actuator cooperative control of an automotive suspension system in various driving modes, the invention provides a robust tracking control framework based on interval type-2 fuzzy Markov jump system modeling and combined with a distributed minimum-maximum game and integral reinforcement learning. The method comprises the following steps: firstly, considering unknown nonlinear dynamics, external pavement excitation and parameter random jump existing in a suspension system, and modeling the system as an interval type-2 fuzzy Markov jump system; secondly, aiming at the actual constraint that the dynamic part of the system is unknown and only output information can be obtained, constructing a distributed control framework; according to the method, the tracking performance, the closed-loop stability and the multi-actuator cooperation efficiency of the suspension system can be guaranteed under the environment that the system modes are randomly switched and the dynamic uncertainty is high, and the smoothness, the safety and the robustness of vehicle driving are remarkably improved.

The invention relates to the technical field of engineering monitoring in a frozen soil region, and particularly discloses a remote monitoring and diagnosis system for a working state of a hot rod in a frozen soil region based on edge calculation, which comprises the following steps of: constructing a thermodynamic state matrix with a space-time correlation characteristic by deploying a multi-source sensor to synchronously collect temperature, pressure and environment wind speed data of a key part of the hot rod; a feature extraction method combining multi-scale decomposition and physical constraint is adopted to generate nonlinear dynamic response features capable of distinguishing environmental interference and real faults; a multi-dimensional diagnosis vector is constructed based on phase-space reconstruction and trajectory analysis, and comprehensive health degree parameters are generated through feature level fusion in combination with real-time environment parameters; a health state classification model with a fuzzy boundary is established, and four-level health level judgment considering the duration effect is achieved; through correlation analysis of environment disturbance and state evolution, an edge-cloud collaborative adaptive optimization mechanism is constructed.

This invention relates to a machine learning-based intelligent control system for molten salt heating, belonging to the field of thermal energy storage and intelligent control. It includes a data acquisition module, a machine learning prediction module, and a heating control module. The data acquisition module acquires multi-dimensional state data within the molten salt storage tank in real time, including temperature, liquid level, flow rate, and pressure distribution information. The machine learning prediction module constructs a nonlinear dynamic prediction model based on a long short-term memory network to accurately predict temperature change trends over a set future time period. The heating control module dynamically adjusts the heater's output power using an adaptive fuzzy PID control algorithm based on the deviation between the prediction result and the target temperature. This overcomes the temperature fluctuation problems caused by large delays, nonlinearity, and thermal inertia in traditional molten salt heating processes, improves the accuracy and stability of temperature control, significantly reduces the overall energy consumption of the system, and extends the service life of the heating equipment.

The invention discloses a fuel cell health state prediction method, system and device based on space-time modeling and a graph neural network, and a storage medium, and belongs to the technical field of industrial automation and energy system optimization. According to the invention, by constructing a sparse label extension mechanism, a multi-channel correlation feature extraction system and a nonlinear dynamic mapping strategy of a hybrid architecture, the limitations of a traditional data driving and physical modeling method in the aspects of label scarcity, insufficient multi-channel correlation mining, limited nonlinear modeling capability and the like are effectively overcome; reliable technical support is provided for evaluation and predictive maintenance of the state of health of the fuel cell, and improvement of equipment reliability and system energy efficiency is facilitated.

The invention provides a concrete water and soil chemical heat release calculation method and system based on layered pouring and a nonlinear dynamic heat source, and belongs to the technical field of concrete hydration heat calculation. A mass concrete structure is dispersed into a regular lattice system, and layered pouring is simulated by controlling the activation state of lattices; a multi-component cement hydration heat model coupled with the real-time temperature is embedded in each active lattice point to serve as a dynamic heat source; and the lattice Boltzmann method is utilized to calculate the conduction of heat between lattices and the dissipation at the boundary, so that the accurate temperature field distribution of the mass concrete structure in time and space is obtained. The lattice points are activated in a layered manner, and heat release attributes and heat dissipation attributes are stored in the lattice points, so that the problem that heat dissipation boundary conditions of concrete are changed due to introduction of a new dynamic heat source is solved, and accurate simulation of the heat transfer history of the system is realized.

The application discloses a satellite solar cell array rotation angle estimation method, which comprises the following steps: step 1, establishing an earth inertial coordinate system, a satellite body coordinate system and a solar cell array coordinate system, and defining a satellite solar cell array rotation angle; step 2, establishing a state equation of an augmented state composed of a sun vector, the solar cell array rotation angle and a gyro constant drift noise; step 3, calculating an augmented state quantity estimation value by using an extended Kalman filter; and step 4, calculating a satellite solar cell array rotation angle estimation value. The method fuses gyro angular velocity and sun sensor vector data, constructs a nonlinear dynamic model containing a sun vector, a rotation angle and a gyro bias, and designs an EKF algorithm to estimate the solar cell array rotation angle in real time, thereby solving the strong dependence of a traditional scheme on a star sensor and a mechanical sensor, and significantly improving the autonomous recovery capability and energy guarantee reliability of a satellite in an extreme fault scene.

The invention belongs to the field of intelligent control of mining equipment, and provides a self-adaptive asymptotic tracking control method for an uncertain electro-hydraulic servo system based on event triggering in order to solve the problem that the performance of the electro-hydraulic servo system is poor under complex working conditions. The core of the technical scheme is that unknown parameters and disturbance in the system are uniformly estimated and compensated through a single-parameter adaptive law, and a time-varying integral bounded function is introduced to ensure asymptotic convergence of tracking errors; a nonlinear dynamic surface filter is adopted to simplify the controller structure, and differential explosion is avoided; dynamically updating a control signal in combination with an event trigger mechanism to save communication resources; and an unknown control gain direction problem is processed by using a Nussbaum function. According to the method, nonlinearity and uncertainty of the system are effectively overcome finally, high-precision and high-robustness asymptotic tracking control is realized while network bandwidth and computing resource consumption are remarkably reduced, an accurate mathematical model is not needed, and the method has very high engineering practicability and universality.

The invention belongs to the technical field of deepwater oil and gas field drilling gas cut monitoring, and particularly relates to a deepwater drilling gas cut monitoring method. According to the monitoring method, nonlinear dynamic characteristics of ultrasonic signals are extracted through multi-scale fuzzy divergence entropy, and a gas content value is obtained based on inversion of a support vector machine model subjected to sample training, so that quantitative monitoring and early warning of tiny changes of the gas content are realized; compared with an existing deepwater drilling gas cut monitoring method, the method has the remarkable advantages in the aspects of monitoring precision, dynamic response characteristics, noise interference resistance and the like, and accurate recognition and real-time quantitative monitoring of early weak gas cut signals can be achieved. A deepwater drilling gas cut monitoring method comprises the following steps that ultrasonic echo signals of gas-liquid two-phase flow are collected; extracting a multi-scale fuzzy divergence entropy value in the ultrasonic echo signal; and inputting a feature vector in the multi-scale fuzzy divergence entropy into the trained support vector machine classification model to obtain a gas content recognition result.

The invention relates to the technical field of computers, in particular to a variable damping control method and device based on a continuous friction model. The method comprises the following steps: acquiring construction parameter data of a continuous friction model; wherein the constructed parameter data comprises a nonlinear dynamic coefficient, coulomb friction, maximum static friction, turning speed, a viscous friction coefficient and an actual rotating speed of the motor; constructing a continuous friction model based on the construction parameter data; based on a continuous friction model, determining a differential function of friction torque to speed; determining a variable damping coefficient based on the differential function of the friction torque to the speed and a preset adjustment principle of an additional damping coefficient; determining the voltage of a permanent magnet synchronous motor of the space optical pointing servo system based on the variable damping coefficient; and controlling the permanent magnet synchronous motor of the space optical pointing servo system based on the voltage of the permanent magnet synchronous motor. Through the configuration mode, the control precision of the space optical pointing servo system can be improved.

The invention relates to a network connection vehicle fault performance self-healing control method and system considering physical constraints in the technical field of intelligent traffic and vehicle control. The method comprises the following steps: constructing a heterogeneous networked vehicle nonlinear system model based on nonlinear dynamics of displacement, speed and acceleration, actuator fault influence and external disturbance; constructing an auxiliary dynamic variable representing the overall influence of the fault, deducing a time derivative of the auxiliary dynamic variable, designing a self-adaptive fault influence identifier, and carrying out the online identification of the system fault influence; constructing a lower definite bound parameter associated with the unknown control coefficient, designing a parameter boundary estimator, and performing unknown parameter boundary adaptive estimation; and on the basis of fault influence identification and parameter boundary estimation information, a physical constraint function of the actuator is integrated, a self-healing controller of the networked vehicle formation is designed, and fault influence compensation, physical constraint guarantee of the actuator and stable control of the networked vehicle formation are realized. And the reliability and the control precision of the networked vehicle cooperative control system under complex working conditions are improved.

The invention relates to the technical field of motor control, and discloses a disturbance compensation control method of a servo driver, which comprises the following steps: acquiring the operation state of a servo motor to construct a port controlled Hamiltonian kinetic model, and extracting an interconnection matrix and a dissipation matrix; constructing a fractional order superhelix expansion state observer, and obtaining an initial lumped disturbance estimated value; extracting Hamiltonian energy dissipation residual errors to carry out adaptive adjustment on the fractional order superhelix expansion state observer, and outputting a high-fidelity disturbance estimation value; constructing a nonlinear dynamic damping matrix based on the high-fidelity disturbance estimation value to perform energy shaping on the servo motor; and a matching equation is established in combination with the nonlinear dynamic damping matrix and the port controlled Hamiltonian kinetic model, and a compound control law is solved to generate a driving signal to control the servo motor. According to the method, the observer is dynamically adjusted by using the energy residual error, and the damping topology is reconstructed, so that high-frequency chattering and transient oscillation are inhibited, and the anti-interference capability of the system is improved.

The present application belongs to the field of ultrafast laser and nonlinear optics, and particularly relates to a near-infrared few-cycle pulse generation system and method based on high-pressure nonlinear enhancement. The method fills inert gas into a hollow-core anti-resonant optical fiber and adjusts the gas pressure to the high-pressure interval of 20 bar-90 bar, enhances the nonlinear coefficient of the gas to realize the dispersion and nonlinear dynamic balance of the pulse in the waveguide, thereby compensating for the insufficient peak power of the low-energy pump pulse. The pulse feedback module collects the output time-domain intensity waveform and extracts the main peak energy proportion and the splitting factor as feedback indexes. The control module diagnoses the offset state of the self-compression focal point relative to the truncated end face according to the feedback indexes, controls the small-step pressure regulation of the gas pressure regulation module to anchor the focal point on the truncated end face, and directly outputs high-quality and high-coherence near-infrared few-cycle pulses without external dispersion compensation devices.