317 results about "Actuator fault" patented technology

The invention relates to a multi-sensor fusion intelligent actuator fault self-diagnosis method and system, and belongs to the technical field of actuator fault diagnosis. The method comprises the following steps: realizing multi-sensor clock synchronization through a unified clock source, generating a synchronous time sequence for asynchronous data such as Hall, pressure and temperature by adopting an interpolation method, and intensively acquiring signals such as rotating speed, torque and vibration; based on a current sensor and temperature data, primary fault identification is carried out through a current overload protection model, and an adaptive processing mechanism is triggered; demodulating the noise-reduced vibration signal, extracting high-frequency energy and detecting the deviation degree between the fault frequency of the bearing and a base line; bearing energy consumption abnormity is detected, and a fusion weight is dynamically set by combining the deviation degree, the energy consumption coefficient and an overload result; and carrying out weighted fusion on the multi-modal features, inputting an intelligent diagnosis model to carry out fault mode identification, and finally outputting a fault type / position and triggering a processing strategy. Accurate and rapid self-diagnosis of the actuator fault is realized.

The invention discloses an intelligent automobile cooperative cruise safety control method under Dos attacks and physical faults, and relates to automobile intelligent safety and automatic driving. A hierarchical control framework is adopted and comprises an observer layer and a tracking layer. The method comprises the following steps: establishing a vehicle longitudinal dynamics model and a DoS attack model for DoS attack in a V2X communication network and the problems of sensor and actuator faults and parameter isomerism of a vehicle; a completely distributed self-adaptive preset time observer based on event triggering is designed for each following vehicle in the observer layer, and rapid and accurate estimation of the state of the pilot vehicle is achieved; the method comprises the following steps: constructing an augmentation system at a tracking layer, designing a distributed intermediate observer, carrying out online estimation and compensation on faults of a sensor and an actuator, designing a distributed active fault-tolerant controller based on a fault estimation value and a pilot vehicle state estimation value, and calculating a wheel driving torque to realize safe cruise control. Multiple threats of coexistence of network attacks and physical faults are effectively handled, and the stability and safety of the cooperative cruise system are ensured.

The invention relates to the technical field of vehicle stability control and fault-tolerant control, and discloses a steer-by-wire vehicle stability control method under actuator faults and input hysteresis, a terminal and a medium. The method comprises the following steps: establishing a kinetic model of a steer-by-wire vehicle, and constructing a front wheel subsystem kinetic model in a steer-by-wire system by comprehensively considering an actuator fault, input hysteresis and a state constraint condition; according to the steering angle instruction of the driver and the ideal vehicle model, the expected yaw velocity and the expected sideslip angle of the vehicle are obtained through calculation; designing an upper-layer stability controller of the front wheel subsystem; generating a desired front wheel steering angle for the front wheel subsystem; and a lower-layer steering angle tracking controller of the front wheel steering system is designed, the front wheel steering angle tracking performance of the steer-by-wire system under the working conditions of actuator faults and input hysteresis is ensured, and the actual front wheel steering angle of the vehicle approaches the expected front wheel steering angle. According to the invention, the steer-by-wire system can still maintain the stability of the vehicle under the conditions of actuator fault and input hysteresis.

The invention discloses a security boundary consistency control method for a multi-agent spatio-temporal dynamic system. The method specifically comprises the following technical steps: firstly, constructing a leader-follower spatio-temporal dynamic mathematical model of the multi-agent system based on a partial differential equation; then establishing an abnormal working condition mathematical model containing an actuator fault and a network spoofing attack; deducing to obtain an error dynamic system equation by defining a system consistency control target; constructing an energy function by using a Lyapunov stability theory, and deducing to obtain a system stability criterion; finally, a security boundary consistency control method is designed, and multi-agent space-time dynamic consistency control is achieved. The method effectively solves the problem of high cost caused by arrangement of actuators in a whole spatial domain in a traditional scheme and the problem that a multi-agent system is easily interfered by network attacks in a communication process, and has remarkable application value in engineering practice.

The invention provides a fault-tolerant control method for preset performance of a morphing aircraft based on sliding-mode control. The fault-tolerant control method comprises the following steps: establishing a longitudinal nonlinear dynamic model of the morphing aircraft; based on the longitudinal nonlinear dynamic model of the morphing aircraft, establishing a longitudinal nonlinear dynamic model of the morphing aircraft under the fault condition of the execution mechanism; the method comprises the following steps: extracting a speed subsystem and an attitude subsystem aiming at a longitudinal nonlinear dynamic model of a morphing aircraft under an execution mechanism fault condition; carrying out sliding-mode control-based preset performance fault-tolerant controller design on the attitude subsystem; aiming at a speed subsystem, combining an attitude subsystem controller, and carrying out sliding mode control-based preset performance fault-tolerant controller design; the two designed controllers are utilized to realize the fault-tolerant control of the variant aircraft under the condition of executing mechanism fault. The sliding mode control and the preset performance control are combined, so that the conservative property is improved by quick response, the fault-tolerant capability is enhanced by switching the control law, and the stability is improved.

The invention discloses a cluster adaptive path planning method based on geometric PDE and PINN. The method comprises the following steps: firstly, modeling a dynamic model of a fault cluster with time-varying topology; secondly, through Riemannian manifold and gradient evolution based on heat flow, a fault cluster is constructed based on a parabolic PDE system, and a reference trajectory of a cluster system in an obstacle environment is obtained; and learning a steady-state solution of the cluster based on a parabolic PDE system by using a physical information neural network (PINN), and obtaining a fault-tolerant optimal control law of the cluster system based on geometric PDE and PINN under the conditions of time-varying topology, actuator fault and obstacle operation. According to the method, it is ensured that the intelligent agent navigates to the target position from the initial position along the dynamic reference trajectory, meanwhile, endogenous compensation of the fault effect is achieved, and unified solution of fault compensation and motion planning is achieved.

A system, computer readable storage medium and method for controlling a trajectory of coordinated time-varying maneuvers of a geometric formation of unmanned vehicles is disclosed. The system includes unmanned vehicles, each configured with communication circuitry to communicate between the vehicles. A subset of the unmanned vehicles function as leader vehicles, with the remaining vehicles functioning as follower vehicles for leader-follower maneuvering. The system further includes an actuator suite configured to adjust the direction and orientation of each vehicle, a sensor suite for stabilization and navigation, and a flight controller for maintaining stable maneuvering, even in the presence of actuator faults and sensor deception attacks. Processing circuitry is configured with a reinforcement learning neural network that includes identifier, actor, and critic radial basis function neural networks to estimate movement, adjust control actions, and assess vehicle performance based on feedback signals, including corrupted signals from the sensor suite due to deception attacks.

The invention discloses a distributed anti-interference fault-tolerant control method and a distributed anti-interference fault-tolerant control system for a multi-six-rotor unmanned aerial vehicle attitude system. The control method comprises the following steps: constructing a multi-six-rotor unmanned aerial vehicle attitude model based on a multi-six-rotor unmanned aerial vehicle attitude system model and interference uncertainty; designing a disturbance observer which is used for estimating the degree of external disturbance and actuator faults; and based on the output of the multi-six-rotor unmanned aerial vehicle attitude model and the degree of external interference and actuator fault, constructing a Lyapunov function by using a backstepping recursion technology, and designing a distributed anti-interference fault-tolerant controller for generating a control output signal to the multi-six-rotor unmanned aerial vehicle attitude model. According to the method, the Lyapunov function is constructed, so that output signals of all followers and leaders are synchronous, and errors are converged. Meanwhile, the general approximation characteristic of the neural network is utilized, the nonlinear term and the unmodeled dynamic are approximated online, and the model complexity is reduced.

The invention discloses a quadruped robot anti-disturbance motion control method based on reinforcement learning and application, and belongs to the field of robot motion control. Firstly, a self-adaptive propulsion control frame is provided, and a propeller and a leg movement system of the quadruped robot are deeply fused. And secondly, through cooperative training of a double-encoder system (a teacher encoder and a student encoder) and a predictor, hidden state extraction and environment disturbance reconstruction are realized, so that the robot adjusts a motion strategy in real time under complex scenes such as actuator faults and water flow interference. And finally, in combination with multi-stage reinforcement learning training and privilege information utilization, strategy network output is optimized, and a speed and trajectory tracking task is completed. The motion robustness of the robot under unknown interference and damage conditions is remarkably improved through cooperative control of the propellers and the legs, anti-disturbance decision of hidden state driving and dynamic reward function design. The method is suitable for amphibious environments, can adapt to diversified disturbance scenes without additional fine adjustment, and has high practicability and reliability.

The invention relates to the technical field of mechanical equipment fault diagnosis, and particularly discloses an attention mechanism CNN-based electric actuator fault feature extraction method and system. The method comprises the following steps: through a mode of combining simulation modeling and field acquisition, obtaining a fault feature of an electric actuator; obtaining multi-source data of the electric actuator in a normal state, a known fault state and an unknown fault state, and preprocessing and enhancing the multi-source data; and constructing a lightweight convolutional neural network model containing a cross-modal attention mechanism. According to the method, multi-source signals such as current, voltage and displacement can be effectively fused, weights of different modes and characteristics are automatically distributed, and the extraction capability of key fault characteristics is enhanced; accurate distinguishing between known faults and unknown faults is realized through multi-scale feature complexity measurement and a dynamic discrimination mechanism; in combination with domain self-adaption and an incremental learning mechanism, the model can migrate between simulation and real working conditions, and has self-expansion capability for a novel fault mode.

The invention discloses a modular mechanical arm actuator additive fault optimal fault-tolerant control method and device of a zero sum differential game, and relates to the field of robot control algorithms, and the method comprises the steps: representing a nonlinear damping characteristic through a joint friction torque, describing the dynamic interaction of multiple joints through a cross-linking coupling item, and determining the optimal fault-tolerant control of the additive fault of the modular mechanical arm actuator; a dynamical model containing faults is constructed. And uncertain items in the model are updated online by adopting a radial basis function neural network identifier, so that the model precision is improved. A performance index function is constructed based on position errors, actuator faults and controller input are regarded as two opposite parties of a zero and differential game, the performance index function is approximated through a single evaluation neural network, a Hamiltonian-Jacobi-Axaxi equation is approximately solved, and an optimal fault-tolerant control strategy is obtained. According to the method, the game theory is combined with the neural network, the dynamic unknown fault problem of the modular mechanical arm is effectively solved while the system energy consumption integration is reduced, and real-time optimal control over the modular mechanical arm is achieved.

The invention belongs to the technical field of artificial intelligence, and discloses an actuator fault performance compensation method for a wind power plant grid-connected interconnection system, and the method comprises the steps: building a dynamic model of a nonlinear interconnection system containing a wind power plant; then, in combination with an actuator fault, constructing a state-space equation of a nonlinear system introducing a neural network; a composite observer with a dynamic event triggering mechanism is constructed, and estimation of the system state, external interference and actuator faults is achieved; according to the output of the composite observer, constructing a sliding mode safety compensation controller for realizing disturbance and fault compensation, and further obtaining an equivalent controller and a switching controller; and constructing a system master controller according to the equivalent controller and the switching controller. According to the invention, comprehensive estimation of the system state, external disturbance and actuator faults can be realized, active compensation of disturbance and faults can also be realized, and the system control stability is improved.

The invention discloses a finite time preset performance fault-tolerant control method for a two-degree-of-freedom rigid mechanical arm. The method mainly solves the problems that according to a conventional trajectory tracking control scheme, unknown disturbance in the external environment of a system cannot be estimated, disturbance cannot be reduced, and the stability of the system is affected. The method comprises the steps of constructing dynamical models of partial failure and complete failure of an actuator, designing a finite time preset performance function constraint tracking error, constructing a virtual controller and an actual controller based on a backstepping method, introducing an adaptive disturbance observer to compensate unknown external disturbance, and proving system stability through a Lyapunov function. According to the method, high-precision trajectory tracking of the mechanical arm under the actuator fault condition can be achieved, it is ensured that tracking errors converge to a steady-state area within the set time, and meanwhile dependence on external disturbance and system parameter priori knowledge is reduced.

The invention provides a preset performance fault-tolerant control method under motor failure of a tendon-driven mechanical arm. The preset performance fault-tolerant control method comprises the following steps: constructing a tendon-driven mechanical arm system model with actuator fault, external interference and system parameter uncertainty; designing a tendon-driven mechanical arm trajectory tracking error model and a fixed time sliding mode observer; an improved preset performance function is constructed, so that the trajectory tracking position error of each joint of the mechanical arm meets a preset performance boundary; and a fixed-time preset performance fault-tolerant controller is designed, so that trajectory tracking errors of all joints of the mechanical arm are converged to a preset performance boundary within fixed time. According to the method, non-singular terminal sliding mode control is combined with the preset performance function, so that the performance boundary constraint of the tracking error is realized while the stability of the fixed time of the system is ensured; by dynamically adjusting the boundary of the performance function, the problem of control precision degradation under the actuator fault working condition is effectively solved, and the robustness and trajectory tracking precision of the system are remarkably improved.

The invention relates to an automobile finite time path tracking fault-tolerant control method considering steering actuator faults. The method comprises the following steps: firstly, establishing a vehicle dynamics model and a steering execution mechanism dynamics model, designing a path tracking control strategy by applying a model prediction control method, and establishing an upper-layer path tracking controller; then, on the basis of a differential driving auxiliary steering principle, a preset performance control method of finite time convergence and a nonsingular fast terminal sliding mode control theory are combined, and a steering actuator fault-tolerant controller is established; and finally, designing a torque distribution controller by adopting an optimal control theory, and solving an optimal four-wheel driving torque control quantity through quadratic programming. According to the method, the influence caused by the failure of the steering actuator can be effectively compensated, the safety and the stability of the vehicle are remarkably improved, and a feasible solution is provided for the fault-tolerant control of the steering actuator in a future intelligent vehicle path tracking scene.

The invention discloses a quadrotor unmanned aerial vehicle attitude fault-tolerant control method. The method comprises the following steps: S1, establishing a tracking error dynamic model of a quadrotor unmanned aerial vehicle attitude system; s2, designing a reduced-order fixed-time expansion state observer; s3, constructing an adaptive preset performance function; s4, designing a fast nonsingular terminal sliding mode surface and a fixed time reaching law based on an adaptive preset performance function and the lumped disturbance estimated value, and generating a posture virtual control quantity; and S5, according to the mapping relation between the attitude virtual control quantity and the attitude loop control moment, performing inverse solution to obtain the attitude loop control moment, and inputting the control moment into an attitude actuator to enable the attitude angle tracking error to converge to be near a balance point within a fixed time. Compared with the prior art, the four-rotor unmanned aerial vehicle attitude fault-tolerant control method has the advantages that strict constraint on attitude system response under the actuator fault condition is provided, it is ensured that unmanned aerial vehicle tracking errors meet preset transient state and steady state performance constraints, and meanwhile the vulnerability problem of preset performance is effectively avoided.

The invention discloses a distributed adaptive fault-tolerant formation tracking control method. The method comprises the following steps: constructing a longitudinal dynamic model of the intelligent agent and decomposing the longitudinal dynamic model into a speed subsystem and an attitude subsystem; constructing an actuator fault model, and reconstructing lumped uncertainty items in combination with the uncertainty of a longitudinal dynamic model and external disturbance; self-adaptive laws of a speed subsystem and an attitude subsystem are determined respectively, so that active compensation is carried out on lumped uncertainty items through an RBF neural network; and respectively determining control protocols of the speed subsystem and the attitude subsystem through a recursive backstepping method in combination with a general obstacle Lyapunov function. According to the method, under the harsh conditions that uncertainty and actuator faults exist, the key state safety boundary is continuously met, meanwhile, the high-quality time-varying formation tracking performance is kept, and the implementation complexity of a control system is effectively reduced.

The invention provides a self-adaptive dynamic programming self-triggering fault-tolerant control method for a flexible mechanical arm, and particularly relates to a flexible joint mechanical arm with a non-affine fault and an unknown control direction. The method comprises the following steps: firstly, designing a self-adaptive observer, and estimating a non-affine actuator fault and an unknown item caused by an unknown control input direction; constructing an evaluation neural network considering unknown terms by using the approximation capability of the neural network so as to approach a system cost function; then solving a Hamiltonian-Jacobian-Bellman equation to obtain approximate optimal control input for realizing ideal trajectory tracking and vibration suppression; meanwhile, a self-triggering mechanism is adopted, the next triggering moment is calculated according to the current system state, hardware requirements are reduced, and communication resources are saved. According to the method, control performance, energy consumption and information transmission constraint are considered, resource consumption can be reduced while quick response is achieved, and trajectory tracking and vibration suppression tasks of the flexible joint mechanical arm are safely and reliably achieved.

The invention discloses an electric actuator fault diagnosis method and system, and belongs to the technical field of fault diagnosis, and the method specifically comprises the steps: collecting the current, voltage, displacement, temperature rise and electromagnetic noise signals of an electric actuator in the operation process, and forming a multi-dimensional operation data set, performing time-frequency domain fusion processing on the multi-dimensional operation data to obtain a feature vector, constructing a health state track of the electric actuator, introducing an electromagnetic noise coupling factor into the health state track, performing evolutionary analysis on the health state track, extracting trend information related to a fault, and when the trend information exceeds a set threshold value, determining that the electric actuator is in a fault state. Comparing with a pre-stored actuator fault evolution sample, and judging the fault type and severity; according to the method, advanced identification and classification discrimination of potential faults can be realized through trend extraction and adaptive comparison, so that the comprehensiveness and accuracy of diagnosis are improved.

The invention discloses a nonlinear system robust residual error multi-fault detection and reconstruction method based on a sliding-mode observer, and solves the problems that an actuator fault and unknown input disturbance in a servo driving system are closely related and mutually coupled, and a fault detection algorithm is insensitive to identification of a first-degree fault, so that fault report failure or misjudgment is caused. According to the method, a general nonlinear system is transformed into two subsystems with independent fault influence conditions based on global differential homeomorphic transformation for the first time. Equivalently transforming reverse channel disturbance to forward channel disturbance by using a filter, designing a sliding mode state observer according to different actuator fault types, and respectively extracting fault information and unknown input disturbance information of a servo driving system in the two subsystems, thereby realizing decoupling of the fault and the unknown input disturbance. And the sensitivity of fault signal detection and the accuracy and robustness of fault diagnosis are effectively improved.

The invention provides a sewage treatment process adaptive neural network control method with time-varying asymmetric constraints, is used for a sewage treatment process with unknown parameters, and belongs to the technical field of urban sewage treatment process intelligent control. A controller is designed by adopting a self-adaptive control method, and the concentration of dissolved oxygen and the concentration of nitrate nitrogen in the sewage treatment process are accurately controlled. Firstly, due to the fact that the fuzzy neural network has excellent robustness, unknown dynamics appearing in a sewage treatment plant can be effectively approached. Secondly, control input is designed by constructing a time-varying asymmetric obstacle type Lyapunov function, and a fault-tolerant control method is designed in consideration of an actuator fault condition. And finally, performing a simulation experiment by using the reference simulation model 1, and verifying the effectiveness of the proposed method by a simulation result.

The invention relates to a multi-unmanned aerial vehicle formation fault-tolerant control method based on an extended state observer. The method comprises the following steps: firstly, establishing a dynamic model of a multi-unmanned aerial vehicle formation; a multi-unmanned aerial vehicle formation communication topological structure is constructed according to the actual demand of a formation task, and information interaction among multiple unmanned aerial vehicles is realized based on a piloting-following method architecture and a distributed control method; establishing a global position tracking error and a speed tracking error by using the states of the adjacent unmanned aerial vehicles; introducing an extended state observer to estimate the unknown state of the formation system in real time; and a sliding-mode adaptive fault-tolerant controller is designed to effectively deal with the influence of actuator faults, system uncertainty and unknown external disturbance on formation performance, and stable control of the multi-unmanned aerial vehicle formation system is realized. According to the method, under the condition that a single unmanned aerial vehicle or multiple unmanned aerial vehicles break down, reliable transmission of task information and effective keeping of the formation can still be guaranteed, and the method has high system robustness and environment adaptability and is suitable for complex flight task scenes.

The invention relates to a mechanical arm actuator fault tolerance control method based on sliding mode control and a neural network, and belongs to the field of industrial robots. The method comprises the steps that modeling is conducted on the fault condition of a mechanical arm actuator, and a fault model is established; defining a joint angle tracking error, designing a sliding mode controller, and establishing a sliding mode surface of the sliding mode controller as linear sliding; designing a mechanical arm dynamic form according to the linear sliding surface and the fault model; estimating a total dynamic item by adopting an RBFNN neural network to obtain an estimated value of a system dynamic item, and taking the estimated value as an output quantity of the RBFNN neural network; designing a control law; calculating and generating a corresponding control input quantity according to a control law by adaptively updating a fault matrix of the weight matrix; and the mechanical arm actuator controls the mechanical arm according to the control input quantity so as to achieve fault compensation. According to the method, high-precision trajectory tracking is achieved under the condition that dynamics and actuator faults are unknown, and an accurate fault model or a fault detection system is not needed.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a fault-tolerant control method, system and device for an unmanned aerial vehicle cluster and a medium. The method comprises the following steps: establishing a first dynamic model of a leader and a second dynamic model of each follower; designing a distributed expansion observer, and estimating state information of each follower and lumped interference formed by external disturbance of the follower and an actuator fault by using relative state information between each follower and a neighbor follower; determining the state information of the leader, and designing an event triggering mechanism according to a first error between the state information of each follower and the state information of the leader and a second error between the state information of each follower; and when the first error is greater than a first preset threshold value or the second error is greater than a second preset threshold value, determining a triggering event triggering mechanism, and controlling each follower in the unmanned aerial vehicle cluster according to the relative state information between the followers and the estimated lumped interference.

The invention discloses a fault compensation and H-infinity fault-tolerant tracking control method for an autonomous underwater vehicle, and belongs to the technical field of engineering control methods. Comprising the following steps: S1, aiming at actuator faults, sensor faults and external disturbance problems in vertical plane motion of an autonomous underwater vehicle, introducing a depth variable and establishing a high-order all-drive fault model; s2, designing an intermediate estimator based on a high-order all-drive model, and realizing estimation of a system state and a fault; and S3, designing a controller based on the high-order all-drive model, solving a game Riccati equation by using a Minmax method, obtaining an optimal control strategy, and realizing fault compensation and external disturbance suppression. The effectiveness of the method and the excellent tracking control effect of the designed controller are verified through a depth control simulation experiment of the autonomous underwater vehicle.

The invention discloses a distributed fault-tolerant cooperative control strategy based on a nonlinear observer, belongs to the field of intelligent control, and mainly aims at estimating and compensating actuator faults, unknown nonlinearity and external disturbance in a queue in queue control. And the whole queue is ensured to keep the consistency, stability and passing efficiency of advancing under the condition of a non-zero initial spacing error. The method comprises the following steps: establishing a queue system model with actuator faults, unknown nonlinearity and external disturbance; establishing a queue tracking error; establishing a queue sliding mode error; establishing a queue coupling sliding mode error; establishing a nonlinear observer for external disturbance of the queue; establishing a neural network estimation mechanism for unknown nonlinearity in the queue; establishing a self-adaptive parameter estimation mechanism aiming at the queue actuator fault; and establishing a distributed fault-tolerant queue control strategy based on a nonlinear observer, a neural network estimation mechanism and an adaptive parameter estimation mechanism. The method is used for queue cooperative intelligent control.

The invention provides a preset time adaptive fuzzy cooperative formation control method for a multi-water-surface-robot system, and the method comprises the steps: building a motion model containing uncertainty and actuator faults based on a speed state vector, and approaching the uncertainty through a fuzzy logic system; constructing a fuzzy logic expansion state observer containing a time-varying gain function to estimate an unmeasurable speed and compensate interference and uncertainty; based on observer output, distributed errors are defined, a barrier Lyapunov function constraint boundary is introduced, and uncertainty and faults are compensated in combination with fuzzy and multiplicative fault adaptive laws. According to the method, the multiple water surface robots can form and keep the expected formation within the preset time, the tracking error is converged to the small neighborhood of the original point, the method does not depend on initial conditions, and the control precision and robustness are improved.

The invention discloses an automobile engine multi-mode electronic control system and a control method thereof, and particularly relates to the technical field of automobile engine multi-mode electronic control, the system comprises a sensor module, a mode switching module, an execution control module and a diagnosis module, the sensor module collects parameters such as rotating speed and air inlet pressure in real time; the mode switching module judges four working conditions of idling, cruising, accelerating and pressurizing based on threshold logic; the execution control module calls strategies such as PID (Proportion Integration Differentiation) control and EGR (Exhaust Gas Recirculation) rate calculation aiming at different modes to realize accurate adjustment of parameters such as throttle opening and boost pressure; the diagnosis module monitors faults of the sensor and the actuator in real time through voltage out-of-limit detection, position deviation comparison and temperature correlation analysis.

The invention discloses an unmanned aerial vehicle rotor fault tolerance control method based on micro simulation optimization, and relates to the technical field of unmanned aerial vehicle rotor control, and the method comprises the steps: constructing a high-fidelity micro four-rotor fault simulation platform, building an unmanned aerial vehicle four-rotor dynamic model, and simulating a single-rotor fault of any severity through fault injection; passive fault-tolerant controller training based on learning is carried out, a quad-rotor hovering stability control test is carried out through a high-fidelity micronizable quad-rotor fault simulation platform, and a controller is trained through micronizable simulation and BPTT; and training a selector through a Selector-Controller strategy network, carrying out rapid learning of a fault-tolerant control strategy, outputting a control instruction, and carrying out high-precision adaptive control in a quadrotor fault scene. According to the method, switching of a controller is not needed, any actuator fault scene can be covered, and it can be ensured that multiple rotors can stably fly in the whole actuator fault process.

The invention belongs to the technical field of fault diagnosis, and discloses a magneto-rheological semi-active suspension actuator fault diagnosis system and method, and the method comprises the steps: constructing a training set based on historical execution data; constructing a fault classification model based on a machine learning algorithm, and training the fault classification model by using the training set; and collecting real-time execution data, inputting the real-time execution data into the trained fault classification model, and outputting to obtain a fault type related to the real-time execution data. The fault classification model comprises a feature extraction layer, a feature fusion layer and a classification layer; the feature extraction layer extracts time domain fault features and dimension reduction frequency domain fault features through a convolutional neural network and a fast Fourier transform dual channel; and the feature fusion layer performs superposition fusion on the time domain fault features and the dimensionality reduction frequency domain fault features, and performs dimensionality reduction again on the combined fault features obtained through superposition fusion through kernel principal component analysis. The feature information integrity is ensured, and the diagnosis confidence is improved.