26 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 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.

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.

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 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.

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 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 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 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 invention discloses a point-to-point damping self-adaptive control method and system based on state driving, belongs to the technical field of mechanical arm control, and aims to establish a three-axis damping self-adaptive adjustment process by using a normalized singular value and a directional error from a single-step state of a mechanical arm so as to realize intelligent expansion of a traditional DLS controller. The sensitivity and residual response relation of the damping pseudo-inverse controller is accurately modeled according to the system state, a point-to-point prediction mechanism and a three-axis independent adjustment strategy are provided, and the method has obvious advantages in the aspects of precision, stability, real-time performance, structural migration and the like; the blank of damping parameter intelligent adjustment in current damping pseudo-inverse control is filled, a new state-driving-oriented numerical control parameter adjustment normal form is provided, and the method has remarkable engineering and scientific research values.

The application discloses a kind of aircraft multi-constraint anti-saturation adaptive attitude control method and system thereof, belong to aircraft control technical field.The six-degree-of-freedom attitude dynamics model of aircraft is established;The defined attitude dynamics model is converted into the affine nonlinear form facing control, and is divided into the outer loop of attitude angle and the inner loop of angular velocity;Non-singular terminal sliding mode surface and adaptive sliding mode control law are designed;Anti-saturation mechanism is designed and closed-loop system stability analysis is completed, the proposed guidance method can converge in finite time is verified by Lyapunov theory, the effectiveness of the proposed method is proved.The application is used to solve the problems caused by strong nonlinearity and strong coupling, parameter uncertainty and external disturbance, physical constraint, gain scheduling based PID control, dynamic inverse control and traditional sliding mode control.

The application discloses a kind of model experiment aerodynamic load high-precision and low-delay loading method, belong to the field of ocean engineering, comprising: obtaining the static characteristic curve and dynamic characteristic parameter of actuator;According to target working condition, generate target aerodynamic thrust sequence, based on static characteristic curve and dynamic characteristic parameter, calculate offline inverse control sequence by inverse dynamics model;In real-time loading stage, according to current physical time and pure lag time, determine look-ahead time, based on look-ahead time, carry out time domain interpolation in offline inverse control sequence to obtain the control instruction of current time and send to actuator.The application combines the hybrid control strategy of offline inverse model compensation and online time domain interpolation look-ahead, effectively eliminates the phase lag and amplitude attenuation caused by physical inertia of actuator, realizes high-precision, low-delay aerodynamic load loading, and is suitable for wind load equivalent simulation in scale model test of floating offshore wind turbine.

The invention discloses an attitude control method, system and device based on sensitive estimation, a medium and a product, and relates to the field of aircraft attitude control, and the method comprises the steps: constructing an attitude kinetic equation and an attitude kinematics equation of an aircraft; constructing an excitation-enhanced linear regression equation based on an attitude kinetic equation stabilization filter, and detecting the excitation intensity of the current aircraft state signal by adopting an excitation intensity detection algorithm; based on the linear regression equation and the excitation intensity detection result, identifying the aerodynamic parameters to be estimated by adopting an improved self-adaptive sensitive estimation algorithm to obtain estimated aerodynamic parameters; and determining an attitude control law of the aircraft by adopting a nonlinear dynamic inverse control algorithm based on the estimated aerodynamic parameters, the attitude kinetic equation and the attitude kinematical equation. According to the method, the aerodynamic parameters can be sensitively identified in real time by using the flight data, so that the adaptive control of the aircraft is realized.

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 relates to the technical field of flight simulation, in particular to a variable stability flight control method based on fixed time increment dynamic inversion, and the method comprises the steps: firstly building an aircraft six-degree-of-freedom dynamics and kinematics affine nonlinear model; secondly, establishing an airplane longitudinal and transverse course tracking reference model according to a flight quality evaluation criterion; and finally, designing an outer ring controller and an inner ring controller by utilizing a fixed-time convergence incremental dynamic inverse control method, and driving the airplane model to quickly and accurately track the control response characteristics of the reference model. According to the method, the capability of dynamically simulating different levels of flight quality in the same set of flight simulation model is realized, and the limitation of one type and one quality of a traditional flight simulation model is avoided; meanwhile, the scheduling work that a nonlinear model of the airplane needs to configure a controller at a plurality of trim points is reduced; the problem that after the model deviates from the trim point, the control response under the expected flight quality is difficult to generate is solved.

The application relates to an underwater three-dimensional cooperative guidance method based on angle constraint, and belongs to the technical field of underwater vehicle control. The leading missile adopts an optimal guidance method with angle constraint, and an additional maneuvering instruction is added to the state of the missile based on proportional guidance to control the state of the missile. On the basis of the dynamics and kinematics model of the vehicle, the required rudder angle is designed through three-channel decoupling control to control the attitude of the vehicle, and finally the cooperative guidance is realized. The key of the application lies in the design of the additional maneuvering instruction of the missile, which needs to design a nonlinear state tracking controller based on the dynamic inverse control principle. In order to solve the singularity problem of the angle in the navigation process, the design of the additional maneuvering instruction considers two attack conditions, and the multiple vehicles can hit the target on both sides of the target motion.

The present application relates to the field of numerical control machining and automation control technology, in particular to a numerical control machining adaptive control system and method based on multi-source data, comprising: FPGA hardware synchronous acquisition of spindle current, each feed axis servo current, cutting vibration and grating pulse, time base alignment packaging into processing multi-dimensional state matrix; Decoupling multi-physical field error through three-layer cascaded machine tool dynamics model, solving the deviation vector of the tool tip contour, and constructing the dynamic constraint boundary by fusing servo lag and cutting force deformation factors. When the deviation amplitude is out of limit, generate speed feedforward gain compensation instruction based on inverse control model for immediate suppression; When the oscillation elimination regression steady state is monitored, trigger the full-time domain data flow interception logic, and generate process optimization instruction by backtracking. The present application solves the problem of nonlinear time-varying error decoupling and real-time compensation lag under multi-physical field coupling, and significantly improves the dynamic accuracy and process stability of numerical control machining under complex working conditions.

The application discloses a transition mode comprehensive weight intelligent control method for a compound wing unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle flight control and intelligent control. In view of the problems of lift switching, nonlinear coupling, control chattering and stall risk in the transition from a rotor mode to a fixed wing mode, a dual-variable comprehensive weight distribution mechanism with airspeed dominance and angle of attack correction is adopted to construct rotor / fixed wing control weights which are continuous, smooth and meet the normalization constraint; meanwhile, the unmanned aerial vehicle is modeled as an integral nonlinear system, and strong robustness, low chattering and high precision integral control input solutions are realized through integral sliding mode control, neural network disturbance compensation and Moore-Penrose pseudo-inverse control distribution; finally, the control commands are distributed to the rotor, rudder and propulsion system according to the comprehensive weight, and the control dominance is smoothly transferred. The height and attitude stability, anti-interference ability and flight safety margin in the transition stage are improved, the engineering realizability is strong, and the method is suitable for mode transition control of various compound wing unmanned aerial vehicles.

The application discloses a flexible continuum manipulator and a damping Jacobian pseudo-inverse control method of the manipulator, and the method comprises the following steps: S1, a mapping relationship from a joint space to an operation space is established, and a homogeneous transformation matrix of an end relative to a reference coordinate system is obtained; S2, a mapping relationship from the joint space of the flexible continuum manipulator to a driving space is established; S3, a forward kinematics relationship from the driving space to the joint space is established; S4, a Jacobian matrix from the driving space to the operation space is established based on a chain rule; S5, a step factor and a damping factor initial value are set, and an improved damping least square Jacobian pseudo-inverse control model is established; S6, initial and final positions are differentiated by interpolation, and a driving space increment is iteratively solved; and S7, two flexible joints of the flexible continuum manipulator are controlled to reach a target position according to the driving space increment obtained by solving. On the basis of realizing high-precision control, the application avoids great changes of driving amount of the continuum manipulator at singular points.

A method of adaptive current control for an alternating-current AC servo motor includes acquiring an initial three-phase AC motor current; inputting the initial three-phase AC motor current to a current vector transformation module to generate first target data; inputting the first target data to a current control loop, and simultaneously taking time derivative of the first target data to generate second target data; and inputting both of the second target data and an output of the current control loop to an incremental dynamic inversion control module, and inputting an output of the incremental dynamic inversion control module to the current vector inverse transformation module. The method eliminates a need of motor model with the controller parameters being tuned, thereby overcoming the technical problems in conventional technologies that motor model identification, particularly online model identification, significantly increases system complexity.