38 results about "Observer based" patented technology

The invention discloses a predetermined time expansion state observer generation method and an observation error convergence method, which are applied to the technical field of aerospace, and comprise the following steps: constructing a controller for a second-order nonlinear system according to an all-drive system theory; converting the second-order nonlinear system according to the controller to obtain an all-drive system based on the main controller; wherein the main controller is a controller for controlling the all-wheel-drive system; designing a predetermined time expansion state observer based on the total disturbance and observation error of the all-wheel-drive system; wherein the predetermined time expansion state observer is an observer which limits the upper bound of convergence time of the observer based on a predetermined time parameter. Compared with most existing extended state observers which can only ensure asymptotic convergence and finite time or fixed time convergence, the observer generated based on the predetermined time extended state observer generation method can ensure that the observation error converges within the predetermined time, so that the converged predetermined time is more stable; and the user experience is improved.

The invention discloses a method and a device for designing a multi-anti-interference incremental backstepping observer based on specified time, and belongs to the technical field of signal tracking and processing. The method comprises the following steps: establishing a system dynamics model containing time-varying disturbance and model uncertainty items; scaling the tracking error and the estimation error by using a scaling function so as to realize the accurate convergence of the system state and the disturbance estimation within the specified time T; combining the scaled error, introducing an incremental backstepping control thought, and designing an incremental backstepping observer capable of processing model uncertainty and multiple disturbances at the same time; under the condition that the total disturbance and the derivative thereof are assumed to be bounded, stability analysis is carried out through a Lyapunov function, and constraint conditions of system stability convergence are deduced; and finally, verifying the effectiveness and superiority of the observer under various input and disturbance working conditions through simulation. The method is suitable for a high-dynamic and time-sensitive precision control system.

The invention discloses a robot anti-interference control method based on a neural network interference observer, and the method specifically comprises the following steps: 1, building a robot dynamics model containing external interference, and constructing a state space model; step 2, establishing a fixed time interference observer based on a radial basis function neural network, and performing external interference estimation on the robot kinetic model; and step 3, a fixed time sliding mode controller is designed based on the interference estimation result, and fixed time control under the condition of external interference is realized. According to the robot anti-interference control method based on the neural network interference observer, the interference is accurately and rapidly estimated by using the fixed time interference observer, the convergence of the system state in the fixed time is realized by combining the fixed time sliding mode controller, and the feedforward compensation and feedback suppression of the interference are combined, so that the system stability is improved. The method has the advantages of simple implementation mode, high interference estimation precision and high response speed.

The embodiment of the present application relates to the technical field of computer software, and discloses a multilingual dynamic switching method based on an observer mode and related devices, the method comprising: when a program starts, a resource management module asynchronously loads translation resources of a default language and preloads commonly used language resources to a cache, and a mapping relationship between text keys and translation resources is established; each interface control is registered to an observer registration module as an observer, and a layered control tree of the observer is formed; when a user switches languages, the resource management module loads target language resources; a difference detection module compares differences between texts, and identifies an observer set that needs to be updated; an event bus module publishes layered update events according to the observer set that needs to be updated and a current interface state; and an update strategy module controls the observer layer that needs to be updated to execute the update of the interface control according to the layered update events and the target language resources. The embodiment of the present application realizes efficient, intelligent and extensible multilingual dynamic switching of an interface.

The application provides a vehicle suspension state parameter estimation method and device, and relates to the technical field of intelligent control of vehicle suspension, and the method comprises the steps of: constructing an interactive state observer based on at least two physical characteristic parameters of a vehicle suspension; determining prior state prediction parameters corresponding to each sub-observer based on a previous model probability and a transition probability matrix corresponding to each sub-observer; updating the prior state prediction parameters of each sub-observer based on current observation data corresponding to each sub-observer to obtain posterior state estimation parameters of each sub-observer; adaptively determining a current forgetting factor corresponding to each sub-observer based on the current observation data, an observation matrix and the prior state prediction parameters of each sub-observer; and fusing the current model probability, the posterior state estimation parameters and the current forgetting factor corresponding to all sub-observers to obtain a current overall state parameter estimation value corresponding to the interactive state observer. The application can improve the estimation accuracy of the suspension state parameters.

The invention relates to a large-diameter bearing quenching optimization method and system based on interactive multi-model estimation. The method comprises the following steps: determining state parameters of a quenching process; through the state parameters and a holosymmetric polytope method, an integrated nonlinear system is constructed; obtaining a linear expression of the nonlinear system through a Taylor expansion method with remainder terms; based on the linear expression, at least determining prediction equations of the two observers; calculating a weight and a set membership estimation initial value of each observer based on a preset interactive fusion rule and the historical state parameter value; calculating a residual set of each observer based on the prediction equation and the set membership estimation initial value of each observer; updating the weight of each observer in real time according to the residual set and the probability model; and calculating an estimated value of the nonlinear system through an interactive fusion rule. According to the method, through multi-model probability interaction and linearization of the set membership set, the accuracy and robustness of quenching control of the large-diameter bearing are improved.

The application discloses a humanoid robot obstacle-crossing reinforcement learning control method based on DCM constraint, an external force observer based on hyperbolic tangent sliding mode compensation is designed for quickly estimating external force impact, and is used for estimating external force impact suffered by the robot; a motion divergence quantity (DCM) model considering external force influence is established based on a traditional linear inverted pendulum model, and a reward function based on DCM optimization is designed to improve stability. On this basis, a multi-dimensional reward function covering dynamic leg lifting height reward, foot landing area reward, obstacle-crossing reward and the like is designed to prevent training from falling into local optimum. In view of the problems of slow training speed and excessively high calculation cost in the simulation environment where a visual sensor is used to identify the terrain, a sampling point grid based on terrain height is designed to improve the perception accuracy and real-time performance of the system on complex obstacle terrains.

The embodiment of the invention relates to the technical field of computer software, and discloses a multi-language dynamic switching method based on an observer mode and a related device.The method comprises the steps that when a program is started, a resource management module asynchronously loads a translation resource of a default language and preloads a common language resource to a cache; establishing a mapping relationship between the text key value and the translation resource; each interface control serves as an observer to be registered to the observer registration module, and a hierarchical control tree of the observer is formed; when a user switches languages, the resource management module loads target language resources; the difference detection module compares the differences between the texts and identifies an observer set needing to be updated; the event bus module issues a hierarchical update event according to an observer set needing to be updated and a current interface state; and the updating strategy module controls the observer needing to be updated to hierarchically execute updating of the interface control according to the hierarchical updating event and the target language resource. According to the embodiment of the invention, efficient, intelligent and extensible multi-language dynamic switching of the interface is realized.

The invention relates to the technical field of spacecraft control, and provides a spacecraft rendezvous control method based on a disturbance observer and related equipment, and the method comprises the steps: constructing a kinetic model of orbit rendezvous of a target spacecraft at an earth-moon translation point, and designing the disturbance observer based on the kinetic model; defining a cost function based on the system state quantity of the target spacecraft, and constructing an action controller of the target spacecraft according to the cost function; calculating a control interference estimated value of the target spacecraft according to the interference observer, and calculating an optimal control value according to the action controller; and calculating a final control value of the target spacecraft based on the control interference estimated value and the optimal control value, and performing orbit intersection control on the target spacecraft based on the final control value. According to the method, the spacecraft rendezvous control precision can be improved.

The application discloses a kind of cluster unmanned plane fixed time quick adaptive fault-tolerant cooperative control method, first, design a kind of elasticity distributed fixed time observer based on topology switching signal, for accurately estimating virtual leader state and weakening the influence of denial-of-service attack.Second, by combining power integrator technology with topology switching signal, for follower unmanned plane affected by unknown actuator fault, construct fixed time adaptive fault-tolerant cooperative control protocol, so that the cluster unmanned plane system can still realize trajectory tracking in fixed time under the condition of actuator fault and denial-of-service attack coexistence.Finally, by radial basis function neural network, online approximation of composite nonlinear disturbance is realized, more accurate cooperative control is realized.The application can be applied to cluster unmanned plane formation fault-tolerant cooperative control under actuator fault and denial-of-service attack.

The application relates to the technical field of spacecraft control, and provides a spacecraft rendezvous control method based on an interference observer and related equipment, the method comprising the following steps: constructing a dynamic model of target spacecraft rendezvous on a geosynchronous orbit, and designing an interference observer based on the dynamic model; defining a cost function based on system state quantities of the target spacecraft, and constructing an action controller of the target spacecraft according to the cost function; calculating a control interference estimation value of the target spacecraft according to the interference observer, and calculating an optimal control value according to the action controller; calculating a final control value of the target spacecraft based on the control interference estimation value and the optimal control value, and performing orbit rendezvous control on the target spacecraft based on the final control value. The method can improve the precision of spacecraft rendezvous control.

The application is directed to the problem of fault-tolerant formation control of fuzzy multi-leader UAV system. A fault-tolerant formation control method based on reduced-order observer is proposed, including: using T-S fuzzy model to model the multi-leader UAV system; transforming the original system into a reduced-order form, and then deriving an augmented form; designing a reduced-order fault observer based on the intermediate variable for the reduced-order augmented system to estimate the state of the follower UAV, process fault and system uncertainty; using the relative state information to design a tracking fault-tolerant formation controller, taking the estimation of process fault and system uncertainty as compensation term; designing adaptive parameters for the reduced-order fault observer and the tracking fault-tolerant formation controller; verifying the performance of the reduced-order fault observer and the tracking fault-tolerant formation controller; the application can more prominently deal with the nonlinear and uncertain control problem, while reducing the calculation amount and improving the efficiency of distributed decision-making.

This invention relates to a full-state control method for vector hexacopter aircraft based on a high-order dynamics model, belonging to the field of vector hexacopter tracking control. The invention addresses the problems of poor accuracy and low precision in angular acceleration estimation found in existing vector hexacopter control methods based on jerk dynamics models. This invention designs an angular acceleration observer based on a high-order dynamics model and full-drive system theory, enabling effective estimation of angular acceleration signals without relying on external sensors. This allows for high-precision, high-speed tracking of the position and attitude of a six-DOF vector hexacopter aircraft. This invention is primarily applied to attitude tracking of six-DOF vector hexacopter aircraft.

The application provides a low-speed position sensorless control method based on an observer considering convergence effect q Firstly, a permanent magnet motor current model is used to construct q a shaft current sliding surface; then, a sliding mode observer based on the shaft current model of the sliding surface is designed to accurately estimate the rotor speed and integrate the rotor position. Affected by the convergence effect, when the observer is used for low-speed operation, large load mutation can cause motor stalling, and then cause the loss of signals for the observer, and the output speed and position disorder. Therefore, a table compensation strategy based on deep symbolic regression and data filling is provided, large load mutation data is generated based on small load mutation data, so that the position error can be compensated in real time, and the stalling problem caused by the convergence effect is eliminated. The application not only realizes noiseless low-speed position sensorless control, but also solves the stalling problem caused by the convergence effect under the condition of low-speed large load mutation.

This invention discloses a design method for a cooperative target pose observer based on a novel potential function, used for real-time position and attitude estimation of cooperative targets in space. Common pose estimation methods typically include static and dynamic methods. Dynamic methods combine observation information with the system's kinematic or dynamic information, and under the same conditions, usually yield better estimation results than static methods. As a dynamic estimation method, the observer is valued for its simple structure and ease of application. Addressing the problem of the limited variety of potential functions commonly used in current pose estimation observers, this invention, based on existing potential functions, proposes a method for constructing an observer based on a novel potential function. This method can be used for synchronous relative pose estimation during operations by a space robotic arm on a cooperative target. The observer derived from the novel potential function designed in this invention not only possesses the same theoretical stability characteristics as existing observers but also demonstrates better estimation performance in practical applications.

This application discloses a time-varying formation tracking method and system for multi-agent systems based on the internal model principle, relating to the field of disturbance suppression. The method includes: initializing all relevant parameters; designing two observers based on the relevant parameters to estimate the state and input of the unknown leader, ensuring observation is completed within a preset convergence time; designing an internal model dynamic compensator; determining the time-varying formation tracking error based on the state estimation result of the unknown leader; determining the formation tracking compensation term based on the multi-agent system parameters; determining the adaptive estimation update law based on the time-varying formation tracking error; and determining the final control law based on the input estimation result of the unknown leader, the internal model dynamic compensator, the time-varying formation tracking error, the formation tracking compensation term, and the adaptive estimation update law. This application, by designing a distributed preset-time observer and an internal model-based controller, can realize time-varying formation tracking in multi-agent systems with unknown disturbances and non-autonomous leaders.

The application discloses an unmanned ship safety tracking control method for data forgery attack, and belongs to the technical field of intelligent control. A kinematics and dynamics model of an unmanned ship and a neural network approximation function are constructed; an observer based on an adaptive neural network is constructed, and then an observation error is obtained; a virtual kinematics control law is designed, a position tracking error model is constructed; a speed error dynamic model is constructed; a safety control compensation mechanism for compensating a control input is designed, and then a safety control compensation signal is obtained; a false control signal observer is designed to obtain a false control signal observation error; a dynamic tracking control law is constructed; the dynamic tracking control law is substituted into the speed error dynamic model, and then a speed vector error is obtained; a control input is generated according to the obtained speed vector error; and the control input is transmitted to an actuator of the unmanned ship through a communication network for execution. The application has stronger attack resistance, and the system has strong adaptability and control stability.

The invention relates to a motor rotor position estimation method and system based on distillation learning and multi-observer fusion, and aims to solve the problems of low rotor position estimation precision in a low-speed area and estimation angle jump during algorithm switching. In the method, different position estimation methods are adopted for the rotating speed of the motor. A neural network observer is adopted in a low-speed section; a counter electromotive force observer based on neural network compensation is adopted in the medium-speed section; and an observer based on back electromotive force is used in the high-speed section. The neural network observer can improve the position estimation precision through distillation learning training. The fusion weight generation network outputs a dynamic fusion weight in real time, and performs weighted fusion on the outputs of two adjacent algorithms in an algorithm switching interval to ensure that the angle has no jump at the switching moment. According to the method, smooth switching of low, medium and high speed domains is realized, the characteristics of different algorithms are considered, and + / -4-degree prediction precision is realized at 3.3% rated rotating speed.

The application discloses a permanent magnet synchronous motor speed control method and system based on a novel sliding mode observer, which is applied to the technical field of permanent magnet synchronous motor control and comprises the following steps: a disturbed kinematic equation of a permanent magnet synchronous motor is established; a sliding mode observer based on nonlinear resonance control is established according to the disturbed kinematic equation of the permanent magnet synchronous motor; an error feedback control law is established based on a mechanical angular velocity error of the permanent magnet synchronous motor; and a reference current is obtained by compensating the error feedback control law to an observation value of a total disturbance of the sliding mode observer.

This invention discloses an effective sliding mode control method for suppressing overshoot in the position response of a series elastic actuator. The method includes: designing a sliding mode controller and a lumped disturbance decoupling observer based on a dynamic model of the series elastic actuator system considering lumped disturbances. The sliding mode controller includes a Gaussian dynamic gain sliding surface and an anti-integral saturation superspiral reaching law; inputting the state feedback vector of the series elastic actuator system into the lumped disturbance decoupling observer, processing it, and outputting the lumped disturbance torque of the motor side and load side of the series elastic actuator system, then inputting it together with the preset position command of the load side into the sliding mode controller, processing it, and outputting the q-axis current reference value, thereby performing closed-loop control of the load side position of the series elastic actuator system. This invention, in point-to-point position control of the load side position of a series elastic actuator, can effectively reduce the overshoot of the load side position response while maintaining the system's high dynamic response capability.

This invention discloses a spherical joint variable impedance control method based on force field perception and biomimetic strategy learning, belonging to the field of robot control technology. The method includes: establishing a spherical joint dynamic model; constructing an adaptive control module based on Lyapunov stability in a Jacobi-free inverse Cartesian space; designing a perturbation observer based on angular momentum conservation and a recursive least squares force field parameter identification module with an adaptive forgetting factor; designing a retention rate regulator based on the difference between estimated and adaptive impedance to achieve intelligent impedance modulation; designing a biomimetic strategy learning mechanism to use historically optimal parameters for repetitive task initialization; and conducting simulation verification. This invention improves computational efficiency and singularity robustness through inverse-matrix Cartesian space impedance adaptation, solves overfitting problems through intelligent retention rate regulation, and achieves human-like learning-saving characteristics through an explicit memory mechanism, significantly enhancing the robot's adaptability to dynamic and unknown environments.

This application provides a disturbance estimation method, apparatus, device, and medium based on an observer, applicable to the field of automatic control technology, and can solve the technical problem of low disturbance estimation accuracy caused by observer switching impact. The method includes: determining the observation index values ​​corresponding to the higher-order and lower-order observers based on the corresponding state vectors, and determining whether a target observation index value exists among the observation index values; when the determination result is that no target observation index value exists, calculating the high-frequency energy ratio based on the energy values ​​corresponding to the target frequency band and the high-frequency band respectively; substituting the high-frequency energy ratio into the target weight model to obtain the corresponding dynamic lower-order weights and dynamic higher-order weights; when the determination result is that a target observation index value exists, determining the preset lower-order weights and preset higher-order weights; and weighting and summing the disturbance estimation values ​​according to the lower-order weights and higher-order weights to obtain the target disturbance estimation value; thus, the accuracy of disturbance estimation is improved.

The invention provides an observer-based low-speed position sensorless control method considering a convergence effect, and the method comprises the steps: firstly constructing a q-axis current sliding mode surface based on a permanent magnet motor current model; and then, designing a sliding-mode observer based on a q-axis current model according to the sliding-mode surface so as to accurately estimate the rotating speed of the rotor, and integrating the rotating speed of the rotor to obtain the position of the rotor. Under the influence of a convergence effect, when the observer is used for low-speed operation, heavy load sudden change can cause motor stalling, and then signal loss and output rotating speed and position disorder of the observer are caused, so that a look-up table compensation strategy based on depth symbol regression and data filling is provided; on the basis of the small load sudden change data, the large load sudden change data is generated so as to realize the real-time compensation of the position error, thereby eliminating the locked-rotor problem caused by the convergence effect. Noiseless low-speed sensorless control is achieved, and the problem of locked-rotor caused by the convergence effect under low-speed large-load sudden change is solved.

The invention belongs to the technical field of permanent magnet synchronous motor control, discloses a permanent magnet synchronous motor sensorless control method and a permanent magnet synchronous motor sensorless control system, and aims to solve the problem that the motor speed estimation precision of the system is reduced due to serious buffeting of a traditional sliding mode observer in permanent magnet synchronous motor sensorless control. A fractional order-based high-order sliding mode observer is designed, and by introducing a fractional order theory and a high-order sliding mode control method, buffeting of a sliding mode is effectively suppressed, and the estimation precision of the observer is improved. According to the method, a phase-locked loop is designed based on a fractional order expansion state observer and is used for extracting the rotating speed and position information of the motor, so that the estimation precision of the system on the position and the speed of the rotor is further improved. By means of the method, high-precision estimation of the rotor position and the rotor speed can be achieved in the sensorless control process of the permanent magnet synchronous motor system, and it can be guaranteed that the system has high robustness when load disturbance is not matched with parameters.

The present application relates to the technical field of hydropower generation system control, and discloses a fault diagnosis and fault-tolerant control method and system for a speed sensor, wherein the method constructs an MRAS rotating speed observer based on a particle swarm algorithm optimization, and outputs an estimated rotating speed; an actual rotating speed measured by a physical speed sensor is acquired, and a residual of the two is calculated; an adaptive fault judgment threshold is constructed, which is obtained by dynamically weighting a historical residual mean value, a historical residual maximum value and a q-axis current change rate; the residual is compared with the adaptive threshold, if the residual is less than the threshold, it is determined that the sensor is normal, and a physical feedback signal is adopted; if the residual is greater than or equal to the threshold, it is determined that the sensor may have a fault, and the number of times that the residual is greater than or equal to the threshold is counted, if the number of times reaches a certain value, a disturbance-free switch is made to a sensorless mode, and the estimated rotating speed is adopted as a feedback signal. The present application can effectively distinguish a dynamic disturbance from a sensor essential fault, avoid misjudgment and omission, and realize smooth switching between a physical sensor and a sensorless mode.

The invention discloses an alternating current servo system control method for controlling the stability of an obstacle breaking vehicle body, and the method comprises the steps: constructing an unscented Kalman filter (UKF) load observer based on the UKF, and estimating a load torque value and the rotor angular speed of a permanent magnet synchronous motor in real time through the UKF load observer; feeding back a rotor angular velocity output based on a UKF load observer to an extended state observer (ESO), introducing a load torque value as a feedforward compensation amount into an NLSEF control law, introducing a Newton-Raphson optimization algorithm (NRBO), and performing global optimization on parameters of an active disturbance rejection controller (ADRC) by taking an improved ITAE index as a fitness function; compared with the prior art, the method can improve the tracking precision and the anti-interference performance of the barrier-breaking vehicle under the complex dynamic working condition, and guarantees the stability of the barrier-breaking vehicle body in the advancing process.

The invention relates to an optimal consistency control method of a nonlinear multi-agent system based on reinforcement learning. The method comprises the following steps: establishing a discrete time multi-agent system model containing unknown nonlinear terms; iteratively updating the upper and lower bounds of the state through a reduced-order interval observer based on the binary communication signal, and generating a state interval estimation result; an unknown nonlinear dynamic state is approximated online by using a radial basis function neural network; the stability of a weight error system is analyzed through a deterministic learning theory, and weight estimation convergence is ensured; learning an optimal consistency control strategy by adopting a direct heuristic dynamic programming framework based on state estimation and an unknown dynamic estimation value; and finally generating a distributed consistency control signal. By adopting the method, unknown nonlinear dynamics can be effectively processed under the binary communication constraint, and stable learning and optimal consistency control of a multi-agent system are realized.

The invention relates to the technical field of model predictive control, in particular to a composite control method and system for a permanent magnet synchronous motor, and the method comprises the steps: building a state space model with load torque as an extended state variable, building an optimization problem based on a discrete state space model and a preset constraint condition, employing a multi-parameter quadratic programming problem, and carrying out the offline solving. And generating a partition lookup table of the state space and a corresponding segmented affine control law. During on-line operation, the load torque estimated in real time by a load observer based on a recursive least square method and the current rotating speed form a state vector together, and an optimal q-axis current reference instruction can be obtained through table look-up and one-time linear calculation. According to the method, complex online optimization is converted into efficient offline calculation and online table look-up, the real-time calculation complexity is remarkably reduced on the premise of ensuring global optimality and constraint satisfaction, and meanwhile, through disturbance observation and compensation, the load disturbance resistance robustness is effectively improved.

This invention belongs to the field of robot motion control technology and discloses a global finite-time motion control method for robots based on an adaptive composite observer. The method includes: Step 1: Designing an adaptive composite observer based on finite-time sliding mode manifolds to comprehensively compensate for uncertainties in robot kinematics and dynamics; Step 2: Then, designing a non-singular end-effector sliding mode motion controller based on an adaptive neural network and a preset performance error affine design to directly stabilize motion errors in the task space; Step 3: Finally, achieving global finite-time stability of the entire observer-control system using the Lyapunov design method, ensuring that observer errors and motion control errors converge rapidly to zero within a finite time, while satisfying the system's preset transient and steady-state response characteristics. This invention can rapidly converge robot end-effector motion errors to zero within a finite time, effectively improving robot efficiency and execution accuracy.

The invention relates to an angle module induced effect compensation system and method based on physical-data fusion, and the system comprises a whole vehicle motion planning module which obtains a whole vehicle expected attitude and a rotation angle through calculation; the transfer effect feedforward compensation module is used for constructing a physical embedded feedforward model and estimating transfer disturbance torque in real time; the data driving prediction module is used for constructing a dual-scale space-time fusion network model, extracting long-time-sequence inertial features and short-time-sequence sudden change features of vehicle dynamics, and predicting a vehicle prediction state in a rolling manner; the multi-constraint optimization control module is used for solving the optimal residual compensation torque; the feed-forward parameter online self-adaptive calibration module is used for designing a disturbance observer based on inverse dynamics, extracting real physical disturbance in real time, and correcting feed-forward model parameters online by using a recursive least square algorithm; and an actuator actuation module. Non-linear interference of a turning effect is overcome through a feed-forward-optimization-self-adaptive closed loop mechanism, and high-precision tracking of a vehicle turning angle and coordinated stable control of a whole vehicle yaw attitude are achieved.