533 results about "Linear system" patented technology

The invention belongs to the field of computer graphics, and discloses a fast parallel multi-scale smooth symbol distance curved surface reconstruction method and system, and the method comprises the steps: carrying out the local fitting of point cloud data through employing a moving least square method, and calculating the Gaussian curvature of a point cloud; constructing a curvature adaptive octree based on Gaussian curvature, wherein the octree is used for dynamically adjusting local resolution and capturing geometric details to improve curved surface reconstruction precision; global fitting is carried out through an implicit function, and a zero contour surface is obtained through an octree contour surface extraction algorithm; and multiple threads are introduced in the process of iteratively solving the linear system to carry out parallel computing. The adaptive octree based on Gaussian curvature can effectively capture complex geometric details, and the algorithm realizes high-precision curved surface reconstruction under different scales. In addition, the local and overall calculation efficiency is remarkably improved through parallel calculation, and the high efficiency of the algorithm in large-scale data processing is ensured. The method provided by the invention provides an efficient and accurate solution for complex curved surface reconstruction.

The invention discloses an iterative learning control optimization method for a direct current motor with non-repetitive disturbance, and relates to the technical field of direct current motor control, and the method comprises the steps: lifting a nominal model of a nonlinear system to a high-dimensional linear space through a deep Koopman operator, and obtaining the description of a high-dimensional state space model of the nonlinear system; converting the state space model into a linear repetition process model, and obtaining a linear matrix inequality condition of system convergence in a high-dimensional space by using a two-dimensional iterative learning control method; further, the intelligent agent is trained in a high-dimensional state space model iterative learning control system with model mismatch and a non-repetitive uncertainty nonlinear system, and finally the intelligent agent is applied to an actual system. The control law gain obtained by the method can better adapt to an original nonlinear system, a deep reinforcement learning algorithm is designed to compensate non-repetitive disturbance, and zero-error tracking of expected output is basically achieved.

The invention discloses a digital twin-driven machine tool adaptive control method and system and a machine tool control system, and the machine tool control system comprises a data collection unit, a data transmission unit, a twin model unit, a data processing unit, a control decision unit, an adaptive algorithm unit, a control execution unit, a machine tool execution unit and a feedback monitoring unit. The output ends of the data acquisition unit, the twin model unit and the data processing unit are connected with the input end of the data transmission unit; the output end of the data transmission unit is connected with the input end of the control decision unit; according to the method, the control strategy is adjusted in real time through the self-adaptive control algorithm to cope with the change of system parameters and external disturbance, and the advantages of dynamic response capability, robustness enhancement, performance optimization, manual intervention reduction, nonlinear system processing, safety improvement, complex task implementation and the like are provided. The advantages enable the adaptive control algorithm to become an important control means in many modern engineering applications.

The invention discloses a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation Satellite System) anti-deception navigation method based on ESKF (Enhanced Shift Keying Function) and dynamic window detection, which comprises the following steps of: 1, acquiring observation data of an INS and a GNSS, performing time synchronization and coordinate system unification on the observation data of the INS and the GNSS, and respectively eliminating a time error and a coordinate difference; 2, a dynamic window detection mechanism is designed, an error state dynamical model is constructed, the dynamic window detection mechanism is used for detecting whether GNSS cheating exists or not, and meanwhile the error state dynamical model can output stable navigation parameters; and 3, outputting a final high-precision navigation result according to the stable navigation parameters. According to the method, the problems of error accumulation and covariance distortion of nonlinear system model linearization are effectively solved, pollution of GNSS deception signals can be effectively inhibited, high-precision positioning and anti-deception capability are kept in complex environments (such as urban shielding and electromagnetic interference), and a navigation system with high precision and high reliability is formed.

A hybrid quantum-classical (HQC) computer system, which includes a classical computer and a quantum computer, solves linear systems. The HQC computer system splits the linear system to be solved into subsystems that are small enough to be solved by the quantum computer, under control of the classical computer. The classical computer synthesizes the outputs of the quantum computer to generate the complete solution to the linear system.

The embodiment of the invention provides a parameter acquisition method and device, a medium and a vehicle, and relates to the technical field of vehicles. According to the parameter acquisition method provided by the embodiment of the invention, the driving state information such as the three-axis acceleration, the three-axis angular velocity, the real-time vehicle speed and the output torque is acquired through the sensor equipped on the vehicle, and the algorithm such as the unscented Kalman filtering and the recursive least square with the forgetting factor is combined; the mass of the vehicle and the road gradient can be obtained with low cost, high real-time performance and high precision. The algorithms have advantages in processing nonlinear systems and dynamic changes, and can better adapt to complex working conditions, reduce interference of noise and errors and improve the vehicle quality and the acquisition precision and real-time performance of road gradient parameters especially under the complex working conditions of rapid acceleration, rapid deceleration, up and down of abrupt slopes and the like.

The invention discloses a double neural network model predictive control method for a robot flexible joint, and belongs to the technical field of robot intelligent control, and the method comprises the steps: carrying out the modeling of a nonlinear dynamic system of a flexible joint robot, and building a dynamic model; setting a GRU neural network model to approach a dynamic model of the system; based on a GRU neural network model, constructing a hybrid control NNMPC controller including position, speed and torque; optimization of the NNMPC controller is optimized on the basis of a nonlinear optimization solver; and setting an NARX neural network model to approach a solution of an NNMPC controller, and forming a dual-neural network model predictive control method. According to the double-neural-network model prediction control method for the flexible joint of the robot, two neural networks are introduced to work cooperatively, one neural network is used for approaching the dynamics of the system, the other neural network is used for accelerating the optimization solving process of the model prediction controller, the calculation overhead possibly caused by a traditional method in a high-dimensional nonlinear system is reduced, and the calculation efficiency is improved. Therefore, real-time control is realized.

The invention relates to the technical field of geophysical exploration, and particularly provides a three-dimensional finite element transient electromagnetic forward modeling calculation method based on an iterative solution method.The method comprises the steps that tetrahedron space discretization is carried out based on a vector finite element method, and an electric field is given to six edges of a space tetrahedron; constructing a back-pushing Euler finite element equation set, and assembling a linear system formed by a large sparse matrix based on the equation set; performing iterative solution on the linear system based on an FGMRES algorithm to obtain an electric field value corresponding to a spatial tetrahedron unit edge; optimizing a spatial tetrahedral mesh based on a quadratic nesting method, and optimizing a time step length based on a third-order backward Euler difference format; a grid pre-encryption method is adopted, the calculation error of iterative solution is reduced, a region decomposition method is used for achieving parallel calculation, and the calculation efficiency is greatly improved. According to the method, the requirements of high precision and high efficiency in three-dimensional transient electromagnetic forward modeling calculation can be met, and an advanced calculation tool is provided for resource exploration in a complex geological environment.

The invention discloses a T-S fuzzy and TD3-based slow dynamic unknown coal-fired power generation system coordination control method, which comprises the following steps: firstly, decomposing a coal-fired power generation system into a fast subsystem and a slow subsystem by using a singular perturbation theory, and decomposing an original control task into a stabilization task of the fast subsystem and a tracking task of the slow subsystem; for the fast subsystem, a fuzzy set is selected on a definition domain of the fast subsystem by using the slow variation characteristic of steam pressure, and a T-S fuzzy model is constructed; a fast subsystem controller is obtained by solving algebraic Riccati equations corresponding to a plurality of linear systems. For a slow subsystem, a double-Q network is adopted to reduce overestimation of a Q value, a dynamic learning rate and a batch size adjustment mechanism are introduced to accelerate training convergence, and learning is carried out under a TD3 algorithm framework to obtain control input of the slow subsystem. The fast subsystem and the slow subsystem control inputs to be combined and then act on the original system to obtain state information at the next moment, and the intelligent agent interacts with the original system to complete collaborative optimization.

The invention provides a train operation state estimation method and system based on a high-order extended Kalman filter considering noise correlation, and belongs to the technical field of rail transit train operation management. According to the method, correlation analysis between high-order terms is introduced, the filtering precision is remarkably improved, and particularly, excellent performance is shown under a strong nonlinear system; by establishing a multi-mode train state model, the dynamic characteristics of a train in operation modes such as traction, cruising, sliding and braking are comprehensively covered, and a more accurate input basis is provided for a filtering algorithm; by introducing a sliding window error criterion, the filter can quickly switch operation modes according to real-time errors, so that the filter is ensured to keep high adaptability in a complex and changeable operation environment; the comprehensive performance evaluation framework based on the error covariance matrix not only evaluates the mean square error and the mean absolute error, but also quantifies the contribution of the high-order correlation to the precision of the filter, and provides a more scientific and comprehensive evaluation system.

The invention relates to the field of heat supply network analogue simulation, and particularly discloses a regional heat supply network dynamic simulation method and system, a terminal and a medium, the method comprises the following steps: constructing a hydraulic model semi-analytical expression and a thermal model semi-analytical expression of a pipeline in a regional heat supply network, and according to the pipeline momentum conservation and the node mass conservation, calculating the thermal model semi-analytical expression of the pipeline in the regional heat supply network; constructing semi-analytic coefficient recursive relation linear equations, and respectively calculating semi-analytic coefficients of a hydraulic model semi-analytic expression and a thermal model semi-analytic expression through the two semi-analytic coefficient recursive relation linear equations according to initial conditions and boundary conditions of the pipe section; the calculated semi-analytic coefficient meets the condition that the truncation error is not greater than a truncation error threshold value; and substituting the semi-analytic coefficient into the semi-analytic expression to obtain a semi-analytic calculation result of each pipe section of each pipeline under all time steps. According to the method, non-iterative calculation of the PDAEs is realized, the calculation complexity is reduced, the simulation efficiency is improved, and the solution continuity can be ensured.

The invention provides an efficient privacy set intersection method and device based on bucket coding, and the method comprises the steps: obtaining an input set and a target value set, the input set comprises a private set of a sender and a private set of a receiver, and the target value set is generated based on the private set of the receiver; encoding the private set of the receiver into a sparse matrix composed of a plurality of buckets through a hash function; determining a linear system of the sparse matrix; performing ascending sorting and re-labeling on the linear system according to the initial bit position set to obtain a target linear system; according to a bucket-based Gaussian elimination algorithm, based on a target linear system, through exclusive-or operation, determining oblivious key value pairs for storage; an intersection of the private set of the sender and the private set of the receiver is determined based on the oblivious key-value pair storage according to an oblivious linear evaluation protocol. According to the invention, communication resources required by privacy set intersection in an actual production environment can be further reduced.

The invention relates to the field of power system frequency stability analysis and control, and discloses a power system frequency dynamic characteristic analysis method considering a photovoltaic frequency modulation effect, and the method comprises the steps: building a power system linearization model considering a photovoltaic primary frequency modulation effect; substituting the power system linearization model into an input linear system equation, performing approximate decoupling on the linear system equation to obtain a cubic equation, and deriving an approximate solution of the power system linearization model about a dominant characteristic value of a frequency time domain by solving the cubic equation; and setting a frequency dynamic index, deducing an analytical expression of the frequency dynamic index based on the approximate solution of the dominant characteristic value, and analyzing the influence of the parameters of the power system linearization model on the power system frequency dynamic response characteristics. The method is beneficial to frequency stability analysis and quantitative evaluation of frequency control measures after the power system is connected to photovoltaic power.

The invention provides a structural damage detection method and device for a nonlinear system, and relates to the technical field of nondestructive detection. The method comprises the following steps: applying excitation force to a to-be-detected test piece, vibrating the to-be-detected test piece based on the excitation force, performing signal acquisition on the to-be-detected test piece to obtain an excitation signal and a response signal, constructing initial NARX models of different orders by combining the excitation signal and the response signal with a plurality of imported orders respectively, and calculating the initial NARX models according to the initial NARX models. And screening a target NARX model from the plurality of initial NARX models based on an akaike information criterion, optimizing the target NARX model, comparing order information in the NARX model to be detected obtained by optimization with order information in the reference NARX model, and determining whether the test piece to be detected has defects according to an order comparison result. The nonlinear model is constructed and optimized through the signal data of the to-be-detected test piece, so that the model accurately represents the structural characteristics of the to-be-detected test piece, and then whether the to-be-detected test piece has defects or not is judged.

The invention relates to the technical field of mechanical transmission, in particular to a reinforcement learning compensation method and system for gear multi-body dynamics constraint, and the method comprises the steps: building a multi-body dynamics digital twinborn model containing bearing rigidity and gear meshing force; constructing an adaptive reward function based on nonlinear manifold mapping, and dividing a system state space into a small deviation domain and a large deviation domain according to an angle deviation threshold value; training the multi-body dynamic model based on a depth deterministic strategy gradient algorithm to obtain a compensation strategy; the obtained compensation strategy is applied to a multi-body dynamic model, and an optimal compensation signal is obtained through self-adaptive fusion of static compensation and dynamic compensation, so that the training efficiency of a reinforcement learning algorithm in a complex nonlinear system is improved, the adaptability of the system to working condition changes is enhanced, the vibration of a gear system is remarkably reduced, and the reliability of the system is improved. The angle control precision is improved, and the service life of equipment is prolonged.

The invention discloses a novel model-free super-spiral sliding mode control method based on a sliding mode disturbance observer, and the method comprises the steps: designing a novel model-free super-spiral sliding mode controller of a rotating speed ring based on the combination of a novel super-local model and a second-order super-spiral law; meanwhile, a sliding mode disturbance observer is designed to estimate uncertain and unknown disturbance parts of system parameters in real time and feed back to a novel model-free super-spiral sliding mode controller of a rotating speed ring, so that the rotating speed of the motor is controlled; the controller introduces a linear system state item and a nonlinear disturbance item according to the novel super-local model, establishes a novel super-local model of the system, combines the novel super-local model of the system with a rotating speed ring state equation of the motor, establishes a novel super-local model of a rotating speed ring, and performs the super-local model of the rotating speed ring; and establishing a novel model-free super-spiral sliding mode control law of the rotating speed ring based on the novel super-local model of the rotating speed ring and an observation result of the sliding mode disturbance observer. The influence of external disturbance on the motor is effectively solved, buffeting is weakened, and effective tracking of the given rotating speed can be achieved.

The invention relates to the technical field of inverter control, and discloses a micro inverter control method and system in a photovoltaic system and a storage medium. The method comprises the following steps: carrying out parameter acquisition and processing on the photovoltaic micro inverter to obtain an initial parameter set; designing a radial basis function neural network compensation uncertainty parameter based on the initial parameter set; inputting the compensation parameter set into a particle swarm optimization algorithm to optimize control parameters; and training a back propagation neural network based on the optimized parameter set to realize maximum power point tracking control. According to the method, the uncertainty of the system is compensated by introducing the radial basis function neural network, the control parameters are optimized in combination with the particle swarm optimization algorithm, and the real-time prediction and adjustment of the parameters are realized by using the back propagation neural network; the technical problems that a traditional control method is low in control precision and poor in adaptability when facing nonlinear system characteristics, environmental condition changes and parameter uncertainty are effectively solved.

Methods are provided for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. Models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.

The invention relates to the technical field of CPU card chips, and discloses a self-adaptive power consumption control method and system of a CPU card chip. The method comprises the following steps: collecting power consumption characteristic data of a CPU card chip in different working load states, and constructing a power consumption signal model; creating a generalized power consumption control operator, and performing matrix decomposition on the power consumption characteristic data of the CPU card chip to obtain a power consumption weight vector; performing dual-state monitoring and feature selection on the power consumption state vector and the performance state vector which are acquired in real time to obtain a low-power-consumption sparse control matrix; performing parameter optimization training through a five-layer fuzzy neural network to obtain a target inverse system model; the target inverse system model and the original power consumption control system are connected in series to form a pseudo-linear system, a linear closed-loop controller is added, and the decoupling control system is obtained, compared with a traditional method, the decoupling control system has the advantages that the energy efficiency ratio of a CPU card chip is remarkably increased, and guarantee is provided for efficient operation in various working environments.

The invention belongs to the field of automatic control, and particularly relates to a nonlinear system dynamic gain global trajectory tracking control method based on a self-adaptive neural network observer, which comprises the following steps: converting an uncertain nonlinear system model with unknown interference according to the requirement of output feedback control to obtain a first nonlinear system model; enabling an unknown nonlinear continuous function in the first nonlinear system model to be expressed as a function only containing an output signal and other system state estimation values; performing approximate approximation on an unknown nonlinear continuous function in the first nonlinear system model by using the first radial basis function neural network vector to obtain a second nonlinear system model; designing a second radial basis function neural network vector to construct a dynamic gain state observer of the second nonlinear system model, and obtaining an estimated value of a system state and an estimated value of an unknown nonlinear continuous function in the model; according to an inversion control method, defining a dynamic system tracking error index containing an intermediate virtual control signal, designing a Lyapunov function, and obtaining a change rate of a dynamic gain, a weight adaptive update rate of a second radial basis function neural network vector, and a control rate of the intermediate virtual control signal and a system input signal; different from most existing nonlinear system control methods, the method combines a novel dynamic gain neural network observer with an inversion controller with dynamic gain, and provides a robust global trajectory tracking control solution for a dynamic system operating under uncertain conditions.

The invention discloses an armored vehicle diesel engine digital twin model reduction method and system, and the method comprises the steps: building a diesel engine twin model which is a high-dimensional nonlinear system and comprises a thermodynamic equation of a diesel engine; operating the diesel engine twin model under different working conditions, collecting input and output data of variables, and performing standardization processing on the input and output data of the variables to obtain simulation data; a neural network is used for constructing a nerve differential equation model, simulation data is used for training the nerve differential equation model to obtain a reduced-order model, and the output of the diesel engine is predicted based on the obtained reduced-order model. Through Simulink modeling, neural network training, incremental learning and real-time data updating, a dynamic model of the diesel engine is effectively simplified, and it is ensured that high-precision prediction capacity is kept under different working conditions and operation environments. The system architecture supports real-time monitoring, online learning and self-adaptive adjustment, and high efficiency and stability of the diesel engine control system in practical application are guaranteed.

The invention provides a nonlinear system tracking control method with segmented discontinuous output constraints, and aims to solve the problem that a constraint boundary function is discontinuous but bounded at a certain moment within a period of time when a system starts to run and solve the problem that system output is not limited by constraints at a certain moment. Different from most existing methods requiring consecutive and bounded constrained boundary functions and high-order derivatives thereof, the method provided by the invention only needs consecutive and bounded converted constrained boundary functions and first-order derivatives thereof, so that the requirements on the constrained boundary functions are relaxed, the practicability of a control strategy is greatly improved, and the complexity and calculation burden of a controller are reduced. Besides, by changing discontinuous moments without changing the structure and design parameters of the controller, the proposed control strategy can also be suitable for other constraint situations, such as continuous constraint conditions, discontinuous constraint conditions at specific moments and unconstrained limitation of system output at a certain moment, so that the practicability of the control strategy is further enhanced.

The invention provides a linear equation set of a diffusion equation diagram neural network model for water quality prediction, an F-GNN method and a system, and the F-GNN method comprises the steps: S1, collecting the monitoring historical data of each station, and constructing a space-time diagram; s2, on the basis of the space-time diagram, calculating a diffusion equation calculation formula by adopting a finite difference method, constructing an overdetermined linear equation set according to the diffusion equation calculation formula, and estimating parameters of the diffusion equation of each river reach by applying a least square method; and S3, if the parameters of the diffusion equation of each river reach are positive numbers, performing F-GNN prediction to obtain feature prediction of each station. According to the method, the defect that a water quality prediction model lacks space-time joint modeling is overcome, the problem that a traditional data driving model is prone to overfitting under the small sample condition is solved, diffusion equation constraints are introduced, the defect is effectively overcome, meanwhile, physical constraints are introduced, prediction precision and reliability are improved, and the method is suitable for large-scale popularization and application. The method is suitable for various environment monitoring scenes.

The invention provides a self-adaptive chaotic synchronous controller design method and a secret communication method based on an MANN and a DH-PGA algorithm. The method is used for solving the synchronization problem of the three-dimensional chaotic system under the conditions of uncertainty and external disturbance. By introducing the memristor and the DH-PGA algorithm, the controller not only shows strong robustness when facing system uncertainty and external disturbance, but also can improve the synchronization precision and stability of the system through adaptive adjustment. Generally speaking, the chaotic synchronous controller based on the MANN and the DH-PGA provides an effective solution for synchronization of a complex nonlinear system, and particularly shows excellent performance in scenes with high real-time requirements and limited computing resources.

The invention discloses a fault-tolerant control method and device for a faulty unmanned aerial vehicle based on incremental nonlinear dynamic inverse terminal sliding-mode control, and the method comprises the steps: determining an incremental expression based on a terminal sliding-mode control law, determining a sliding-mode control law of the faulty unmanned aerial vehicle based on an attitude control algorithm of incremental nonlinear dynamic inverse nonsingular terminal sliding-mode control, and carrying out the fault-tolerant control of the faulty unmanned aerial vehicle. The method comprises the steps of calculating the rotating speed of a rotor wing based on a sliding-mode control law and incremental expression, calculating the acceleration required to be applied by a faulted unmanned aerial vehicle based on the rotating speed of the rotor wing, and controlling the attitude and position of the faulted unmanned aerial vehicle based on the acceleration, and comprises the steps of converting a nonlinear system of the faulted unmanned aerial vehicle into a linear system, and designing a terminal sliding-mode control law; and determining an incremental expression according to the sliding-mode control law to calculate the acceleration required to be applied by the faulted unmanned aerial vehicle, thereby solving the problems that the fault-tolerant control cost is high and an expected index is not easy to reach under a relatively serious fault condition that the faulted unmanned aerial vehicle fails in a single-rotor or double-rotor mode.

The invention belongs to the technical field of electromagnetic exploration and numerical simulation. According to the regional decomposition magnetotelluric three-dimensional forward modeling simulation method and system, overlapping DDM is introduced to decompose a large-scale MT problem into a plurality of small sub-domains, so that the calculation complexity is reduced; each small linear system in a sub-domain is solved by using a direct solver, so that the convergence problem of an iterative solver is avoided, an LU decomposition result is repeatedly utilized in a Schwarz iteration process, and the calculation speed is increased; a multi-grid scheme is integrated to accelerate convergence, and a good initial condition is provided through a coarse grid solution, so that the number of iterations is reduced, and the calculation efficiency is further improved; the adaptability of a complex geological structure is enhanced by using an unstructured grid technology, so that the simulation precision is improved; according to the method, the aviation magnetotelluric three-dimensional forward modeling technology which is high in calculation speed and low in memory requirement is developed, and a basis is provided for theoretical research and data three-dimensional inversion of aviation magnetotelluric.

This disclosure describes a computing module for reducing communication burden between a controller and a physical system comprising a non-linear system by which only a m-length string is transmitted between the controller and the physical system. Further, the computing module introduces a self-adjustable performance mechanism that flexibly adjusts symmetric or asymmetric performance boundaries of the nonlinear system to accommodate various type of initial conditions, providing a solution to an underlying tracking problem.

The invention relates to a stability margin measuring method based on data driving, which adopts a small gain theorem to ensure the stability of a closed loop of a system and defines the stability margin of the system based on the stability margin. The defined gain margin and the time delay margin can be obtained through a data-driven experimental measurement method. According to the method, a class of gain margins and time-delay margins for the multiple-input multiple-output linear system are established through the small gain theorem, so that the defect that the small gain theorem is only suitable for amplitude margin analysis is overcome. And a frequency sweeping method is established to obtain the gain margin and the time delay margin through an experimental method, so that the uncertainty and unmodeled dynamics in practice can be considered, and the obtained stability margin is closer to the stability margin of an actual system.

The invention belongs to the technical field of airplane nonlinear system control, and discloses a deicing vehicle hierarchical control method and system under an airplane idling condition. According to the method, a deicing vehicle overall dynamic model under incomplete constraint is constructed based on a Lagrange equation, a vehicle-arm combined trajectory tracking error state model is constructed, a cost function of a model prediction controller is designed according to a control target of deicing vehicle combined trajectory tracking, a quadratic programming problem is constructed on an upper layer to solve an optimal virtual control quantity, and the optimal virtual control quantity is calculated. And the lower layer analyzes the virtual control quantity into control input which can actually act on an execution mechanism by designing a self-adaptive fuzzy logic strategy so as to realize trajectory tracking control. According to the invention, a hierarchical control strategy is provided by combining model predictive control and an adaptive fuzzy logic system (FLS), the stability and high efficiency of system operation are ensured, and the deicing vehicle is ensured to always maintain a stable operation state in a complex and changeable operation environment.

The embodiment of the invention relates to the field of dynamic systems, and discloses an active fault-tolerant control method for a nonlinear system, which comprises the following steps of: acquiring a fault estimation value through an observer, and calculating an absolute value of the fault estimation value to obtain a numerical value of a residual signal; calculating a difference value between the system state signal and the system expected signal to obtain a system tracking error, substituting a filtering error of a filter into a preset compensation calculation formula, and calculating to obtain a compensation signal; calculating a difference value between the system tracking error and the compensation signal to obtain a compensation tracking error; when the value of the residual signal is larger than or equal to the detection threshold value, it is indicated that the actuator breaks down, a calculation formula of a preset compensation control signal is obtained through design according to the Lyapunov principle, the compensation control signal is obtained through calculation, the compensation control signal can change according to a fault estimation value, and when disturbance and uncertainty exist, the compensation control signal can be accurately calculated. The fault can be flexibly and accurately estimated, the error of fault compensation error is reduced, and the control performance of active fault tolerance is improved.