48results about How to "Reduce tracking error" patented technology

The invention discloses a robot positioning navigation system and method based on multi-modal information fusion, and belongs to the technical field of multi-source information fusion navigation. The invention discloses a robot positioning and navigation system based on multi-modal information fusion. The robot positioning and navigation system comprises a visual sensing module, an inertial measurement unit (IMU), a Beidou navigation receiver, a control processor and an electric control driver. The problems that in the prior art, the robot guiding mode is single, the positioning navigation precision and reliability are low, and the robot is difficult to adapt to the complex environment are solved; according to the invention, by fusing the positioning information of different guiding modes, such as vision, IMU and Beidou navigation information, the positioning precision and reliability of the robot in a complex environment are improved, and the robot can better adapt to low-texture scenes such as illumination change, large-angle curve motion and rapid motion.

The invention discloses an energy regulation and control method of a photovoltaic system, and relates to the field of energy regulation and control of the photovoltaic system, and the method comprises the steps: collecting comprehensive state data in a time period T, processing the comprehensive state data, and obtaining time sequence state data; performing primary processing on the time sequence state data by using moving average filtering to obtain a smoothed initial data set, taking the initial data set as observation input of Kalman filtering, further smoothing the data by using the Kalman filtering to obtain a high-precision gain data set, and using improved Pamp; performing disturbance judgment on the gain data set through an O algorithm; according to the method, power oscillation of voltage in a prediction interval is eliminated by using an IncCond method, subdivision demand judgment is performed, a maximum power point is accurately positioned by using a subdivision search method, and the maximum power point is output as a working point of the system, so that energy regulation and control of the photovoltaic system at the maximum power point are realized, and the energy efficiency of the photovoltaic system is improved.

The invention belongs to the technical field of force / position hybrid control of mechanical arm systems, and particularly discloses a finite-time self-adaptive impedance control method and system for a flexible joint mechanical arm system. In order to solve the problems that the control effect of a mechanical arm system is affected by nonlinear and unknown system model parameters at present and derivation is complex in backstepping design, the fuzzy self-adaptive finite time impedance controller is designed based on the impedance control technology so as to achieve hybrid control over the track and force of the tail end of the mechanical arm. In the design process of the controller, a real control law is constructed to realize finite time stability of the system; and meanwhile, command filtering and fuzzy self-adaptive technologies are introduced to solve the problems of calculation explosion in backstepping design and unmodeled dynamics of a mechanical arm system. By means of the control method, the influence caused by system uncertainty can be effectively overcome, the position tracking error of the mechanical arm system is reduced, and meanwhile it can be guaranteed that all signals in a closed-loop system are bounded within finite time.

The invention discloses a steer-by-wire system self-adaptive steering angle tracking strategy based on a high-order all-drive theory, which comprises the following steps of: 1, establishing a kinetic model of a steer-by-wire system according to the steer-by-wire system of a passenger vehicle, and determining a state variable and a control variable; 2, designing an extended disturbance observer according to the kinetic model established in the step 1, and observing the internal and external uncertainty and disturbance of the system, and compensating the uncertainty and disturbance to a controller; 3, converting the steering-by-wire system dynamics model in the step 1 into an all-wheel-drive system model, and designing a self-adaptive high-order all-wheel-drive controller; and 4, verifying the stability of the high-order all-wheel-drive controller designed in the step 3 according to a Lyapunov stability method. According to the method, uncertainty interferences inside and outside the system are considered in the built steer-by-wire system model, the interferences are lumped, the extended disturbance observer is used for observation and compensation to the controller, and the accuracy and robustness of the control system are improved.

A cracking furnace outlet temperature multi-step compensation predictive control method with a time-lag characteristic belongs to the field of advanced control of an industrial process and comprises the following steps: step 1, modeling a cracking furnace outlet temperature control system, and establishing a state space model combining a linear model and an unmodeled dynamic state; 2, establishing an expanded incremental model; 3, designing a robust time-delay prediction controller according to the time-varying and time-delay characteristics of the system; and 4, designing an unmodeled dynamic multi-step compensation controller. A brand new scheme is provided for designing a controller of a cracking furnace outlet temperature control system with uncertainty, strong nonlinearity and time-varying and time-lag, and system tracking errors are eliminated as far as possible by combining data driving and a multi-step compensation principle, so that the system can better track a set value, and meanwhile, the long-term operation capability of the system is improved.

According to the multi-target tracking method and device, the storage medium and the computer equipment provided by the invention, in the to-be-tracked video of the non-first frame, the to-be-tracked target and the historical detection frame thereof are determined from the previous frame, the target position of the current frame is further predicted, and the target position of the current frame is determined according to the confidence of each detection target in the current frame. The detection targets with the confidence coefficients lower than a threshold value serve as candidate detection targets, and the detection targets with the confidence coefficients not lower than the threshold value serve as main detection targets; on the basis of the historical detection frame and the current prediction frame of each to-be-tracked target and the current detection frame of each main detection target, performing multiple times of matching on each current prediction frame and the current detection frame of each main detection target; and matching the current prediction frame which is not matched with the current detection frame of each candidate detection target, and taking each main detection target and the successfully matched candidate detection target as a to-be-tracked target of the current frame. Therefore, the tracking error can be reduced to the greatest extent under the conditions of shielding and detection loss.

The invention belongs to the technical field of multi-vector model predictive control, and provides a calculation-simplified multi-vector model predictive control method and system. The calculation-simplified multi-vector model prediction control method comprises the following steps: carrying out region division on a three-phase space vector diagram to form a plurality of groups of region sets, and enabling each group of region sets to be in one-to-one correspondence with one space vector; searching a plurality of neighbor space vectors for synthesizing the reference space vector according to the region set where the reference space vector is located; on the basis of a converter model, a reference space vector and all searched neighbor space vectors, all on-off state combinations of the converter are calculated on line with the purpose of minimizing the current tracking error of the converter, and no vector coordinate or state combination or other value tables need to be stored in advance; and based on all the calculated switch state combinations of the converter and the voltage control value function, selecting the switch state combination with the minimum capacitance voltage deviation to control the converter so as to realize multi-objective optimization control of the flying voltage and the midpoint voltage.

The invention belongs to the field of power electronic control, and relates to a parallel pulse power decoupling system control device and method, and the device comprises a second-order low-pass filter, a sampling module, an outer-loop nonlinear ADRC controller, an inner-loop nonlinear ADRC controller, a current feedforward module, a comparator, and a dual-inductor bidirectional Buck / Boost converter. All the devices are connected in sequence to form the parallel pulse power decoupling system control device; according to the invention, the dual-inductor bidirectional Buck / Boost converter is adopted as a pulse power compensation unit and is connected in parallel with the output port of the pre-stage power supply system, so that the transient process of inductor current commutation can be eliminated, and the bus current peak generated during pulse load state switching can be effectively reduced.

The invention provides an unmanned underwater vehicle preset time trajectory tracking control system with an obstacle avoidance function so as to improve trajectory tracking precision in an obstacle environment. The method comprises the following steps: establishing an unmanned underwater vehicle model; designing a preset time function; a novel artificial potential field function is designed, so that the obstacle avoidance response speed is increased, and tracking errors are reduced; a preset time obstacle performance function is designed to serve as an error limiting boundary for trajectory tracking under the obstacle condition, and the function can dynamically adjust the error limiting boundary according to error changes; designing a preset time extended state observer for estimating model uncertainty and external interference; an auxiliary system with a smooth switching function is adopted to process an input saturation problem; and finally deducing a trajectory tracking control scheme. A simulation result shows that the absolute error integral of the unmanned underwater vehicle in the advancing direction, the yawing direction and the course angle within 100 seconds is reduced by 56.37%, 43.21% and 7.20%; estimation absolute error integral of longitudinal, swaying and yaw angular velocity is reduced by 25.44%, 8.86% and 35.24%.

The invention discloses a waveform design method and system for tracking a sea surface maneuvering target by an unmanned ship-mounted radar, relates to the technical field of radar communication, and aims to solve the technical problem of inaccurate maneuvering target tracking in a sea surface clutter environment. The method is technically characterized by comprising the following steps: predicting a target state by using a marginalized particle filter algorithm, and obtaining a predicted measurement value; generating a hypothesis tree according to the observation data; performing target state estimation on each assumed branch by using a marginalized particle filter algorithm; a multi-hypothesis tracking algorithm is used to calculate an association probability and update a hypothesis weight, hypothesis pruning and fusion are carried out, and final target state estimation is obtained; a plurality of waveform selection criteria are used as a model, and an interactive multi-criterion selection method is utilized to determine an optimal criterion with the highest effective probability and an optimal transmitted waveform parameter. According to the method, false alarms caused by sea clutters can be effectively handled, the performance of the unmanned ship-mounted radar for processing complex tracking scenes can be improved, and a more accurate and robust target tracking result can be obtained.

The invention discloses an adaptive planner parameter tuning method and device based on reinforcement learning, and the method comprises the steps: employing a reinforcement learning algorithm to adaptively carry out the parameter adjustment of a local planner according to the input environment data in a parameter adjustment network under a low-frequency control frequency; under the medium-frequency control frequency, generating a planning track of robot advancing according to the adjusted parameters in the local planner, and generating a feed-forward speed according to the planning track; under the high-frequency control frequency, a final control instruction is generated in the controller according to the feed-forward speed and the feedback speed calculated by the error compensator; and iteratively and alternately training and testing the parameter adjustment network and the controller, and continuously adjusting and optimizing the parameters of the local planner and a final control instruction generated by the controller according to a test result. According to the invention, the robustness and navigation efficiency of the system can be improved, the tracking error is effectively reduced, the obstacle avoidance capability is enhanced, and a more accurate, efficient and reliable robot navigation task can be completed in a complex environment.

The invention discloses a crisscross optimization robust model predictive control method for an automatic sweeping robot. The crisscross optimization robust model predictive control method is adaptive to a trajectory tracking system with parameter uncertainty. According to the method, a discrete time controlled system with parameter uncertainty, a reference trajectory system and a tracking error system are constructed, performance evaluation indexes are defined, the stable control gain range of a basic RMPC controller is determined by solving a minimum and maximum optimization problem and linear matrix inequality constraints, and a multi-objective optimization function is combined in the range to achieve the stable control of the RMPC controller. The optimal additional control gain is iteratively searched by means of a crisscross algorithm, an optimized controller is formed, the stability and the dynamic performance are considered, the influence caused by parameter uncertainty can be overcome, the rising time and the adjusting time of trajectory tracking are remarkably shortened, the tracking error and the energy consumption are reduced, and the tracking efficiency is improved. And the dual requirements of the automatic sweeping robot on real-time response and control precision are met.

The invention discloses an intelligent agriculture integrated control method and system based on edge calculation, and belongs to the field of mechanical automation control, and the method comprises the steps: building a dynamic twin body, quantifying a response deficit between an expected vehicle body attitude angle change rate and an actual attitude angle change rate under a driving instruction, and defining the response deficit as an observation signal; a control target is redefined based on the observation signal, the control target is set to track a stable manifold, the stable manifold is defined by a preset observation signal amplitude boundary, and the state of the target agricultural machine is controlled to be maintained in the stable manifold; monitoring the steering jerk of the target agricultural machine; according to the method, dynamic twinborn quantitative response deficit characters are constructed, observation signals are defined, absolute trajectory accurate tracking is replaced with stable manifold as a control target, physical disturbance in a dynamic farmland environment is effectively compensated, agricultural machine path tracking errors are reduced, and the operation problems of seedling pressing, seedling damage and the like are reduced.

A computerized method for estimating joint friction in a robotic wrist joint of an end effector is disclosed. A sensor measurement of force or torque is generated in a transmission device that mechanically couples the robotic wrist to an actuator. The joint friction in the robotic wrist joint driven by the actuator is calculated by applying the sensor measurement of force or torque to a closed form mathematical expression that relates a transmission force or transmission torque variable to a joint friction variable. A tracking error of the end effector is also calculated using a closed form mathematical expression that relates the joint friction variable to the tracking error. Other aspects are also described and claimed.

This invention discloses a LiDAR point cloud data completion system and method based on a PCN model, belonging to the field of point cloud data processing technology. It is used to complete LiDAR point cloud data with complex occlusion. The system includes a data loading module, a data processing module, a data completion module, a model training and optimization module, and a data visualization module. The method includes acquiring raw point cloud data files, reading and preprocessing multi-format point cloud files, constructing a dataset, building a point cloud data completion model based on a PCN model, training and validating the model based on the dataset, acquiring a weight file, and performing data completion on point cloud data with complex occlusion based on the weight parameters. This invention solves problems such as poor data format compatibility of existing PCN models, numerical overflow or training instability when the point cloud data coordinate range is large, and low robustness of point cloud file matching and loading, thereby improving the accuracy of sparse point cloud data completion.

The application discloses a master-slave end hybrid mapping method for a human-computer interaction system and application thereof, and belongs to the technical field of human-computer interaction, and comprises the following steps: a slave end executes a corresponding mapping method according to a mode selected by a master end in different stages and specific task requirements; wherein the mode of the human-computer interaction system comprises a first mode and a second mode; the corresponding mapping method in the first mode is applied to a large-range fast rough positioning operation scene, and the corresponding mapping method in the second mode is applied to a small-range slow fine operation scene; through the hybrid mapping method, the consistency of end postures of master-slave end devices is maintained while the reachable range of a working space of the human-computer interaction system is improved; after mode switching, fine operation under operation space mapping can be realized, and continuous and smooth switching of positions and postures can be realized, so that continuous and smooth switching between different operation modes is realized, and the tracking error between the master end and the slave end is greatly reduced.

This invention relates to the field of fast tool servo control technology, and in particular to a dual-stroke fast tool servo control system. It comprises a two-stage displacement control system with two identical displacement controllers. Each displacement controller consists of a parallel PI control module and an error compensation module. The PI control module generates a primary adjustment control signal based on the tracking error signal; the error compensation module includes an inverse function module and a time delay module. The former restores the error displacement drive signal, and the latter acquires historical displacement drive signals. The two-stage controllers control their respective displacement components, gradually reducing the tracking error. Initially, the PI control module uses a proportional term for rapid correction to ensure initial tracking accuracy of the high-frequency signal; the error compensation module directly compensates for the current error without delay, gradually learning to approximate the desired signal as the error converges. The two modules work together to avoid the delay problem of the integral term, ensuring continuous high-precision tracking of high-frequency periodic signals and improving machining accuracy.

The invention discloses an active suspension neural network adaptive control method and system, and the method comprises the steps: constructing a multi-layer bionic reference model of an active suspension, and carrying out the calculation based on the bionic reference model, and obtaining an expected path of the stroke of the active suspension; tracking errors of the suspension stroke are introduced, system uncertainty factors are approximated through a radial basis function neural network, an active suspension controller is designed, and the controller is used for controlling the stroke of the active suspension to accurately track the stroke expected track output by the bionic reference model. The control method does not depend on any linearization operation, an original nonlinear model is not limited, a nonlinear closed-loop system is converted into an all-wheel-drive system, and asymptotic convergence of the system state is guaranteed.

The invention relates to the technical field of robot control, in particular to a double-arm collaborative robot fuzzy adaptive sliding mode control method, which comprises the following steps: S1, establishing a dynamic model of a double-arm collaborative robot; s2, fusing a Taylor series expansion method on the basis of the dynamic model of the double-arm collaborative robot, and establishing an improved dynamic mathematical model suitable for a time delay condition; s3, using a fuzzy algorithm and an average residence time mechanism to develop a fuzzy adaptive sliding mode tracking controller; and S4, designing a neural network tracking control scheme based on fuzzy recursion to eliminate a time delay error existing in the model. According to the control method provided by the invention, the negative influence of the time delay on the system stability can be effectively overcome while the interference influence is eliminated, and the application range of the theoretical delay value is obtained on the basis of improving the control quality of the double-arm system.

The invention relates to the technical field of robots, and particularly provides a snake-shaped robot control method and device, a medium and a program product, and the method comprises the steps: building a tracking error kinetic model of a snake-shaped robot based on a kinetic model and a tracking error of the snake-shaped robot; introducing a nonlinear function and fractional order differential, and constructing a nonlinear fractional order sliding mode surface; designing a superhelix approaching rate based on the nonlinear fractional order sliding mode surface; and according to the tracking error dynamic model, the nonlinear fractional order sliding mode surface and the super-spiral approaching rate, a super-spiral nonlinear fractional order sliding mode controller of the snake-shaped robot is constructed to control the snake-shaped robot. According to the method, through a nonlinear fractional order sliding mode surface control strategy, an error saturation suppression and amplification mechanism is ingeniously utilized, tracking errors are effectively reduced, the stability of the system is enhanced, and it is ensured that the snakelike robot can be rapidly and stably converged to the sliding mode surface through the super-spiral approaching rate.

The application relates to a smart mobile platform trajectory optimization and automatic mapping method, a system and a storage medium, wherein the smart mobile platform trajectory optimization method comprises the following steps: acquiring a first pose corresponding to a last frame radar timestamp, and combining measurement data collected by a sensor to obtain a second pose of the smart mobile platform corresponding to a current frame radar timestamp; the sensor is arranged on the smart mobile platform, and the smart mobile platform moves in a current environment; based on the measurement data and the second pose, a retroreflector column coordinate in a world coordinate system is extracted; after confirming that the trajectory of the smart mobile platform covers the current environment, the poses corresponding to all radar timestamps and the retroreflector column coordinates in the world coordinate system are jointly optimized based on a joint optimization model to obtain an optimized trajectory. Through the application, the trajectory error is reduced through a joint optimization method, so that the effect of reducing the map construction error is achieved.

The invention discloses a predetermined time adaptive control method for a wing-riding separation variant gliding aircraft, and the method specifically comprises the following steps: firstly, constructing a time scale function, and effectively avoiding the problems of unlimited increase of control quantity and singularity; secondly, designing a preset time sliding mode surface based on the time scale function; thirdly, providing a neural network parameter identification strategy, and converting a system identification problem into a parameter estimation problem so as to adapt to a flight scene with uncertain aerodynamic parameters; and finally, designing a preset time self-adaptive sliding mode controller, and realizing convergence in specified time under the condition of ensuring that the system state is not dependent on an initial condition. The adaptive gain of the controller can be dynamically adjusted according to the system environment, jitter can be inhibited, tracking errors can be reduced, and the influence caused by centralized uncertainty can be weakened; in conclusion, the control method not only realizes accurate attitude tracking, but also achieves preset time convergence characteristics irrelevant to initial conditions and control parameters, and has excellent quick responsiveness, stability and robustness.

This application discloses a control method, device, dexterous hand, makeup robot, storage medium, and program product for a makeup robot, belonging to the technical fields of makeup robots and dexterous hands. The makeup robot includes an execution component, which comprises a robotic arm and a dexterous hand disposed at the end of the robotic arm. The execution component is used to hold matching makeup tools. The method includes: acquiring target compliant interaction parameters; obtaining visual displacement compensation based on the user's facial image information; obtaining force-controlled displacement compensation based on the target compliant interaction parameters and the actual force between the matching makeup tools and the user's face; correcting preset trajectory parameters based on visual displacement compensation and force-controlled displacement compensation; and controlling the movement of the execution component based on the corrected preset trajectory parameters. The control method of the makeup robot in this application can adapt to changes in spatial position caused by slight movements of the user during the makeup process, improving the makeup quality and user experience of automated makeup operations.

This invention relates to an active disturbance rejection control method for the surface potential of an isolated conductor based on an RBF neural network and the INFO optimization algorithm, belonging to the field of charge control technology for space inertial sensors. The invention addresses the problems of insufficient accuracy in estimating the fixed gain of the extended state observer and the difficulty in tuning the parameters of the nonlinear feedback control law in existing active disturbance rejection control methods. The method includes: establishing a dynamic model of the isolated conductor charge management system; constructing a dual-error-constrained extended state observer; adaptively adjusting the observer gain using an RBF neural network to observe the potential and total disturbance; constructing a differential tracker to obtain the expected potential and its derivative; designing a nonlinear state error feedback control law; using the INFO algorithm for global optimization to obtain the optimal control parameters; and constructing a composite controller to output feedback control quantities. This invention achieves adaptive adjustment of the observer gain and automatic optimization of the controller parameters, making it suitable for space gravitational wave detection.

The invention discloses a self-adaptive vehicle trajectory tracking control method and system suitable for a curved surface terrain. The method comprises the following steps: S1, obtaining a point cloud map and a reference trajectory of a current environment; s2, selecting key points on the reference trajectory, and fitting a local ground equation around the key points; and S3, establishing a manifold model prediction controller by taking the position, the speed and the yaw angle of the vehicle as state variables and taking the longitudinal acceleration and the front wheel rotation angle of the vehicle as control variables. And S4, adjusting the target function weight of the manifold model prediction controller in combination with the curvature of the current position of the reference trajectory. And S5, in combination with the current speed of the vehicle and the transverse error between the vehicle and the reference trajectory, the PID controller is used for compensating the acceleration output by the controller and the front wheel turning angle. Compared with an existing trajectory tracking method, the method provided by the invention has the advantage that the trajectory tracking precision and control robustness of the vehicle on the complex curved surface terrain can be remarkably improved.

The invention discloses a position control method of a hydraulic servo system, and relates to the technical field of hydraulic servo systems. The method comprises the steps of obtaining an application range of the hydraulic servo system and marking the application range as an initial range; based on the initial range, determining a moving position range of the hydraulic servo system in the initial range and marking the moving position range as an initial search range; based on the initial search range, a final search range is obtained through calculation according to a final range correlation function, and the optimal movement position of the hydraulic servo is determined in the final search range and marked as an execution position; re-determining an execution position according to an execution correlation function based on the final search range; and controlling the hydraulic servo system to move to the execution position by utilizing the super-spiral sliding mode controller based on the execution position. The position control performance of the hydraulic servo system is improved.

Disclosed herein is a high throughput optical scanning device and methods of use. The optical scanning device and methods of use provided herein can allow high throughput scanning of a continuously moving object with a high resolution despite fluctuations in stage velocity. This can aid in high throughput scanning of a substrate, such as a biological chip comprising fluorophores. Also provided herein are improved optical relay systems and scanning optics.

The invention discloses a dynamic delay compensation control method for an electro-hydrostatic actuator, and the method achieves the effective compensation of a dynamic lag characteristic through constructing a set of control flow which comprises expected acceleration generation, delay disturbance estimation, disturbance feedback correction, target pressure derivation and control voltage calculation. The method is suitable for an electromechanical hydraulic execution system with a delay characteristic, and the response speed and robustness are improved while the control precision is guaranteed; according to the invention, the tracking performance and stability of the actuator under a high-dynamic working condition are improved, and the method is especially suitable for a mechanical-electrical-hydraulic integrated system with high requirements on response speed and control precision.

The stop / start detection block (63) determines whether the joint is in a stopped state before the rotation direction of the joint is reversed based on the stop flag signal (Stop_Flg). When the filter processing block (65) determines that the joint is in a stopped state, the frequency component of the correction amount for correcting the gap is changed to a low frequency lower than a given threshold.

The invention belongs to the technical field of numerical control, and discloses a device and method for inhibiting uncertainty disturbance and improving the conveying performance of an euphausia superba conveying suction pump. The lumped disturbance of the system is estimated by using a disturbance observer, and the constraint of the disturbance upper bound on the conservative property of the control design is avoided; then, a novel sliding mode device is adopted to suppress system lumped disturbance, and the robustness of the system is improved; the novel sliding mode controller adds an exponential function item and a power function item in the reaching law to enable the system state to converge rapidly. A sigmoid function is used for replacing a switching function, continuous control input is achieved, high-frequency buffeting is weakened, and stability and continuity of control signals are guaranteed. Therefore, the positioning precision is improved, a stable control signal can be generated, the control performance of the system is obviously improved, the tracking error is reduced, high-frequency oscillation is avoided, and the control precision of the euphausia superba conveying suction pump servo system is further improved.