1257 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.

Provided in the present invention is an interference-factor-based high-safety and stable control method for an onboard magnetically suspended flywheel battery system. The method comprises: in the case of a safe operating condition interference, obtaining the difference between a displacement reference signal and an actual displacement signal, and inputting the difference into a Butterworth-filter-fused fuzzy active disturbance rejection control module, so as to obtain control currents, wherein the control currents comprise a radial control current, a torsion control current and an axial control current; the radial control current and the torsion control current are subjected to Clark inverse transformation, and the axial control current is subjected to power amplification, and the three currents are then compensated for, such that a three-phase control current of a radial coil, a three-phase control current of a torsion coil, and a control current of an axial coil are obtained; the control currents control the corresponding coils, and are also input into a multi-information extended Kalman filtering prediction module, such that the actual displacement signal is output; and in the case of a dangerous operating condition interference, cutting off an output signal from an operating condition recognition and safety determination module. The present invention can improve the stability, robustness and anti-interference capability of a system.

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 stabilization control method and system for a fuel filling ship at a zero navigational speed. The method provided by the invention comprises the following steps: acquiring rolling data and wind wave data of a refueling ship under a target sea condition in a fuel refueling process in real time; the rolling data and the wind wave data are processed based on a radial basis function neural network, and the precession angular velocity of the stabilization gyro device under the target sea condition is obtained through calculation; the stabilization gyro device is controlled to rotate at the precession angular speed, the stabilization gyro device generates a gyroscopic effect and generates rolling moment, the rolling moment is converted into stabilizing moment for resisting rolling through precession motion, and the rolling angle of the filling ship is reduced; and the stabilizing moment is opposite to the vibration direction of the rolling. According to the method and system, rolling control of the filling ship at the low speed or the zero navigational speed is achieved, the stabilization torque can be adjusted in real time according to sea wave changes, and the stability and safety of the filling ship in the fuel filling operation process are improved.

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 relates to the field of forging processes, and discloses an efficient forging process for an output shaft shared by ship accessories and mining machinery, which comprises the following steps of: 1, simulating the stress condition of a mold by adopting finite element analysis through mold design and optimization, ensuring uniform stress of the mold, and avoiding mold wear and material segregation; secondly, the forging force and the deformation rate are precisely controlled through a numerical control forging machine tool, so that stable control over the forming precision of the forge piece is achieved; thirdly, a composite forging process is adopted, wherein the composite forging process comprises a pre-forging process and a precision forging process; 4, optimizing a heat treatment process; and 5, monitoring temperature, stress and deformation data in real time in cooperation with an intelligent temperature control and monitoring system. The forging force and the deformation rate are precisely controlled through the numerical control forging machine tool, the high consistency of the shape and the size of the forge piece in the forging process is guaranteed, error accumulation in the forging process is avoided, and the forming precision of the forge piece is improved.

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 discloses a vehicle control method, electronic equipment, a collision stability control system and a vehicle. The vehicle control method comprises the steps that it is determined that the vehicle meets an unstable state; and according to the target rear wheel steering angle value, the rotation angle of the rear wheels of the vehicle is controlled, and according to the target torque value, a four-wheel motor of the vehicle is controlled to conduct driving and / or braking. According to the method, when the vehicle is in the unstable state, the rear wheel turning angle can be adjusted, the four-wheel motor of the whole vehicle can be driven and / or braked, additional yaw compensation torque is generated to maintain the stability of the vehicle, a driver is helped to correct the posture of the vehicle body in the initial stage of the unstable state, and losses caused by secondary collision after the vehicle is unstable are avoided.

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 discloses an aircraft self-learning optimization control method based on adaptive dynamic programming, and the method comprises the steps: firstly, designing a basic stability controller under a backstepping method frame in order to guarantee the stable control of an aircraft under the uncertain and unknown external disturbance of a model; then, considering that the tracking performance of the system has a weakening trend along with disturbance change, introducing an ADP-based parameter self-adjustment intelligent optimization algorithm to design an auxiliary controller, and obtaining a compensation control signal by executing and evaluating updating adjustment of a network weight along with tracking deviation so as to further reduce the tracking error; and the self-learning and self-adaptive capabilities of the controller to environmental changes are realized. And finally, integrating the basic controller and the auxiliary optimization controller to finally obtain a complete robust self-learning optimization controller. The method is high in universality and high in calculation precision, and has important engineering practice reference significance.

The invention relates to an amphibious unmanned vehicle amphibious actuator cooperative control method, belongs to the technical field of amphibious unmanned vehicles, and solves the problem of poor adaptability of amphibious unmanned vehicles in different scenes in the prior art. The method comprises the following steps: acquiring suspension state data and attitude angle data of the amphibious unmanned vehicle at each moment; at the current moment, predicting the overwater model mismatch probability based on the trained recurrent neural network according to the suspension state data and the attitude angle data at the current moment and the historical moment; determining whether the current scene key point is a scene key point based on the predicted overwater model mismatch probability; the scene key points comprise a landing point, a water leaving point, a water entering point and a full floating point; and calculating an actuating mechanism action instruction of the amphibious unmanned vehicle according to whether the current key point is the scene key point or not and the predicted overwater model mismatch probability. And stable control of the amphibious unmanned vehicle under the land and water working conditions is realized.

The invention discloses a stable control method for a deformation process of an unmanned aerial vehicle in a complex environment, and relates to the technical field of unmanned aerial vehicle flight control. The state-space equation is used for describing flight dynamics parameters in the deformation process of the unmanned aerial vehicle in the complex environment; constructing a disturbance observer based on the state-space equation; the disturbance observer is used for observing a lumped disturbance quantity caused by a complex environment and wing tip folding deformation; constructing a sliding mode surface equation according to the attitude angle tracking error; constructing an attitude stability controller according to the disturbance observer and the sliding mode surface equation; and based on the Lyapunov stability theory, performing stable control on the unmanned aerial vehicle by using an attitude stability controller. The lumped disturbance quantity caused by the complex environment and wing tip folding deformation is considered during flight control of the unmanned aerial vehicle, and the control precision of the deformable unmanned aerial vehicle under the complex environment condition can be 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 discloses a USV rotary column stabilization control system considering complex disturbance and motor dynamic characteristics, belongs to the field of motion control systems of ocean mobile observation platforms, and aims to improve the stability and control efficiency of USV navigation. The method comprises the following specific steps: firstly, establishing a rotary column stabilizer lift model, a USV nonlinear rolling model and a servo motor dynamic model; then designing a Backstepping controller based on a differential item disturbance observer, estimating and compensating disturbance caused by external and model uncertainty in real time, then adjusting expected control input in real time, and ensuring that the output rolling angle tracks the expected rolling angle. The MPC controller based on servo motor state feedback is designed, the state quantity of the motor is fed back to the controller for prediction and adjustment, the problems of response lag and output overload of the servo motor in complex sea conditions are effectively solved, rolling angle error minimization and effective control over energy consumption are further achieved, and the service life of the servo motor is prolonged. And the purpose of multi-objective optimization is achieved.

The invention provides a modulation and control method of a bridge arm multiplexing type FSBB-CLLLC bidirectional converter, belongs to the technical field of power electronics, and solves the problems that the modulation method of the bridge arm multiplexing type FSBB-CLLLC converter is not clear, the working principle is not clear, and the efficiency is reduced due to the fact that soft switching implementation is complex. The main circuit topology is divided into different bridge arms; different working modes of the converter during forward and reverse operation are researched by combining a bridge arm phase shift angle and a duty ratio; researching and solving the relationship between the inductive current and the phase shift angle of the converter by combining a modulation method and a soft switching condition, and establishing a three-dimensional table; according to different input and output voltage requirements, the duty ratio is determined, the optimal phase shift angle is determined after table look-up, and full-switching tube soft switching of the FSBB-CLLLC converter under voltage stabilization control is achieved.

The invention belongs to the technical field of unmanned aerial vehicle accompanying flight, and particularly relates to a guiding system based on a vehicle-mounted accompanying flight unmanned aerial vehicle, which comprises the steps of carrying out state calculation on the unmanned aerial vehicle, judging whether the unmanned aerial vehicle reaches a take-off condition, checking battery power endurance of the unmanned aerial vehicle, whether a communication link is stable, and whether sending and receiving states of a control instruction are normal. The vehicle-mounted interaction platform is in signal transmission with the vehicle-mounted terminal, obtains the position information and state of the unmanned aerial vehicle in real time, maps the position information and state to a vehicle-mounted terminal screen and keeps a real-time interaction state with the unmanned aerial vehicle, and the vehicle-mounted terminal obtains the flight normal state information and the flight dynamic information of the unmanned aerial vehicle in real time and controls the unmanned aerial vehicle according to the two-state information of the unmanned aerial vehicle. The vehicle-mounted terminal can select to take over or the vehicle-mounted trusteeship platform to perform trusteeship, and the control system can send a landing instruction. According to the invention, exploration operation tasks in a large area can be completed more quickly and autonomously, dangers possibly foreseen by vehicles are avoided or eliminated, the driving safety is improved, and the dependence of the unmanned aerial vehicle on manual control is reduced.