459 results about "System dynamics model" patented technology

The invention relates to a control method and device for wind load resistance of a mooring unmanned aerial vehicle and a storage medium. The method comprises the following steps: collecting and modeling a mooring unmanned aerial vehicle to obtain a system dynamics model; state estimation is carried out to obtain system wind load disturbance data and expected attitude data; performing optimal control solution on the plurality of mooring points to obtain tension distribution control data of each mooring point; inputting into a discrete time neural network controller for attitude compensation calculation to obtain thrust vector compensation control data; performing adaptive robust control on the attitude of the mooring unmanned aerial vehicle to obtain a rotating speed control instruction of each motor of the four rotors; and performing safety threshold judgment, and when any monitoring data exceeds a preset threshold, outputting a working mode switching instruction and executing safe landing control. According to the invention, a multi-stage safety protection mechanism is established, and reliable operation of the mooring unmanned aerial vehicle under extreme conditions is ensured through real-time monitoring and mode switching.

The invention discloses a source network load storage system cooperative regulation and control method and system, and the method comprises the steps: employing a high-precision sensor and multi-protocol communication to obtain system multi-dimensional data through global data collection and preprocessing, and carrying out the noise reduction; constructing a dynamic association model based on a graph neural network, and accurately capturing a system node relationship in combination with a multi-head attention mechanism and topological constraints; layered multi-objective decision, reinforcement learning real-time regulation and control, layered control architecture and closed-loop feedback correction are adopted, and economic optimization, safety guarantee and strategy iteration are considered. The system is composed of a global data sensing unit, a system dynamic modeling unit and the like, and all the units are in close cooperation. According to the method and the system, the defects of insufficient data processing, low model precision, single regulation and control and the like in the prior art are effectively overcome, the system fault prediction accuracy can be improved, the carbon emission is reduced, the power fluctuation coping capacity is enhanced, and the operation efficiency and the stability of the source network load storage system are remarkably improved.

The invention relates to a V2G-based alternating current charging power adjusting method and device. The method comprises the following steps: carrying out multi-scale data acquisition and preprocessing on a V2G alternating current charging system to obtain a standardized system state data set; constructing a system dynamic model comprising a power grid voltage characteristic and a charging power characteristic; carrying out decomposition to obtain a power output mapping relation calculation, and obtaining a power modulation control parameter; establishing a fast and slow time scale coordination control framework, and designing a power regulation agent and a voltage regulation agent to obtain a coordination control strategy; checking a charging power limit value constraint, a voltage limit value constraint and a power factor constraint to obtain a constraint violation judgment result, and executing constraint projection calculation to obtain a target control instruction; and controlling a power conversion circuit of the charging pile to execute power regulation operation. According to the invention, a hierarchical control structure is adopted, the control complexity of the system is reduced, and the robustness and the adaptive ability of the control strategy are improved.

The invention discloses a plain water network area runoff simulation method which comprises the following steps: acquiring and preprocessing multi-source data to obtain a time-space consistency hydrological data set; based on the time-space consistency hydrological data set, obtaining polder area water level data and exchange flow data by adopting an enhanced polder system dynamic model; carrying out runoff production calculation in the plain area by adopting an improved XAJ model; constructing a multi-scale river network topological structure, and executing multi-directional flow state identification and prediction of deep space-time fusion; and constructing a time-varying structure adaptive BP neural network model to realize runoff simulation of the plain water network area. According to the method, the technical problems of reverse flow phenomenon identification in a plain water network area, polder area water exchange threshold effect simulation, distribution node dynamic distribution coefficient optimization, temporary moving pump station influence correction and the like can be effectively solved.

The invention discloses a coal mine pressure risk assessment method based on complex system dynamics, belongs to the technical field of mine pressure risk assessment, is used for mine pressure risk assessment, and constructs a complex system dynamics model, combines various sensor data, and carries out real-time monitoring and risk assessment of mine pressure changes. The method comprises the steps of geological data acquisition, sensor data real-time monitoring, data preprocessing, mine pressure prediction model construction, model training and optimization, real-time risk assessment and the like, data of a stress sensor, a displacement sensor and an acoustic emission sensor are comprehensively utilized, and data preprocessing is performed by adopting a Kalman filtering algorithm; and optimizing the mine pressure prediction model by using a graph convolutional network and an attention mechanism, and performing real-time risk assessment through an adaptive graph neural network. According to the method, the adaptive graph neural network is adopted for real-time risk assessment, potential risks in mine pressure changes can be found in time, risk scores are generated, and scientific decision support is provided for coal mine managers.

The invention belongs to the technical field of medical robot control, and discloses a physiotherapy robot self-adaptive control method and system based on multi-mode biological feedback, and the method comprises the steps: carrying out the preprocessing of a biomechanical sensing signal obtained in advance, carrying out feature extraction and dynamic weight fusion on the preprocessed biomechanical sensing signal by utilizing wavelet transform and frequency domain transform to obtain a joint feature; establishing a system dynamics model based on a Lagrange equation, generating a time-varying impedance trajectory model by utilizing a joint state acquired in real time and combining a rolling optimization algorithm, and dynamically adjusting impedance parameters of the time-varying impedance trajectory model according to joint features to generate a safe motion trajectory; and performing double closed-loop control on the physiotherapy robot according to the safe movement track and a biomechanical sensing signal obtained in real time to obtain a control signal of the physiotherapy robot, and realizing joint motor driving of the physiotherapy robot based on the control signal. The device can be flexibly changed according to individual differences of patients and rehabilitation processes.

The invention discloses a secondary ripple optimization method based on star cascade H bridge injection three-time zero sequence voltage. The method comprises the following steps: acquiring real-time voltage and current data of a system, performing dynamic harmonic analysis on the acquired data, generating a harmonic compensation signal, and generating an optimized control signal; constructing a multi-objective optimization model based on the optimized control signal and pre-stored system parameters, solving the model to obtain a Pareto optimal solution set, and selecting an optimal cubic zero-sequence voltage parameter from the Pareto optimal solution set; based on the optimal cubic zero-sequence voltage parameters, constructing a system dynamic model, constructing an adaptive prediction controller, generating an optimal control sequence, and performing closed-loop feedback adjustment; the ripple suppression effect is evaluated, the ripple suppression proportion is calculated by comparing the secondary ripples before and after the third zero-sequence voltage is injected, and the control parameters are dynamically adjusted according to the result. And the adaptability and precision of harmonic analysis are greatly improved. The problem that secondary ripples rise when the star cascade device compensates reactive power is effectively solved.

The invention discloses a building equipment fault rapid attribution and self-optimization method, which relates to the field of intelligent operation and maintenance of building equipment, establishes a multi-dimensional association relationship among equipment, building space, sensors and fault modes, and deeply combines a fault attribution engine with system dynamics. Data driving flexibility and physical logic preciseness are simultaneously realized in fault attribution, and reasoning from abnormal data to root cause analysis is realized. According to the method, the optimization efficiency is remarkably improved through a specific incremental learning / local updating mode, the weight of the knowledge graph and the Bayesian network conditional probability table are adjusted according to the maintenance result, physical equation parameters are calibrated, and dynamic updating of node attributes of the knowledge graph is achieved; through dynamic weight optimization, the adaptive ability of real-time reasoning confidence evaluation and scene context is improved; a physical equation is used as an executable knowledge unit to be embedded into a knowledge graph through establishment and deep integration of a system dynamics model, and a safety and reliability mechanism is designed for verification.

The invention provides an unmanned surface vessel cluster system finite time distributed formation method, which not only can ensure that an unmanned surface vessel system reaches an expected formation mode in finite time, but also can ensure that the system speed and the formation error always meet the time-varying constraint requirement in the whole operation period. The method comprises the following steps: establishing kinematics and dynamics models of the three-degree-of-freedom unmanned surface vessel; using a smooth function to approximate saturation constraints of the system, and combining with the fault model to establish a new system dynamic model limited by driver faults and saturation constraints; designing a virtual control law, also known as a kinematics guidance law; designing a parameterized channel rate of the virtual leader; designing a finite time distributed formation controller; and designing an uncertain compensation adaptive law. The method can design a time-varying formation mode and a time-varying constraint function according to the needs of a user, dynamically adjusts the formation form of multiple unmanned surface vessels, and guarantees that the speed and formation tracking errors are not deviated from a specified constraint set all the time.

The invention belongs to the related technical field of multi-agent control, and particularly discloses a predetermined performance consistency tracking control method and system for a nonlinear multi-agent system, and the method comprises the steps: constructing a Lyapunov function based on the consistency tracking error of the multi-agent system, and obtaining a control law; consistency tracking control of the multi-agent system is realized according to the control law; wherein the system state quantity in the control law is estimated through a distributed state observer; the distributed state observer is designed through the following method: according to a universal approximation principle, a nonlinear function in a multi-agent system dynamic model is approximated based on a fuzzy logic system, and then the distributed state observer is obtained by combining the multi-agent system dynamic model. According to the method, the precision and robustness of the consistency tracking process of the multi-agent system can be remarkably improved.

The invention relates to the field of four-rotor control, and discloses an optimal trajectory planning method for a four-rotor suspension payload system, which comprises the following steps of: establishing a four-rotor suspension payload system kinetic model; for unknown lumped disturbance in the established model, estimating the lumped disturbance through a predefined time nonlinear disturbance observer to obtain a compensated nominal model; based on the real-time positions of the four rotors, a dynamic window particle swarm optimization algorithm is adopted in the detection range of a sensor carried by the four rotors to minimize a cost function about path points, and an initial optimal track is found; and taking the compensated nominal model as a new prediction model, taking the initial optimal trajectory as a reference trajectory, and using nonlinear model prediction control to minimize a cost function so as to obtain a prediction trajectory meeting system constraints. According to the method disclosed by the invention, the load transportation reliability and stability can be improved, and the control precision is effectively ensured.

The invention provides a bidirectional DC / DC converter parameter identification method based on physical information machine learning, and relates to the technical field of parameter identification. The method comprises the steps of firstly completing establishment of a system dynamic model of the bidirectional DC / DC converter, secondly utilizing an implicit Runge-Kutta method to construct a relationship between a data intermediate state and an initial state as well as a relationship between the data intermediate state and an end state, applying the relationship to the system dynamic model, then inputting historical training data into a deep neural network to obtain the intermediate state of the data, and finally establishing a system dynamic model of the bidirectional DC / DC converter. And substituting the physical model as an input to realize seamless coupling of the physical model into training of the deep neural network. And then the trained PINN is used to realize online parameter identification. The hybrid design can seamlessly combine the data of the BDDC physical model, and effectively modulate the training stage, so that the training stage is forced to be executed to the underlying physical principle and constraint of the converter, and the required training data is greatly reduced.

The invention provides an optimal configuration method of an off-grid wind power hydrogen production system considering full life cycle income, which comprises the following steps: receiving original data, carrying out abnormal value detection and missing value repair, obtaining multi-scale component data through empirical mode decomposition, and generating a time sequence characteristic matrix; extracting equipment performance characteristic parameters and establishing a performance prediction model; constructing a system dynamic model based on a Bond Graph method, and performing stability analysis; calculating all costs and system income of the whole life cycle, and generating a comprehensive evaluation matrix in combination with risk analysis; a multi-objective optimization function is constructed, a grey wolf algorithm is adopted for solving, and an optimal configuration scheme is generated; and performing simulation verification and sensitivity analysis on the optimal configuration scheme to generate a final configuration scheme. According to the method, the equipment performance attenuation law and the system dynamic characteristics are fully considered, and the accuracy and reliability of optimal configuration are improved.

The invention discloses an energy-saving ground source heat pump control method and system, and the method comprises the steps: building a ground source heat pump system dynamic model which comprises an indoor heat balance model, a ground source heat pump unit model and a soil heat exchange model, obtaining a topological network through coupling, designing a distributed control and fault-tolerant operation strategy according to the topological network structure, and carrying out the control of the ground source heat pump system. Each node is locally controlled according to the position and adjacent node information, a state space model is constructed, an optimal control action sequence is solved through quadratic programming to balance energy consumption and an indoor and outdoor temperature deviation objective function, the optimal operation state is obtained, and the mapping relation between the optimal operation state and environment parameters is learned through a neural network; a ground source circulation flow control algorithm is designed, and circulation pump flow distribution and unit working parameter automatic adjustment are achieved. According to the method, the control precision and stability of the ground source heat pump system can be effectively improved, reliable operation of the ground source heat pump system under complex working conditions is ensured, the energy utilization efficiency is remarkably improved, energy conservation and consumption reduction are achieved, and meanwhile good interpretability is achieved.

The invention belongs to the field of space manipulators, and relates to a vibration suppression control method and system for a flexible base space manipulator, and the method is based on a Lagrangian method and considers multiple coupling effects to completely establish a dynamic model of the flexible base space manipulator system. An inertia matrix estimation item is constructed based on a parameter identification method to realize motion decoupling of the space manipulator, and a control law is designed based on a fast and slow subsystem theory to realize high-precision control of an assembly task of a space manipulator module and vibration suppression of a flexible base. And mechanical arm fine assembly operation control and base vibration suppression control for the flexible base space mechanical arm are achieved. The problem of how to complete a high-precision assembly task on the basis of restraining vibration of the base of the double-space mechanical arm is solved.

The invention discloses a vehicle-mounted unmanned aerial vehicle visual servo precise landing control method considering view field constraints, and the method comprises the following steps: employing virtual plane feature points to form an image moment feature value, and building a decoupling system dynamics model integrating unmanned aerial vehicle dynamics and image dynamics; according to the image shot by the camera at the current moment and the motion state of the unmanned aerial vehicle, acquiring the system state at the current moment; outer ring control and inner ring control are set based on a model prediction control method, an optimization target is set in the outer ring control, attitude constraints are set in the inner ring control, the system state at the subsequent moment is predicted based on the system state at the current moment, and the optimal control at the current moment is obtained to serve as system input; and performing flight control on the unmanned aerial vehicle based on system input. The method disclosed by the invention ensures that the view field visibility is met in the autonomous landing process of the unmanned aerial vehicle.

The invention discloses a multi-unmanned-aerial-vehicle cooperative hoisting method and system, and the method comprises the steps: firstly, enabling each unmanned aerial vehicle to operate an extended state observer, regarding internal and external disturbances (such as airflow, unknown load parameters, internal coupling force and the like) as a lumped disturbance, and carrying out the real-time estimation; and then, through a communication network operation consensus algorithm between the unmanned aerial vehicles, fusing the local disturbance estimation values of the unmanned aerial vehicles into a global consistent system-level disturbance estimation. And finally, each unmanned aerial vehicle actively counteracts the influence of disturbance by using the accurate disturbance estimation value in a feedforward compensation controller, and accurately tracks the respective target trajectory in combination with feedback control. According to the method, active observation of disturbance, distributed fusion and feed-forward compensation are combined, dependence on an accurate system dynamic model is eliminated, and trajectory tracking accuracy, stability and stability of the system in a complex environment are remarkably improved.

The invention provides a vehicle dynamics performance analysis method and system considering suspension parameter performance degradation, and relates to the technical field of railway vehicle dynamics, and the method comprises the steps: building a nonlinear Maxwell hydraulic damper model; establishing a vehicle system dynamics model in simulation software based on a vehicle dynamics theory; integrating the nonlinear Maxwell hydraulic damper model into a vehicle system dynamic model, and establishing a vehicle system nonlinear dynamic model considering suspension parameter performance degradation; setting model parameters of the nonlinear Maxwell hydraulic damper to obtain a simulation result; and analyzing the dynamic performance evaluation index of the vehicle so as to complete dynamic performance evaluation of the vehicle under the conditions of actual service mileage and environment temperature. The method effectively reflects the influence of suspension parameter degradation on the dynamic performance of the vehicle, improves the precision of a dynamic simulation model of the railway vehicle and the reasonability of an analysis method, and has important theoretical research significance for innovative design and intelligent operation and maintenance of the railway vehicle.

The invention relates to the technical field of agricultural development, and discloses a characteristic rural industry green planning system combined with economic analysis, and the system comprises a data collection and analysis module which is used for collecting and integrating policy information, environment data, economic data and market demand information; the environment evaluation and grading module is used for dividing the rural industrial area into a green multi-dominant production area, a pollution-free multi-dominant production area, a green less-dominant production area and a pollution-free less-dominant production area; the industrial planning module is used for designing production, processing, packaging and supply chain schemes of characteristic agricultural products and constructing a brand construction scheme in combination with regional historical and cultural characteristics; the economic analysis module is used for analyzing economic benefits of the industry, evaluating risks and providing an optimization scheme; and the intelligent auxiliary decision-making module simulates economic and ecological influences of different planning schemes. According to the method, intelligent and dynamic planning is realized through reinforcement learning, linear planning and a system dynamics model, and the scheme is quickly adjusted according to real-time environment, economy and policy data.

The invention discloses a regional drought toughness simulation analysis method and device, relates to the technical field of regional drought toughness analysis, and aims to solve the problem of low regional drought toughness prediction precision of a static analysis model in the prior art. The method comprises the steps of obtaining observation data of a target drought toughness system corresponding to a target area; based on a preset system boundary and observation data, constructing a regional drought toughness system dynamic model corresponding to the target drought toughness system; constructing an initial drought toughness evaluation index system based on the causal relationship cycle diagram and the relationship between the stock and the flow; based on a preset simulation scene, carrying out scene simulation by adopting the regional drought toughness system dynamics model and outputting a corresponding dynamic change result; and adjusting the initial drought toughness evaluation index system based on a dynamic change result to obtain a target drought toughness evaluation index system. The method is used for improving the prediction precision of the regional drought toughness.

The invention discloses a constraint following control method of a two-arm collaborative robot based on differential homeomorphic mapping, and relates to the field of robot control. According to the method, a nominal dynamical model of a robot is established, and a geometric constraint equation is defined according to a cooperative task; establishing global differential homeomorphic mapping by constructing constraint tracking variables and complementary coordinates, and decoupling a system dynamics model into a constraint tracking subsystem and a complementary subsystem; designing a robust adaptive controller fusing sliding mode control and an adaptive law, and estimating and compensating the uncertainty of the system on line; according to the method, the constraint processing complexity is effectively simplified through differential homeomorphic mapping, the constraint tracking precision and robustness of the system under model uncertainty and external interference are remarkably improved, and high-precision and high-stability cooperative task execution of the two-arm robot in the dynamic environment is achieved.

The invention discloses a cross-domain cluster system distributed safety formation control method in an urban interference environment, and the method comprises the steps: constructing a distributed elastic observer to estimate the state of a virtual leader, and providing a formation output reference for cooperative formation tracking control; the augmentation system of the air-ground unmanned cluster system is constructed by combining the state data of the distributed elastic observer and the state data of the air-ground unmanned cluster system, and a control gain matrix is constructed by using a feedforward-feedback control method. Initial excitation is applied to an augmentation system of the air-ground unmanned cluster system, when operation time meets an initial excitation condition, state data of operation of the air-ground unmanned cluster system is collected, optimal formation tracking control is determined by using an online double-integrator reinforcement learning method, and optimal formation tracking control of the air-ground unmanned cluster system is realized. According to the method, the optimal safe formation tracking control of the cross-domain air-ground unmanned cluster system can be realized under the conditions that the dynamic model of the system is unknown and the distributed denial of service is attacked.

The invention relates to the technical field of data signal processing, in particular to a conductivity meter data processing method based on sage-husa self-adaptive Kalman filtering, which comprises the following steps of: sampling conductivity and temperature original data of a measured object through a conductivity sensor probe; an improved sage-husa adaptive Kalman filtering model of the conductivity meter is established; the method comprises the following steps of: transmitting original data measured by a probe of a conductivity sensor into a sage-husa self-adaptive filter, and performing filtering and noise reduction processing on an original signal by using an improved sage-husa self-adaptive Kalman filtering algorithm; the sage-husa adaptive filter can dynamically adjust filter parameters according to the change of the system state and the difference of noise characteristics, a system dynamic model can be well reflected without setting an accurate initial value so as to obtain more accurate estimation data, the robustness of the system is enhanced through outlier judgment introduced during innovation calculation, and the robustness of the system is improved. The measurement precision is effectively improved, the risk that parameters are affected by outliers is reduced, and the real-time performance of the system is enhanced.

The invention discloses a mechanical arm iterative learning control method based on a radial basis function network. The method solves the limitation that a traditional mechanical arm control method deals with nonlinear characteristics and external disturbance problems in a complex dynamic environment. The method provided by the invention comprises the following steps: firstly, constructing a mechanical arm system dynamic model, and dynamically correcting and optimizing a reference track by adopting a dynamic correction strategy; then designing a radial basis function neural network to construct a nonlinear compensation item, and designing a dynamic weight parameter and a dynamic learning gain for optimizing the performance of the controller; and finally, designing an iterative learning controller, and verifying the stability and error convergence of a control algorithm. Through the radial basis function network, the dynamic adjustment strategy and the iterative learning strategy, the control precision, the adaptive capacity and the error convergence speed of the mechanical arm system are improved.

The invention relates to a transformer comprehensive safety assessment method and system based on symbol regression. The method comprises the following steps: acquiring monitoring variables of an electrical signal dimension, an equipment state dimension and an operation environment dimension of a transformer; based on the maximum mutual information coefficient, screening the dimension monitoring variables to obtain a plurality of monitoring variable sets with closely associated variables; performing sign regression on the plurality of monitoring variable sets to obtain a nonlinear function expression between any reference monitoring variable and other monitoring variables in the monitoring variable sets, namely a mutual check function, and realizing sign regression by using a genetic algorithm; establishing a single-variable fault probability function according to the influence of the multi-dimensional monitoring variable on the transformer equipment; and building a system dynamics model based on the mutual verification function and the fault probability function to realize comprehensive safety evaluation of the transformer. Compared with the prior art, the method has the advantages of improving the safety assessment level of the transformer, being high in interpretability and the like.

The invention discloses a lower limb exoskeleton control system and method based on phase recognition and model prediction control. The system comprises a kinematics modeling module, a gait database module, a data processing and feature extraction module, a gait phase recognition module, a dynamics modeling module, a model prediction controller and an actuator control unit. By recognizing the gait phase and dynamically adjusting and controlling the target, precise assistance of the natural gait of the wearer is achieved. The system calculates the trajectory of the ankle joint relative to the hip joint in real time based on a kinematic model, and constructs a database in combination with gait data collected under multiple working conditions to provide an expected trajectory and reference parameters for control. In the dynamic modeling process, motor driving torque and man-machine interaction torque are introduced, a system dynamic model is established in a unified mode, and control characteristics under different gait phases are reflected.

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.

The invention discloses a reel-to-reel multilayer printing control method based on improved simulated annealing optimization LQR, and belongs to the field of multilayer printing flexible electronics. According to the reel-to-reel multilayer printing flexible electronic control method based on the improved simulated annealing optimization LQR, initial LQR controller design is carried out on an overprinting system according to a reel-to-reel overprinting system dynamic model, and a simulated annealing algorithm is selected to carry out LQR controller optimization; and self-adaptive annealing, dynamic step length, mixed neighborhood search and other methods are used to make up for the defects of slow convergence speed, difficult temperature control and the like of the traditional simulated annealing algorithm, so as to obtain the optimal control quantity and improve the overprinting precision and control stability of the system. The control quantity optimization of the overprinting system is realized, the control stability of the overprinting system is ensured, and the overprinting precision is improved.

The invention discloses a steer-by-wire system self-adaptive steering angle tracking strategy based on a high-order all-drive theory, which comprises the following steps of: 1, establishing a kinetic model of a steer-by-wire system according to the steer-by-wire system of a passenger vehicle, and determining a state variable and a control variable; 2, designing an extended disturbance observer according to the kinetic model established in the step 1, and observing the internal and external uncertainty and disturbance of the system, and compensating the uncertainty and disturbance to a controller; 3, converting the steering-by-wire system dynamics model in the step 1 into an all-wheel-drive system model, and designing a self-adaptive high-order all-wheel-drive controller; and 4, verifying the stability of the high-order all-wheel-drive controller designed in the step 3 according to a Lyapunov stability method. According to the method, uncertainty interferences inside and outside the system are considered in the built steer-by-wire system model, the interferences are lumped, the extended disturbance observer is used for observation and compensation to the controller, and the accuracy and robustness of the control system are improved.

The invention discloses an unmanned system dual-time scale security cooperative control method and system under FDI attacks, and relates to the technical field of unmanned system security collaboration.The method comprises the steps that a communication topological graph and an unmanned system dynamic model with dual-time scale characteristics are established firstly, then a bounded FDI attack model and a dynamic observation error model are established, and the dynamic observation error model is established; the method comprises the following steps of: firstly, carrying out fast and slow decomposition on a follower, constructing a distributed elastic observer, then analyzing sufficient conditions of observation error convergence, secondly, carrying out fast and slow decomposition on the follower, constructing a novel composite controller and a dynamic output error system model, and finally, analyzing sufficient conditions of consistent adjustment output of a leader and the follower under FDI attack. According to the method, the ill-conditioned numerical problem of an adjustment equation caused by the dual-time scale characteristic is avoided, the FDI attack is eliminated in real time and is not influenced by the attack occurrence time and probability, and the output consistency of the leader and the follower in the unmanned system is effectively realized.