54 results about "Inverse control" patented technology

The invention discloses a PMSM speed regulation method (ILADRC) based on improved RLS identification and dynamic inverse control. According to the method, a layered framework is adopted; a linear expansion observer with fixed parameters is used for a bottom layer to ensure observation stability; the middle layer constructs a parameter identification regression model based on a generalized disturbance observation value by using an improved recursive least square algorithm, introduces an online correction mechanism, and performs online decoupling identification on input gain and pure physical disturbance of a speed ring by using the generalized disturbance observation value and a current instruction; and the upper layer reconstructs an adaptive dynamic inverse control law based on an identification result, weakens an algebraic coupling loop of observation and control, realizes no-static-error and no-overshoot rapid tracking of the system on a rotating speed instruction under a parameter mismatch working condition in combination with an acceleration error integral enhancement item, and effectively improves the dynamic performance and robustness of the motor under a parameter perturbation working condition.

The invention relates to the technical field of numerical control machining and automatic control, in particular to a numerical control machining self-adaptive control system and method based on multi-source data, and the method comprises the steps that FPGA hardware synchronously collects main shaft current, servo current of each feed shaft, cutting vibration and grating ruler pulse, and time bases are aligned and packaged into a machining multi-dimensional state matrix; the multi-physical field error is decoupled through a three-layer cascade machine tool dynamics model, a tool nose contour deviation vector is solved, and servo lag and cutting force deformation factors are fused to construct a dynamic constraint boundary. When the deviation amplitude exceeds the limit, generating a speed feed-forward gain compensation instruction based on an inverse control model to perform instant suppression; when it is monitored that oscillation is eliminated and returns to a steady state, full-time-domain data stream interception logic is triggered, and a process optimization instruction is generated through backtracking. According to the method, the problems of nonlinear time-varying error decoupling and real-time compensation lag under multi-physics field coupling are solved, and the dynamic precision and process stability of numerical control machining under complex working conditions are remarkably improved.

The invention discloses a tilt rotorcraft decoupling flight control method based on model compensation. The tilt rotorcraft decoupling flight control method comprises the steps that a flight control system reads airborne sensor data in real time; according to control input acquired in real time and airborne sensor data, superposing force and torque generated by each component by utilizing a nonlinear dynamic model of the tilt rotorcraft to obtain the stress condition of the rotorcraft body; determining a first-order differential of the model state quantity of the tilt rotorcraft; linearizing the nonlinear dynamic model, extracting a control matrix and constructing attitude inner ring virtual control input; on the basis of inversion of the transfer function, constructing an inverse model expression of each channel of the tilt rotorcraft; constructing an error compensation link of each channel to compensate a model error; and minimizing an increment norm of the compensated virtual control quantity through a weighted pseudo-inverse control distribution model to obtain an increment of the control quantity, and forming a final execution command in combination with the current total pitch and the helm deflection angle to be sent to a rotor and control surface execution mechanism of the tilt rotorcraft.

The invention discloses an intelligent shock absorption and noise reduction control method for rail transit, and belongs to the technical field of rail transit noise and vibration control. The method aims at solving the problems that traditional passive control measures are poor in adaptive capacity and limited in frequency band, and effective prospective offset is difficult to achieve due to system delay and inaccurate reference signals in existing active control. The method comprises the following steps: synchronously acquiring vibration and noise signals through a sensor array; calculating the contribution weight of vibration to noise by using a cross-domain modal correlation module based on an attention mechanism, and fusing the features; predicting an evolution sequence of vibration and noise in a specific time period in the future by adopting a long-short-term memory network model; synthesizing a target reference signal according to the prediction sequence, and generating an anti-phase control signal through an adaptive filtering algorithm; finally, it is ensured that the total delay of the system is smaller than the feedforward advance time, and an actuator is driven to achieve accurate foresight counteracting. The method is mainly used for improving the self-adaptive capacity and the control effect of the rail transit damping and noise reduction system.

The invention discloses a fruit and vegetable greenhouse carbon dioxide concentration adjusting method based on neural network optimization and nonlinear dynamic inverse control. According to the method, environmental parameters and fruit and vegetable growth information are collected in real time through a sensor, after data cleaning and Z-score standardization preprocessing, a nonlinear mapping relation between parameters and carbon dioxide concentration is established through an LSTM model, and LSTM hyper-parameters and NDI control parameters are synchronously optimized through a self-organizing memory optimization algorithm. And a system inverse model is constructed by combining nonlinear dynamic inverse control, nonlinear dynamic is counteracted, and real-time accurate adjustment of the carbon dioxide concentration is realized. The future concentration trend is predicted based on LSTM, ventilation equipment and carbon dioxide supply amount are dynamically adjusted, and the model is periodically updated to adapt to environmental changes. The problems that a traditional method is low in control precision, poor in adaptability, high in energy consumption and the like are solved, the regulation and control efficiency and stability are remarkably improved, and resource waste is reduced.

The invention discloses a variable length control method with a limited processing state. According to the method, an iteration length selection index is constructed, so that the state value of the system does not violate a given limit range. Under the framework of a backstepping method, vector parameter uncertainty and disturbance of the system are converted into a scalar form for analysis. A controller without a reversible control matrix is constructed to control a high-order system. Compared with a traditional control method needing an accurate mathematical model, the control algorithm provided by the invention is more novel and simple in application condition. When the method is used for repeated task operation, the control precision is effectively improved, and the method has good engineering application value.

A cable force control method includes: establishing a friction model, and calibrating parameters; and calculating the parameters in real time, and controlling a force: identifying the parameters according to the friction model to obtain parameters of an auxiliary cable Bowden system and a power Bowden system: a friction coefficient μa of the auxiliary cable Bowden system, and a friction coefficient μp of the power cable Bowden system; calculating the auxiliary cable Bowden system θa in real time according to the model and a force value of a sensor, and using same as a cable bending angle of the power cable Bowden system θp; and obtaining an inverse control formula Fin=Fout·e−uλθ according to the friction model, and bringing the power cable Bowden system θp into the inverse control formula to serve as a feedforward controller, so as to achieve an effect of real-time force control.

The invention provides an air-ground autonomous landing control method for a fixed-wing unmanned aerial vehicle facing a movable base platform. The air-ground autonomous landing control method comprises the following steps: firstly, establishing a kinetic model of the fixed-wing unmanned aerial vehicle and a relative motion model of the unmanned aerial vehicle and a movable base; secondly, considering the influence of the ground effect, the wake flow of the movable base and the movement of the movable base on the landing precision, and respectively introducing disturbance models; then designing a trajectory control law based on a nonlinear L1 control law, and controlling the unmanned aerial vehicle to slide down to a target point along an optimal trajectory by generating a trajectory acceleration instruction in real time; designing a hierarchical attitude control method based on incremental nonlinear dynamic inverse, designing an angular velocity control law for an outer ring attitude angle by adopting a nonlinear dynamic inverse control method, and obtaining an angular acceleration control law for an inner ring angular velocity by adopting an incremental nonlinear dynamic inverse method; and finally, incremental power compensation accelerator control is designed, speed error feedback is introduced, and dynamic adjustment of a thrust instruction is achieved. The problem of strong coupling interference in the landing process of the unmanned aerial vehicle on the dynamic base platform is solved.

The invention discloses an unmanned aerial vehicle trajectory tracking high maneuverability control method based on incremental dynamic inversion, and the method comprises the steps: designing an outer ring path tracking guidance law, and calculating a needed acceleration instruction according to the deviation between the current position of an unmanned aerial vehicle and a reference trajectory, so that the unmanned aerial vehicle can track the reference trajectory; combining with an acceleration instruction calculated by a guidance law, considering gravitational acceleration, distributing transverse and longitudinal expected accelerations of an airframe, and solving expected airflow angles (an incidence angle and a sideslip angle); an incremental nonlinear dynamic inverse control algorithm is designed, and tracking of an expected attack angle, a sideslip angle and a rolling angle around a speed axis is achieved by controlling rudder deflection of a fuselage. By using the method, the expected trajectory and maneuvering attitude can be tracked on the premise of high maneuverability. The method can be widely applied to the field of unmanned aerial vehicle control.

This invention discloses an adaptive inversion control method for variant aircraft based on RBF neural networks, comprising the following steps: setting up an RBF neural network to estimate the unknown influence term d of the trajectory inclination angle caused by model uncertainty due to aircraft deformation and external disturbances. γ And the unknown influence term d of pitch angular velocity q This invention discloses an adaptive inverse control method for variant aircraft based on RBF neural networks. The method uses RBF to approximate unknown influence terms and compensates for them in the control law, thereby improving the accuracy and robustness of the controller. The adaptive weights of the RBF neural network for unknown influence terms of ballistic tilt angle and pitch angular velocity are obtained and used to set up an inverse adaptive controller. The adaptive weights of the RBF neural network are then added to the control law of the controller, and the variant aircraft is controlled through the inverse adaptive controller.

The present invention discloses a flying wing aircraft attitude control method based on neural network incremental dynamic inversion, the steps include: establishing a nonlinear model of the flying wing aircraft attitude angle and angular velocity loop based on the principle of time scale separation; designing the attitude angle loop control law based on the dynamic inversion method, and designing the angular velocity loop control law based on the incremental dynamic inversion method; introducing a single hidden layer neural network to online compensate for the inverse error caused by sensor delay, external interference and inaccurate modeling, so as to ensure that the aircraft attitude angle can achieve the expected dynamic performance. The present invention utilizes the characteristics of the neural network to fit unknown dynamics online, solves the inverse error caused by sensor delay in the traditional inverse control method, further improves the robustness of the controller under the premise of ensuring that the controller is insensitive to the dynamics of the flying wing aircraft and external interference, and improves the dynamic performance and steady-state performance of the flight attitude.

The invention belongs to the technical field of robot control, and particularly relates to a preset performance control method of a mechanical arm with output constraint based on a self-adaptive sliding-mode observer, which comprises the following steps: obtaining a first system model of a kinetic equation of the mechanical arm according to a Newton-Euler equation; a nonlinear part in the first system model is represented as one item in a concentrated manner, so that the first system model of the kinetic equation is rewritten into a more concise form, and a second system model of the kinetic equation of the mechanical arm is obtained; aiming at the problem that the speed of the second system model of the mechanical arm cannot be measured, designing a self-adaptive sliding-mode observer to estimate the speed of the mechanical arm on line; a system tracking error is defined based on estimated position and speed signals, and a novel construction method of a time-varying preset boundary based on priori knowledge of output constraint is designed for the output position constraint problem and the preset performance control problem of a second system model of the mechanical arm. In combination with an inversion control method and a novel preset performance boundary, a barrier Lyapunov function is constructed, and then an intermediate virtual control signal and a system input control law are derived. According to the technical scheme, breakthrough is achieved in the field of robot control, the problems of unobservable speed state, output constraint and preset control of the mechanical arm are considered and processed at the same time, and the industrial problems of transient response and steady-state precision in global tracking control of the mechanical arm with the output constraint are effectively solved.

The present invention discloses a dynamic model adaptive trajectory tracking control method for a deformable UAV, including: establishing a dynamic model of the deformable UAV; constructing a control framework based on the model, wherein: flat mapping is performed according to a given reference trajectory, the desired state feedforward term is calculated according to the current estimated or measured state, and flight control is realized according to the quadrotor geometric control theory; deformation control is realized based on feedback control of the servo motor angle error; the uncertainty factors in the grasping process of the deformable UAV are modeled as residual terms composed of external forces and corresponding torques, and the L1 adaptive control algorithm is used to adaptively compensate for the external forces, and the incremental nonlinear dynamic inverse control algorithm is used to compensate for the external torque, thereby realizing adaptive trajectory tracking control. Flight control is performed based on the dynamic model of the deformable UAV, and external forces and torques are observed and compensated in real time, effectively solving the uncertainty problem and the influence of external disturbances.

The application discloses a control method for an underwater wireless power transmission device. The application is characterized in that: a primary sensor is used to collect input and output voltage values of the underwater wireless power transmission device, and inverse modeling of the underwater wireless power transmission device is performed in a computer. According to a pre-set expected output voltage value of the underwater wireless power transmission device, an inverse model of the underwater wireless power transmission device obtained by the inverse modeling is used to calculate an output voltage value of the inverse model; then, the input voltage value of the underwater wireless power transmission device is adjusted according to the obtained output voltage value of the inverse model. The output voltage value of the underwater wireless power transmission device is the pre-set expected output voltage value of the underwater wireless power transmission device; and finally, the output voltage control of the underwater wireless power transmission device is realized. The application uses the inverse control method to improve the adjustment speed and output voltage precision of the underwater wireless power transmission device in the underwater environment.

This invention discloses an interactive multi-model adaptive active vibration control method based on variational Bayesian methods, belonging to the field of vibration control technology. The method includes establishing a system model set containing multiple potential dynamic modes and initializing each model; performing variational Bayesian iteration on each model within each control sampling period; repeating the above process for each channel to obtain the optimal state estimate for each channel; fusing the state estimates of multiple channels using a covariance cross-fusion method to calculate the global optimal state estimate; convolving the global optimal state estimate with a reference signal filtered by the secondary channel model to generate an inverse control signal, which drives an actuator via a power amplifier to produce a secondary force that counteracts the original vibration, thus achieving vibration suppression. This invention achieves high-precision and highly robust vibration suppression by fusing variational Bayesian adaptive Kalman filtering with interactive multi-model estimation.

A control apparatus for a vehicle including (i) an input device which is configured to receive a first predetermined operation to request a first predetermined function and a second predetermined operation to request a second predetermined function, and (ii) a steering wheel to which the input device is fixed. The control apparatus is configured to execute a first inverse control and / or a second inverse control. In execution of the first inverse control, the first predetermined function is performed even when the input device receives the second predetermined operation, in a case when a steering angle of the steering wheel is in a first predetermined angle range. In execution of the second inverse control, the second predetermined function is performed even when the input device receives the first predetermined operation, in a case when the steering angle is in a second predetermined angle range.

The invention discloses a fixed time increment fault-tolerant control method for a control surface mixed fault aircraft. The method comprises the following steps: analyzing the fault type and performance of an aircraft control surface; modeling a control surface fault aircraft; improving the design of a tracking differentiator; designing an incremental dynamic inverse control law; and designing a fixed time increment dynamic inverse fault-tolerant controller. The control method provided by the embodiment of the invention is low in cost and simple in structure, can realize accurate acquisition of the angular acceleration signal and noise filtering, reduces hardware consumption of the angular acceleration sensor, and has a practical engineering application prospect; the robustness is high, and stable control and instruction tracking of the aircraft under the control surface mixed fault can be achieved; the method is high in convergence speed and good in steady-state performance, can guarantee that the faulty aircraft quickly recovers to be stable in a fixed time and accurately tracks the instruction, and effectively improves the flight performance of the faulty aircraft.

The present invention provides a control algorithm for a multi-physics field coupling test device, the control algorithm includes a real-time control system, the multi-physics field coupling test device (1) also includes a vibration table array component (30), a rainfall component (40) and a landslide component (50), the real-time control system includes a first earthquake controller (5), a rainfall controller (9) and a second earthquake controller (6), and the real-time control system includes a real-time force feedback controller, an identification error compensator and an adaptive inverse controller. The present invention proposes a test device for multi-physics field coupling, which can realize different earthquake excitations and rainfall effects on different landslide areas (top and bottom of the slope). At the same time, based on the real-time force feedback control method, an adaptive inverse control strategy considering the system identification error is proposed, which can effectively improve the simulation accuracy of multi-physics field coupling.

The invention provides a vehicle trajectory prediction method and system based on intention anchoring and micro reverse control inference, and belongs to the technical field of vehicle trajectory prediction. A high-dimensional context feature tensor containing space-time interaction information of a target vehicle and traffic participants in the surrounding environment is obtained and input into a deterministic intention inference network; predicting a plurality of potential driving intention anchor points of the target vehicle at the future end moment, and mapping the intention anchor points back to the feature space to realize conditional guidance; on the basis of a probability microkinematics decoder and the guided features, probability distribution of bottom layer control signals in a future time sequence of the vehicle is predicted, and an instantaneous discrete control vector is obtained; and inputting the instantaneous discrete control vector into a differentiable vehicle kinematics model embedded in a deep neural network to generate a prediction trajectory. The track generated by the method is smooth and strictly conforms to the vehicle underlying dynamics constraint, and highly reliable support is provided for safety decision and chassis motion planning of the automatic driving vehicle in a complex traffic scene.

The invention discloses a tilt rotorcraft transition mode robust flight control method based on dynamic inversion. The method comprises the steps that a flight control system of a tilt rotorcraft judges whether the tilt rotorcraft is in a transition mode or not based on the current nacelle tilt angle and the airspeed of a rotorcraft body; for an angular velocity ring, an inner ring nonlinear dynamic inverse control law is constructed, and Iamp is combined; the I algorithm carries out online estimation and compensation on system disturbance; designing a nonlinear dynamic inverse angular velocity controller, and performing inner ring control distribution; for the attitude ring, Euler angle and angular rate mapping and control gain adjustment are carried out; the outer ring takes an expected airspeed and a climbing rate as targets, calculates a dynamic relation of an outer ring state by using a reduced order model, constructs an outer ring nonlinear dynamic inverse control law, generates a pitch angle increment and a total distance increment through weighted pseudo-inverse distribution, and completes control instruction distribution in a forward velocity channel and a vertical velocity channel respectively; and finally, a total pitch angle and a total pitch angle are formed by superposing control instructions and sent to an inner ring for execution.

The invention discloses a flexible continuum mechanical arm and a damping Jacobian pseudo-inverse control method thereof, and the method comprises the following steps: S1, building a mapping relation from a joint space to an operation space, and obtaining a homogeneous transformation matrix of a tail end relative to a reference coordinate system; s2, establishing a mapping relation from a joint space to a driving space of the flexible continuum mechanical arm; s3, establishing a forward kinematics relation from the driving space to the joint space; s4, establishing a Jacobian matrix from the driving space to the operation space based on a chain rule; s5, setting initial values of a step length factor and a damping factor, and establishing an improved damping least square Jacobian pseudo-inverse control model; s6, interpolating and differentiating initial and final positions, and iteratively solving a driving space increment; and S7, two flexible joints on the flexible continuum mechanical arm are controlled to reach the target position according to the solved driving space increment. According to the invention, on the basis of realizing high-precision control, huge change of the driving quantity of the continuum mechanical arm at the singular point is avoided.

The embodiment of the invention provides an output feedback inverse control method of a hysteresis nonlinear system and computer equipment, and relates to the technical field of system control, and the method can utilize a high-gain K filter observer to carry out estimation on an internal state variable of a nonlinear control object mathematical model in real time, and simulate and generate a feedback signal. And calculating a control law based on the feedback signal, and controlling the nonlinear control object according to whether the control law is calculated for the first time or not. Wherein when the control law is not calculated for the first time, whether the control law meets the updating condition needs to be determined based on the calculated control law and the self-adaptive updating rate calculation logic, and when it is determined that the control law meets the updating condition, the control law is calculated again based on the feedback signal, and the non-linear control object is controlled based on the re-calculated control law. According to the method, the high-gain K filtering observer is used for accurately estimating the state, the control law is adaptively updated, and the control precision and stability of the hysteresis nonlinear system can be effectively improved.

The invention discloses an unmanned aerial vehicle adaptive anti-interference precise landing control system for urban logistics, and the system comprises the following modules: a fractal intention tensor body construction module which is used for constructing a fractal intention tensor body; the asymmetric self-evolution intention generation network module is used for generating an intention section sequence; an anti-causal state projector module for generating a plurality of virtual future state nodes; the back projection function construction module is used for performing inverse mapping on each virtual future state node to generate a back-stepping control path set; the disturbance guide path search module is used for sampling and variation by adopting a disturbance guide Monte Carlo path search algorithm to generate an optimal control path; the anti-interference control instruction generation module is used for outputting an anti-interference control instruction; and the flight feedback updating module is used for collecting execution feedback information of the unmanned aerial vehicle and updating state evolution distribution of the fractal intention tensor body. According to the method, tensor modeling and disturbance optimization path backstepping are fused, and anti-interference accurate landing of the unmanned aerial vehicle is achieved.

This invention relates to the field of attitude vibration reduction control for quadrotor aircraft, specifically disclosing a method and system for attitude vibration suppression control of a rotary-wing UAV based on additive decomposition notch filtering. The method includes: Step S1, based on the basic dynamic model of the quadrotor aircraft, introducing a blade eccentricity model with an unbalanced mass model to obtain a vibration suppression dynamic model for the quadrotor aircraft; Step S2, constructing an output vector based on the designed output matrix and the state variables in the vibration suppression dynamic model; Step S3, calculating the dynamic first derivative of the output vector along the vibration suppression dynamic model; Step S4, designing a traditional additive decomposition dynamic inverse controller based on the first derivative dynamics, and improving the filter in the traditional additive decomposition dynamic inverse controller using a notch filter to obtain an additive decomposition notch dynamic inverse controller; Step S5, using the additive decomposition notch dynamic inverse controller to perform attitude vibration suppression control on the rotary-wing UAV.

The present application relates to a kind of cone-shaped dielectric elastomer actuator (Dielectric Elastomer Actuator, DEA) implicit inverse control method for input square state constraint, aims at effectively reducing the influence of hysteresis on system control performance, and solve the input square full state constraint problem in DEA control, including the following steps: based on traditional Preisach model, input square Preisach model is proposed, the hysteresis modeling when input is voltage square term in DEA system is realized.Combined with Fuzzy Logic System (FLS) and Barrier Lyapunov Function (BLF), a new adaptive fuzzy segmented implicit inverse controller is proposed for DEA system with input square state constraint.Based on the control platform built, compared with the controller without implicit inverse and the traditional Backstepping controller, the accuracy and effectiveness of the designed control strategy are verified, the proposed control algorithm has higher precision, and the high-precision control goal of dielectric elastomer actuator is realized.

The invention relates to the technical field of intelligent material driving control, provides a self-adaptive pseudo-inverse control method of an output constraint interconnection system with hysteresis, and aims to realize high-precision control of an intelligent material actuator. Comprising the following steps: designing a high-gain state observer, and deducing a stability condition of a system under disturbance; thirdly, designing a hysteresis temporary controller to improve the robustness and response capability of the controller; and finally, designing a pseudo-inverse algorithm to improve the accuracy and real-time performance of system response. The control precision of the provided control scheme is improved by two times compared with the control precision of a control scheme without considering hysteresis, and is improved by three times compared with the control precision of a PID (Proportion Integration Differentiation) control scheme, so that the provided control scheme has a better control effect.

The invention relates to a flight control reconstruction method and system in a control surface fault state and a readable storage medium, and the method comprises the steps: S1, constructing a dynamic model of each control surface of a tandem wing aircraft, and detecting the state of an execution mechanism in real time according to a current rudder instruction and rudder feedback; s2, judging whether an execution mechanism is normal or not, and if the execution mechanism is abnormal, dividing a fault state into a steering engine stuck state and a control surface floating state according to control surface angle feedback; s3, defining four control surfaces of the tandem wing aircraft, and dividing the four control surfaces into six fault conditions according to fault positions and numbers; s4, redistributing the control surface, and converting the control torque into the deflection angle of the normal control surface by using the disturbance torque brought by the control surface balance fault in a normal state and adopting a generalized inverse control distribution method; and S5, after control surface redistribution, the aircraft is controlled to land according to the planned trajectory. According to the method, the control surfaces can be redistributed based on the real-time fault detection state, and the control surfaces in the normal state are utilized to balance the disturbance torque brought by the control surface faults.

The application belongs to the technical field of flying wing unmanned aerial vehicle control, and discloses a flying wing unmanned aerial vehicle dynamic inverse glide control method based on cascade observation. First, a flying wing unmanned aerial vehicle dynamics model is established, a nonlinear dynamic inverse method is adopted to design attitude loop and angular velocity loop control law respectively, and unmanned aerial vehicle three-channel dynamic decoupling control is realized. In view of the problem that traditional dynamic inverse control is sensitive to model accuracy and weak in anti-interference ability, a cascade extended state observer is introduced to realize real-time estimation and dynamic compensation of model uncertainty and wind disturbance. Meanwhile, a control distribution strategy based on weighted pseudo-inverse is designed to realize high-precision distribution of control moment. The simulation result shows that the method can effectively inhibit the three-channel coupling effect in the glide process of the unmanned aerial vehicle, significantly improve the trajectory tracking accuracy, attitude stability and system robustness, and adapt to the high-precision glide control requirement of the flying wing unmanned aerial vehicle.

This disclosure provides a method and system for flight control of a drone used for inspecting oil and gas pipelines in mountainous areas. Relating to the field of flight control, this invention employs a dynamic inverse control law to linearize and decouple the system compared to traditional PID controllers (Proportional-Integral-Derivative controllers), achieving excellent control performance for high angle-of-attack maneuvers of fixed-wing drones. Furthermore, the introduction of a disturbance observer effectively estimates the total disturbance caused by wind disturbance and model uncertainties, compensating for it in the dynamic inverse controller and improving system robustness. Considering the nonlinear characteristics of the system, it effectively addresses the problem that commonly used linear control algorithms cannot meet the requirements for flight robustness. This invention is used for inspecting oil and gas pipelines in mountainous areas.

The present invention proposes a butterfly hysteresis modeling and adaptive neural segmented implicit inverse control method and device, which not only outperforms other control methods but also reduces tracking errors. In addition, the proposed segmented implicit inverse algorithm greatly reduces the amount of calculation and search time for extracting the actual control signal from the delayed temporary control signal. It includes: step 1: establishing a control system model with butterfly hysteresis characteristics; step 2: establishing a butterfly KP model based on the KP model for the butterfly hysteresis characteristics reflected in the model; step 3: based on the established butterfly KP model, proposing an adaptive neural segmented implicit inverse control method for butterfly hysteresis to eliminate butterfly hysteresis; step 4: using a neural network to approximate an unknown nonlinear function, and using adaptive dynamic surface control technology, performing adaptive dynamic surface control on the nonlinear control system with butterfly hysteresis characteristics, thereby achieving high-precision control of the control system.