1054 results about "Stabilization control" patented technology

The invention provides a humanoid robot action coordination method and system based on joint torque, and the method comprises the steps: obtaining the original data of the joint torque of a humanoid robot through a sensor, carrying out the preprocessing of the original data of the torque, outputting stable torque data, constructing an impedance control model through the combination of the angle data of the mechanical arm joint of the humanoid robot, and carrying out the control of the mechanical arm joint. Predicting a moment change trend to obtain a predicted moment sequence; adaptive threshold detection is carried out on the predicted moment sequence, and moment peak values of different amplitudes are identified; analyzing the propagation velocity of the torque peak value, and calculating a velocity vector of peak value propagation by combining the propagation direction of the torque peak value; whether the velocity vector of peak propagation exceeds a preset threshold value or not is judged, and if yes, the response trend of a joint torque controller is predicted to judge whether joint control parameters of the humanoid robot need to be adjusted or not; and if the joint control parameters of the humanoid robot need to be adjusted, optimizing the rigidity and damping parameters of the impedance control model to output a stable control signal.

The present application belongs to the technical field of material processing, and relates to a mold-free mirror image filament laying method and a mold-free mirror image filament laying system. The method lays a filament ribbon in a mirror image symmetry, without a mold, thereby improving production flexibility and efficiency. The method acquires the temperature and pressure of the filament ribbon in real time, calculates the output power of a heater and the modification value of a filament laying end motion mechanism path by using temperature and pressure differences, and optimizes controller parameters through reinforcement learning, so as to realize stable control of the temperature and pressure. The system comprises a hardware part and a control system. The hardware part includes pressure and heat source components. The control system adjusts through real-time feedback to ensure the quality of filament laying. The present application solves the problem that the temperature and pressure are difficult to accurately control in the process of mold-free mirror image filament laying, and improves the performance of composite products.

The invention discloses an extruder control system for cable protection pipe production, and relates to the technical field of cable protection pipe processing. Comprising a sensor module used for collecting operation parameters of the extruder, the operation parameters comprise the charging barrel temperature, the machine head temperature, the extrusion pressure, the screw speed and the traction speed, and a control module connected with the sensor module and used for receiving the operation parameters and generating a control instruction based on preset control logic and algorithm. A fuzzy self-adaptive PID algorithm is adopted for temperature control, nonlinearity and time-varying properties of extrusion temperature control can be adapted, the temperature control precision is improved, and the influence of temperature fluctuation on material extrusion is reduced, so that the quality of a cable protection pipe is improved; interference factors such as material characteristic changes can be effectively handled, stable control over extrusion pressure is achieved, and the production quality and production efficiency of the cable protection pipe are guaranteed.

The invention relates to a dual three-phase permanent magnet synchronous generator rectification system and a direct current voltage stabilization control method thereof. Comprising a dual three-phase permanent magnet synchronous generator, a VSD decoupling converter, a load current observer, a dynamic load feed-forward compensator, a voltage closed-loop controller, a dead-beat predictive current controller, a maximum four-vector SVPWM modulation module and a six-phase rectifier. The double-three-phase topological structure adopted by the invention has the advantages of multiple control degrees of freedom, high reliability and the like. A dynamic load feed-forward compensator is constructed to perform dynamic feed-forward compensation on quadrature-axis current of the motor, so that the recovery time of bus voltage is shortened, and the voltage fluctuation amplitude is reduced; the designed dead-beat predictive current controller optimizes the dynamic characteristics of a current loop, improves the torque tracking performance of the motor, and further improves the voltage stabilization regulation capability of the system. The designed load current observer realizes the analysis redundancy of current detection and improves the reliability of the system.

The invention discloses a rotor fault positioning and power reconstruction system and method for a composite wing unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle fault-tolerant control. The system comprises a data acquisition module, a fault verification module and a power redistribution module, and is characterized in that firstly, a rotor fault-attitude change direction symbol mapping table is established according to a rotor physical layout and attitude dynamics relationship, and a fault type is identified; after symbol matching is met, a dynamic threshold energy verification function is constructed for judgment, and whether the rotor has a real fault or not is judged through a fault dual verification model of symbol matching and energy verification; a control distribution matrix is dynamically reconstructed according to the verified fault rotor position, power compensation is provided by coordinating normal rotors, and stable control over the flight attitude is achieved. The invention provides an effective method for reducing the false alarm rate and the fault response time of the sensor, so that a control system can quickly respond and recover the flight attitude when the rotor wing suddenly fails, the attitude control stability is ensured, and the flight safety is remarkably improved.

The invention discloses a method, a device and equipment for generating a stability control strategy of a wind power grid-connected system based on digital twinning and a medium. The method comprises the following steps: acquiring historical operation data and wind power operation data of the wind power grid-connected system; constructing a digital twinborn model and acquiring real-time measurement data according to the historical operation data in combination with a topological structure of the wind power grid-connected system; calling a preset filtering algorithm to perform state estimation optimization according to the real-time measurement data, and determining an optimal system state; extracting feature state vectors from the optimal system state and the wind power operation data; and predicting the feature state vector according to a built-in strategy through a preset target reinforcement learning model, generating a stability control strategy instruction, and issuing the stability control strategy instruction to a corresponding module in the wind power grid-connected system, thereby effectively improving the adaptability of wind power grid-connected control.

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

The invention provides a steam conveying control method and system based on multistage pressure regulation and dynamic feedback, and the method comprises the steps: obtaining real-time data, including pressure, flow and vibration signals, of a multi-source sensor in a steam pipe network, and forming a multi-dimensional data set; after judging that the water hammer phenomenon occurs, analyzing the vibration signal to obtain an abnormal vibration frequency in a pipe network, determining a propagation path of impact waves by combining pressure data, and positioning an area where an impact source is located; performing pressure grading control of partition differentiation according to the affected partitions; acquiring and analyzing the steam flow fluctuation condition of each affected subarea, analyzing the steam flow fluctuation condition of each subarea, determining target flow and priority, and adjusting the opening degree of an adjusting valve; real-time pressure and flow reading of a pipe network are continuously monitored, and setting of pressure adjusting points of all levels and the opening degree of the corresponding adjusting valve are dynamically adjusted so that steam conveying can be stably controlled.

The invention provides a medical RFQ accelerator low-level signal phase stability control method and system. The method comprises the following steps: constructing a time-frequency energy spectrum feature vector based on wavelet packet transformation; extracting a second disturbance feature based on a lightweight convolutional neural network and an attention mechanism; constructing a phase dynamic trend prediction module based on a long short-term memory network, and obtaining a first prediction phase error; constructing a phase compensation module based on a residual control network to obtain a second phase compensation amount; and outputting a real-time driving control signal based on the extended Kalman filter. According to the method, the time-frequency energy spectrum feature vector based on wavelet packet transformation is constructed, accurate characterization of the multi-scale disturbance features of the low-level signals is achieved, a medical RFQ accelerator phase dynamic compensation system is established in combination with a deep learning network and an extended Kalman filtering algorithm, the control precision and the anti-interference capability of signal phase stability are remarkably improved, and the method is suitable for popularization and application. The method is suitable for a high-precision medical particle accelerator control system.

The invention provides a multi-rotor unmanned aerial vehicle adaptive attitude stability control algorithm and device, and relates to the technical field of unmanned aerial vehicle control. The control algorithm comprises a geometric phase driving control process, a fractional order chaos game control process, a manifold resonance self-adaptive control process, a topology manifold self-adaptive control process and a multi-scale disturbance prediction control process, and all the processes of the control algorithm are executed through the main control processing unit. In order to solve the problems that a traditional PID control attitude error is 0.02-0.05 degree and is difficult to adapt to a dynamic environment, a geometric phase drive control flow is combined with a plasma pneumatic fine tuning array and an inertial measurement unit, a high-precision 3 * 3 rotation matrix is generated by adopting Lie algebraic transformation and a Newton-Rafson iteration method, the rotation speed of a rotor wing is optimized, and the precision of the rotor wing is improved. The attitude error is controlled to be 0.006 degrees, and the effect is improved compared with a traditional PID control effect.

The invention relates to a quantitative control method for a nonlinear system, computer equipment and a storage medium. The method comprises the following steps of: performing sector bounded processing on the output of a static logarithmic quantizer to establish an expression for quantizing control input; constructing a nonlinear system with disturbance, time lag and parameter uncertainty, and constructing a T-S fuzzy linear model of the nonlinear system according to the expression; constructing a target function according to the T-S fuzzy linear model, and constructing an optimization problem on an infinite time domain by taking a system state constraint, a control input constraint and a disturbance constraint as conditions so as to realize minimum quantization control input under maximum disturbance; and performing stability analysis on the T-S fuzzy linear model by constructing an LKF, and solving an optimization problem under the condition of meeting the stability analysis to obtain quantitative control input. By adopting the method, stable control on a nonlinear system can be effectively realized.

The invention discloses a millisecond regulation and control method for hydrogen fluoride production based on reinforcement learning and model prediction control, and relates to the field of hydrogen fluoride production regulation and control. In the multi-scale neural symbol dynamics modeling step, GNN, a cellular automaton and a state space model are fused, and parameters are updated in real time; in the causal reinforcement learning decision optimization step, an effect is calculated through a causal graph, and a reward function is optimized; in the time-space fractional order sliding mode control step, a fractional order sliding mode surface and a controller are designed, and rapid and stable control is achieved; in the memory enhancement element learning adaptation step, a DNC storage strategy is utilized, new working conditions are quickly adapted through element gradient, and millisecond-level precise regulation and control are achieved. According to the method, prediction errors are greatly reduced, the response speed is increased, and overshoot is reduced; the product yield is improved, the energy consumption is reduced, and new working conditions are quickly adapted; fault detection and risk early warning are more accurate, equipment operation is more stable, production efficiency is effectively improved, cost is reduced, and safety is enhanced.

The invention relates to the technical field of automatic control, and discloses a pressure closed-loop self-calibration method for an ultrahigh-speed jet injection process, and the method comprises the steps: initializing a system, and building a pressure and temperature combined compensation model; a high-precision sensor is used for collecting real-time pressure and temperature data; by calculating a pressure deviation intelligent switching control strategy, PID is combined with feed-forward compensation in a steady state, and a deep reinforcement learning algorithm is switched to realize quick response in a transient state; automatically triggering closed-loop self-calibration according to operation time or a deviation threshold value, executing zero point and full scale calibration and updating compensation parameters; and carrying out residual analysis by using a digital twinborn model to realize fault diagnosis and early warning. According to the invention, the problems of low control precision and poor long-term stability of an ultra-high-speed jet flow process under nonlinear and large-lag working conditions are solved, and high-precision stable control and system self-adaptive calibration under a wide speed range are realized.

The invention provides an overhead and portal crane lifting appliance control method based on multi-stage model predictive control, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring a control parameter and an initial state variable so as to define an operation boundary and an initial state, and establishing a dynamic model containing a coupling relationship among a trolley acceleration, a sling rope length acceleration and a sling swing angle; acquiring obstacle distribution by using space sensing equipment, and calculating the dynamic safety height of the lifting appliance by combining obstacle parameters and control parameters; judging whether the lifting appliance is in an obstacle avoidance stage or a target tracking stage according to the initial state variable and the dynamic safety height; constructing a model prediction controller and dynamically adjusting a state weight matrix and a control input weight matrix; finally, a control input sequence is generated through an optimization algorithm, and track safety and attitude stability control of the lifting appliance is achieved. According to the technical scheme, the swing angle of the lifting appliance is restrained, meanwhile, the obstacles are effectively avoided, and the safety of the lifting appliance in the whole operation process is guaranteed.

The invention relates to the technical field of cold box control methods, in particular to a rapid and stable control method and device for a cold box. According to the technical scheme, the system comprises a cold box historical state feature library, a dynamic feed-forward compensation calculation module and a cooperative control output module; the method specifically comprises the following steps: S1, constructing a cold box historical state feature library; and S2, historical state modeling and dynamic feed-forward compensation control strategy. The feed-forward dominant control is performed by using the historical optimal moving trajectory, so that the valve can be adjusted more actively and more closely to the actual thermal state of the cold box, and the time required for the cold box to reach the target stable working condition is remarkably shortened. The temperature control stability and safety are remarkably improved, the risks of cracks, leakage and even damage of equipment are greatly reduced, and the service life of the cold box is prolonged; the comprehensive utilization efficiency of applications such as liquid-air energy storage is improved; and by reducing the risk of thermal stress damage, the equipment maintenance cost and the accidental shutdown loss are reduced.

The invention relates to the technical field of sintering production, in particular to a self-adaptive control method for an ignition process of a metallurgical sintering machine, which comprises the following steps of: acquiring key parameters such as material layer thickness, hearth pressure, sintering machine speed and air box temperature in a sintering machine system in real time, and transmitting acquired data to an ignition control model unit; and parameters such as fuel and air flow and air volume are dynamically adjusted by adopting a self-adaptive optimization algorithm, so that rapid improvement and stable control of the ignition efficiency are realized. According to the method, temperature and fuel in the ignition process are evaluated through a big data analysis technology in combination with actual requirements of the sintering process, and an ignition control strategy is optimized in combination with an intelligent regulation and control algorithm. Aiming at the complexity of an ignition process of the metallurgical sintering machine, an adaptive optimization technology is utilized to realize accurate control of the ignition process, and optimization suggestions are comprehensively given according to an ignition temperature curve and a fuel consumption condition to timely guide an operator to adjust process parameters, so that the ignition efficiency is improved, the energy consumption is reduced, and the sintering quality is improved.

The invention belongs to the technical field of vacuum coating, and particularly relates to a vacuum coating machine electron gun control method and system.The control method comprises the steps that in the coating process, key real-time parameters reflecting the working state of an electron gun and the evaporation process are continuously collected and fused, and the current coating state is evaluated in real time based on the key real-time parameters; and in combination with preset coating process requirements, an electron beam control target instruction set of a next control period is dynamically generated through a built-in decision mechanism and is sent to a power controller, a focusing coil controller and a scanning coil controller of the electron gun, and the electron gun is driven to adjust the power output, the focusing state and the scanning motion of the electron gun in real time. Through accurate and stable control of the evaporation rate, material characteristic changes are effectively coped with through a strong closed loop of power and the evaporation rate; the dynamically planned scanning path can actively compensate the non-uniformity of the thermal field of the crucible, and beam spot energy distribution is optimized in combination with focusing adjustment, so that the uniformity of the thermal field is remarkably improved.

The embodiment of the invention provides an oxidation kettle temperature self-adaptive control system and a control method. The control system comprises a temperature detection module; the self-adaptive control algorithm module is used for dynamically adjusting the output of the heating or cooling device through data provided by the temperature detection module based on a self-adaptive control algorithm of a snow ablation algorithm; the feedback control module compares the output of the temperature detection module with a set temperature, the feedback control module adjusts control parameters and ensures that the temperature reaches a set value, the feedback control module adopts a double-closed-loop PID control system, and the double-closed-loop control structure comprises a self-adaptive PID controller of an outer loop and a rapid PID controller of an inner loop which are combined; and the man-machine interaction module allows an operator to set a target temperature and a control parameter, and displays a temperature change curve in real time. Through dynamic adjustment of PID controller parameters combined with real-time monitoring, intelligent analysis and an intelligent optimization algorithm and design of a double-closed-loop feedback system, quick response and stable control of temperature control are realized.

The invention discloses a dynamic environment robot adaptive control method and system, and relates to the technical field of robot adaptive control. According to the invention, dynamic compensation is realized based on multi-sensor fusion and real-time anomaly detection in combination with impedance compliance control; through a lightweight error model and a trajectory interpolation technology, the calculation efficiency is improved compared with traditional model prediction control, and high-frequency real-time correction is supported; the system adopts a modular design, and adapts to a heterogeneous robot platform by adjusting equivalent mass-damping-spring parameters; for sudden disturbance, trajectory smoothing and impedance control technologies are fused, the oscillation amplitude of the system is reduced, and execution jitter is effectively inhibited; according to the method, high-precision force tracking and stable control are achieved in scenes such as complex terrain walking and dynamic grabbing, the problems that traditional fixed parameter control response is lagged, model dependence is high and an actuator is overheated are solved, and the adaptive capacity of the robot to the dynamic environment and the task completion rate are remarkably improved.

The invention discloses a numerical control machine tool machining state monitoring method and device, and belongs to the technical field of digital machine tool control. The method comprises the following steps: step 1, constructing a double-layer sensing network system consisting of an environment layer and a machine tool layer, and realizing preliminary physical isolation of environment parameters and a machine tool thermal state; 2, forming a multi-dimensional parameter mapping library of environmental influence and self-heating; 3, decomposing the temperature change signal into a low-frequency environment component and a high-frequency processing thermal response component, respectively extracting environment characteristics and processing characteristics in the low-frequency environment component and the high-frequency processing thermal response component, and realizing real-time classification and decoupling of the temperature signal; 4, constructing a heat influence cross matrix, and quantitatively describing the influence degree and interaction effect of the environmental parameter change and the machine tool heating on the precision of each shaft; and 5, designing a dual-channel parallel compensation control mechanism based on an analysis result of the thermal influence cross matrix, generating a final compensation instruction, and implementing and executing the final compensation instruction to realize precision stable control.

The invention relates to the technical field of coal underground gasification control, in particular to a stable control method for the gasification process of deep coal passing through dirt band. The gas pressure distribution data and the pore change characteristic data of the deep coal gasification area are collected in real time through the distributed sensor array, the gas dynamic correction permeability is generated, the gas injection pressure is adjusted and dynamically corrected according to the gas dynamic correction permeability, and the gas pressure is kept stable; screening a potential risk area through a main stress direction deviation angle and a stress concentration coefficient in the dirt band layer, analyzing a shear stress threshold value of the potential risk area based on a stress-strain coupling model, and judging a stability evaluation index of the potential risk area; and when the stability evaluation index is lower than a threshold value, a multi-stage emergency regulation and control mechanism is triggered, and a dirt band layer reinforcement intervention scheme is generated. By means of multi-source data fusion and a dynamic feedback mechanism, the limitation of a traditional method for lagging response of the instability risk of the dirt band layer is broken through, and full-closed-loop intelligent stable control over the gasification process is achieved.

The invention discloses a data-driven event trigger control method for a linear variable-parameter nonlinear system, which comprises the following steps: for a discrete-time nonlinear system with an unknown model, expressing the nonlinear system as a parameter-dependent linear form by using a linear variable-parameter embedding method; constructing a convex polyhedron-based noise matrix set for unknown bounded noise in the measurement data, and proposing polyhedral data-driven LPV system representation by using offline collected state, input and scheduling variable data; designing a bounded dynamic event trigger transmission mechanism, and optimizing the use of communication resources by adjusting a trigger threshold in real time and setting the upper and lower bounds of a transmission interval; based on a discrete time cycle functional analysis method, controller gain and trigger parameters are jointly designed through a linear matrix inequality tool. The method does not depend on an accurate system model, stable control over a nonlinear system is achieved only through historical data, the communication frequency is remarkably reduced, and meanwhile the system performance and robustness are guaranteed.

The invention belongs to the field of distributed driving automobile control, and discloses a distributed driving automobile path tracking and stability control optimization method and related equipment, and the method comprises the steps: firstly building a tire cornering stiffness dynamic range through multi-source sensing data, and reducing the influence of nonlinear characteristics on a model; then, through a dynamic output feedback model prediction control algorithm, an estimation state sequence and an error boundary of a vehicle side slip angle are calculated online, an optimal control gain is matched in real time based on a two-dimensional table, a combined control quantity of a four-wheel steering angle and an additional yawing moment is formed, and parameter uncertainty caused by vehicle speed change is effectively compensated; and finally, torque distribution is carried out by adopting a quadratic programming algorithm and taking tire utilization rate minimization as a target. According to the method, the vehicle path tracking precision and the transverse stability under the complex working condition are improved, meanwhile, the tire saturation risk is reduced, the tire utilization rate is reduced, the transverse tracking error is reduced, and unified optimization of safety and controllability in the automatic driving process is achieved.

The invention relates to the technical field of sensor data fusion, in particular to a real-time dynamic attitude stability control method and system based on multi-sensor fusion, and the method comprises the following steps: collecting multi-source sensor data, and correcting to generate a synchronous sensor data set; calculating a stability coefficient, a signal quality coefficient and a consistency coefficient, weighting the signal quality coefficient and the consistency coefficient to generate a dynamic fusion weight coefficient, executing weighted fusion based on the weight, outputting a fusion attitude state estimation result through adaptive extended Kalman filtering, and generating an optimization control instruction sequence by combining with dynamic constraint model rolling optimization; according to the method, a dynamic weight mechanism based on noise and signal strength is introduced to achieve self-adaptive fusion, the synchronization precision and reliability are improved by combining consistency verification, abnormal signals are corrected through self-adaptive filtering, the attitude estimation stability is enhanced, and the performance of the system is improved. And a control instruction is generated through rolling optimization, so that rapid and accurate adjustment is realized, and attitude keeping and control consistency in a complex environment is improved.

The invention discloses a satellite attitude system anti-interference finite time control method under complex dynamic disturbance, and the method comprises the steps: building a rigid satellite attitude maneuver control model according to satellite attitude dynamics and kinematics equations; designing a self-adaptive fine disturbance observer to estimate the total disturbance of a satellite attitude system by combining the influence of the external disturbance variable on the rigid satellite attitude maneuver process, and outputting an estimated value of the external disturbance variable; a three-stage segmented sliding mode control method is designed, dynamic continuity of a three-stage sliding mode is ensured, and segmented sliding mode control output is obtained; and compensating the segmented sliding mode control output by using an estimated value of an external disturbance variable output by a self-adaptive fine disturbance observer to realize satellite attitude stable control. According to the method, the problem that the stability of the satellite is reduced in the attitude maneuvering performance process when external disturbance exists is solved, better observation performance and shorter attitude stabilization time are achieved, and the control precision of the satellite is effectively improved.

The invention provides a vehicle control method, electronic equipment and a storage medium, and the method comprises the steps: controlling the driving torque of wheels according to the actual slip rate and the target slip rate of the wheels under the condition that a low-load starting stability control function of a vehicle is activated, a first deviation between the target slip rate and the actual slip rate is maintained in a first set range; and according to the actual yaw velocity and the target yaw velocity of the vehicle, the rear wheel rotation angle is controlled, so that a second deviation between the target yaw velocity and the actual yaw velocity is maintained within a second set range. According to the technical scheme, the acceleration performance of the vehicle under the low-load starting working condition is effectively improved, the steering wheel correction amplitude and frequency of a driver are effectively reduced, the control confidence of the driver is enhanced, and the driving control experience is improved.

The invention discloses a multifunctional hydraulic system experiment table, which relates to the technical field of hydraulic system experiment tables and comprises a time base line synchronous control module, a phase lock correction stability control module, a false peak identification and correction module, a multi-dimensional fault-tolerant judgment control module and a reverse energy slow-release anti-reverse module. And the time base line synchronous control module is used for establishing a cross-sensor unified time base line control structure, generating a synchronous reference surface by using a high-stability crystal oscillator signal, and executing nanosecond phase calibration on sampling signals of the pressure sensor and the flow sensor by taking the synchronous reference surface as a unified time reference. According to the invention, high-precision synchronous acquisition of pressure and flow signals is realized through unified time base line and phase locking correction, and high-pressure transient test data is ensured to be real and stable; and meanwhile, control logic is reconstructed, and an energy slow release strategy is introduced, so that the pressure relief process is stable and controllable, pump reverse rotation and structural impact are avoided, and the safety, reliability and self-healing stability of the experiment table are remarkably improved.

The invention relates to a pan-tilt angle calculation method and device, an electronic device and a storage medium, and the method comprises the steps: obtaining initial gravitational acceleration data and initial angular velocity data, determining reconstructed gravitational acceleration data according to the initial gravitational acceleration data and the initial angular velocity data, and calculating the angle of a pan-tilt according to the reconstructed gravitational acceleration data; and determining an observation model according to the initial gravitational acceleration data, determining real gravitational acceleration data according to the reconstructed gravitational acceleration data and the observation model, obtaining a carrier coordinate gravitational acceleration, and determining a cradle head attitude angle according to the carrier coordinate gravitational acceleration and the real gravitational acceleration data. According to the method and the device, the problem of how to accurately calculate the attitude angle of the unmanned aerial vehicle holder is solved, noise interference is reduced, the real gravitational acceleration is restored, and the stability augmentation control capability of the holder is effectively improved.

The invention discloses a micro-grid layered stability control method and system, and relates to the technical field of micro-grid control. Periodic data and instruction signals of upper and lower layer control links of a micro-grid are collected, and reverse adjustment delay amount and an idle stroke deviation value during period switching are calculated; processing to obtain a period difference index and a collaborative adaptation index containing an impact coefficient; analyzing a time sequence action relationship between the period difference index and a micro-grid adjusting link, and calculating a connection deviation coefficient of a control rhythm by counting lagging frequency and amplitude of instruction response; potential problems of a micro-grid control link can be found in advance, stable operation time is prolonged through strategy adjustment when the instability risk is low, maintenance is conducted in time before the risk rises, power failure accidents caused by layered control mismatch are reduced, and the operation stability and reliability of the micro-grid are improved especially for the micro-grid with the high new energy proportion and large fluctuation.

The invention discloses an unmanned ship formation maneuvering control method considering intermittent communication failure and based on a memory cache mechanism, and belongs to the field of unmanned ship cooperative control. The method and the device are used for solving the problems of poor control robustness, insufficient formation flexibility and strong dependence on global information in a communication interruption scene in the prior art. The method comprises the following steps: establishing an unmanned ship kinematics and dynamics model; constructing a cache region and endowing historical data with different weights to realize state prediction compensation; defining a cluster communication topology and an affine transformation relationship, and dividing a leader and a follower; designing a positive minimum interval event triggering mechanism to reduce communication frequency; constructing a fully distributed state observer based on memory cache; and designing a distributed formation controller to realize flexible formation control. The method is suitable for stable control and dynamic formation in cooperative tasks of multiple unmanned ships in a limited water area or a complex environment.