1389 results about "Attitude control" patented technology

The invention discloses a fusion optimization method based on Kalman filtering and LSTM cascade. The fusion optimization method comprises three steps of dynamic state estimation, time sequence error modeling and closed-loop fusion optimization. IMU (Inertial Measurement Unit) data is used as input, dynamic state estimation is realized through Kalman filtering, modeling system errors are separated, an LSTM (Long Short Term Memory) network is used for carrying out time sequence modeling on a residual error sequence to capture nonlinear errors, and finally, a corrected state quantity is fed back to a Kalman filtering updating link through a closed-loop feedback mechanism. And collaborative optimization of error compensation and state estimation is realized. According to the method, IMU error accumulation is effectively inhibited through a dynamic-data dual-drive mechanism, the navigation precision and robustness in a complex scene are remarkably improved, and the requirements of high-dynamic applications such as intelligent driving and unmanned aerial vehicle navigation can be met. Finally, the optimized IMU data and the GNSS observation value are fused for integrated navigation, high-precision navigation solution is achieved through Kalman filtering, and indoor and outdoor seamless positioning and the all-attitude control requirement of a high-dynamic carrier are met.

The invention discloses a cable inspection robot attitude control method based on MPC and ADRC fusion, and the method comprises the steps: firstly, building a model prediction control (MPC) model, carrying out the prediction of the future state of a robot, solving an optimal control problem, and obtaining initial control input for achieving the internal dynamic compensation; secondly, an extended state observer (ESO) in an auto-disturbance rejection control (ADRC) model is adopted, and external disturbance is estimated in real time and used for compensating control input; thirdly, a fuzzy self-adaptive module based on fuzzy PID is introduced, the attitude error, the attitude error change rate and regeneration disturbance intensity generated based on estimation disturbance are collected in real time, the MPC weight and the ESO gain are dynamically adjusted, and the response speed and robustness of the system under variable working conditions are improved. According to the hierarchical control scheme, the defect that an existing single control method is difficult to keep the optimal control effect under variable working conditions is effectively overcome, and technical guarantee is provided for stable operation of the cable inspection robot in a high-risk and high-complexity environment.

The invention discloses an unmanned aerial vehicle flight control system and method with precise positioning and autonomous obstacle avoidance, and particularly relates to the field of control, and the system comprises a positioning module, an obstacle detection module, a data processing and analysis module, an autonomous obstacle avoidance algorithm module, and a flight control module. Static and dynamic obstacles are detected in real time by integrating the laser radar, the millimeter wave radar, the visual sensor and the ultrasonic sensor, and an obstacle information report is generated through a data fusion algorithm based on machine learning; based on the processed environment data, the system uses deep reinforcement learning and a model prediction control algorithm to generate a safe flight path in real time, and an obstacle avoidance decision is optimized; the flight control part adopts a hierarchical control framework, and precise flight of the unmanned aerial vehicle is realized through trajectory tracking, attitude control and actuator control.

The invention relates to the technical field of unmanned aerial vehicle flight attitude control, in particular to an unmanned aerial vehicle flight attitude adjustment method and system based on reinforcement learning. Comprising the following steps: constructing a dynamic environment grid map based on a digital map and a real-time semantic segmentation result, and generating an initial track by introducing a space-time constraint fast search random tree algorithm; collecting flight state information, environment perception information and image definition indexes of the unmanned aerial vehicle, inputting the flight state information, the environment perception information and the image definition indexes into a space-time attention encoder, and generating a semantic-fused state tensor; performing importance distribution on the state tensor through an information entropy weighting mechanism to obtain a weighted state vector; inputting into a Meta-SAC model with meta-learning ability, and outputting a target flight reference pose and an LQR controller dynamic gain coefficient; the control module drives the LQR controller to generate a flight control instruction based on the information; and a reward function is constructed based on the image quality and the energy consumption efficiency, and a reinforcement learning strategy is fed back in real time.

The invention discloses an unmanned aerial vehicle attitude feed-forward compensation control method for building wind field disturbance, and belongs to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: building a mapping relation between a building space position and a wind field disturbance characteristic; processing the real-time wind field data in combination with the mapping relation to generate a wind field prediction result; calculating a feed-forward compensation amount, and synchronously determining a dynamic weight factor reflecting the disturbance intensity of the wind field; obtaining an attitude error between a current attitude and an expected attitude of the unmanned aerial vehicle, and calculating a feedback control quantity; and performing weighted fusion on the feedforward compensation quantity and the feedback control quantity by using the dynamic weight factor, and generating a final control instruction to control the attitude of the unmanned aerial vehicle. According to the method, the wind field mapping relation is established and the wind field is predicted to calculate the feed-forward compensation quantity, and then the dynamic weight factor is used to carry out weighted fusion on the feed-forward compensation quantity and the feedback control quantity, so that the disturbance of the wind field can be actively inhibited, and the attitude control stability and precision of the unmanned aerial vehicle in a complex environment are improved.

The invention discloses an unmanned aerial vehicle visual positioning flight optimization system based on multi-source data fusion, and belongs to the technical field of unmanned aerial vehicle navigation and control. The system comprises a data acquisition module used for acquiring RGB-D image data and multiple physical data; the environment modeling module is used for constructing a local three-dimensional environment map; the path planning module is used for generating an obstacle avoidance flight path; the instruction generation module is used for converting the obstacle avoidance flight path into an attitude control instruction and an accelerator instruction; the state prediction module is used for obtaining an unmanned aerial vehicle prediction state vector at the next moment; the positioning switching module is used for generating compensated fusion positioning data; and the flight control module is used for executing the attitude control instruction and the accelerator instruction. Through the multi-source data fusion and state prediction compensation mechanism, the problems of positioning interruption and trajectory divergence caused by loss of visual signals and unstable flight caused by sudden change of multi-source coordinate switching in the prior art are solved.

The invention discloses an unmanned aerial vehicle autonomous homeward voyage charging system based on wireless charging, and relates to the field of flight control and energy management, and the system comprises the steps: monitoring the flight path, remaining power and environmental parameters of an unmanned aerial vehicle, predicting the remaining power required for completing a task, and triggering a homeward voyage condition signal and the current position information of the unmanned aerial vehicle; generating an optimal homeward flight path of the nearest wireless charging platform based on the homeward flight triggering condition signal and the current position information of the unmanned aerial vehicle; obtaining an optimal charging point based on the generated return path, and sending a deployment instruction to the mobile charging platform; the mobile charging platform advances according to the deployment instruction path, the unmanned aerial vehicle returns according to the optimal return path, the course is corrected through inertial navigation and a visual algorithm, and the landing point of the unmanned aerial vehicle is obtained. Fine adjustment and positioning of the flight attitude are formed by using the visual identification and attitude control module, and a closed-loop flight control mechanism based on image processing and attitude adjustment amount calculation is realized.

The invention discloses a four-rotor unmanned aerial vehicle model prediction control method based on a deep Q-network (DQN). The method comprises the following steps: firstly, establishing an inertial coordinate system and a body coordinate system, and establishing a kinematic equation and a kinetic equation of the four-rotor unmanned aerial vehicle based on a Newton second law and an Euler-Lagrange equation; according to the method, an unmanned aerial vehicle robust controller based on model predictive control (MPC) is designed, optimal control input is generated through dynamic optimization, and the trajectory tracking performance of the unmanned aerial vehicle is improved. For various disturbances existing in the whole tracking process, a DQN reinforcement learning algorithm and an MPC method are combined to design a flight control system, control rate errors caused by the disturbances are compensated, the stability of attitude control is enhanced, and the tracking precision and the anti-interference capability are improved. And finally, verifying the robust performance of the flight control system through a simulation experiment.

The invention discloses an aircraft attitude control method and device based on a semiconductor microcomputer system, and the method comprises the steps: collecting the multi-source sensor data of an aircraft, calculating an attitude error vector, and constructing a three-dimensional error space coordinate system; taking the three-dimensional error space coordinate system as a reference, adjusting the model in real time, and solving the optimal attitude adjustment amount in a prediction window period; the optimal attitude adjustment amount is input into an execution mechanism distribution module, and a thrust adjustment instruction sequence and a control surface deflection angle instruction queue of each vector propeller are generated through calculation in combination with a propeller response matrix and a control surface efficiency coefficient; dynamically correcting and predicting weight parameters of the controller through closed-loop verification; and updating the multi-objective optimization model based on the corrected parameters, adjusting the instruction distribution proportion through a dynamic reconfiguration interface, and writing the updated parameters into the attitude database to form a complete closed-loop period. According to the invention, high-precision real-time control of the attitude of the aircraft is realized, and the flight stability and the energy efficiency ratio in a complex environment are improved.

The invention provides a quadrotor unmanned aerial vehicle suspension swing suppression flight control method based on a DDPG. The method comprises the steps that a kinetic model of a quadrotor unmanned aerial vehicle suspension system is established; performing input shaping processing on an expected reference track signal by using a ZVD input shaper; the unmanned aerial vehicle suspension system is decoupled into position control of a position loop and attitude control of an attitude loop by adopting a cascade trajectory tracking control strategy, and a fuzzy PID controller of the position loop is designed based on a fuzzy theory; the shaped track signal is input into a fuzzy PID controller, and parameters of the PID controller are adjusted on line; performing attitude settlement on the virtual control quantity output by the fuzzy PID controller, and inputting the virtual control quantity into an active disturbance rejection controller; roll, pitch, and yaw torque control quantities are generated based on the virtual control quantity of the attitude loop and the control quantity generated by the DDPG-based tilt angle controller. Under a complex external interference environment, a preset track is tracked with high precision, load swing is effectively suppressed, reliable execution of a transportation task is successfully guaranteed, and meanwhile, the control precision and the anti-interference capability of the system are remarkably improved.

The invention belongs to the technical field of coupling alignment, and particularly relates to an intelligent auxiliary system for coupling alignment based on dual-laser displacement and an MEMS gyroscope, which adopts a mode of cooperative work of an active stable attitude measurement module and a space reference beacon module to establish a dynamic stable measurement basis which is not influenced by barring operation and environmental disturbance. The micro attitude change of the measuring device can be sensed and actively compensated in real time in the measuring process, it is ensured that laser displacement data at all angles are collected in the same absolute space coordinate system, and the high consistency and accuracy of multi-point measurement data are ensured. And then fusing the attitude feedback signal and the attitude reference signal by using an intelligent resolving module to generate an attitude control instruction, and processing displacement measurement data acquired by a double-laser displacement sensor after the attitude is stable to resolve a centering deviation parameter, thereby being beneficial to improving the efficiency, precision and automation level of coupling alignment work.

The invention discloses a curved surface mapping track optimization and multi-modal force control method for complex wall surface polishing. The method can efficiently meet the polishing requirement of a complex curved surface. A polishing track is determined by introducing a projection technology, a path optimization model is constructed, and multi-node force fusion attitude control is combined, so that interpolation point calculation and attitude change and position planning matching are realized, and a robot polishing path including a polishing position, an attitude and adjustment parameters is effectively generated. According to the multi-mode force control method, a force control mode is dynamically adjusted by combining radial height state switching of state classification of convex and concave path sections; multi-node fusion attitude control is further introduced, the surface contact force of a sand disc is accurately tracked, the tail end attitude of the robot is dynamically adjusted to follow the curved surface characteristics, the problems of irregular curved surfaces, unstable force control and the like in the traditional grinding technology are solved, and the method can be widely applied to automatic efficient grinding operation in the fields of buildings, ships and the like; and efficient and high-precision grinding of the complex curved surface is achieved.

The invention relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection monitoring system based on digital twinning. A twinborn data synchronization module of the system collects fan structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore fan digital twinborn body; the routing inspection path dynamic generation module analyzes a fan fragile area and risk distribution based on the digital twinborn body, and generates a multi-unmanned aerial vehicle cooperative routing inspection path in combination with real-time marine weather forecast; the unmanned aerial vehicle cooperative control module schedules an unmanned aerial vehicle group to execute a task according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module; and the real-time abnormity identification module fuses and analyzes unmanned aerial vehicle returned data, identifies fan defects, outputs abnormal information, calculates relative space deviation, generates an attitude control instruction, and sends the attitude control instruction to the unmanned aerial vehicle cooperative control module to realize unmanned aerial vehicle attitude real-time adjustment.

The invention relates to the technical field of unmanned aerial vehicle flight control, and particularly provides an unmanned aerial vehicle flight control method and system, and the method comprises the steps: obtaining real-time rotating speed information and real-time current signals corresponding to each rotor motor; acquiring expected rotating speed information and expected current information of each rotor motor according to the unmanned aerial vehicle attitude control instruction and the current flight state; for each rotor motor, analyzing whether the working state of the rotor motor is abnormal or not according to the corresponding real-time rotating speed information, the real-time current signal, the expected rotating speed information and the expected current information; when it is analyzed that the working state of the rotor motor is abnormal, a compensation control quantity is obtained according to the deviation between the real-time rotating speed information and the expected rotating speed information and the deviation between the real-time current information and the expected current information, and then working parameters of the rotor motor with the abnormal working state are adjusted according to the compensation control quantity; according to the method, the influence of disturbance caused by local airflow environment difference on the attitude of the unmanned aerial vehicle can be sensed and inhibited.

The invention discloses a dual-ring active-disturbance-rejection trajectory tracking control method for a four-rotor unmanned aerial vehicle, and the method comprises the steps: representing a dynamic coupling effect of the unmanned aerial vehicle caused by the load mass change and dynamic center-of-gravity shift through a six-degree-of-freedom flight dynamic model containing an asymmetric inertia matrix; an outer ring position control loop and an inner ring attitude control loop are constructed on the basis of an active disturbance rejection controller, and the following control cycles are executed in real time on the basis of integrating an extended state observer: the outer ring position control loop generates a position control signal and an attitude expected angle according to an expected track; the inner loop attitude control loop generates a control quantity for driving the unmanned aerial vehicle according to the attitude expected angle, and the two loops cooperatively work through decoupling logic to realize expected trajectory tracking; estimating and compensating unmodeled dynamics and external interference of the unmanned aerial vehicle in real time through the extended state observer; and dynamically adjusting parameters of the active-disturbance-rejection controller by adopting a particle swarm optimization algorithm.

The invention discloses an automatic stacking method and device, a terminal and a storage medium, and the method comprises the steps that package information of a to-be-stacked package is obtained according to an acquisition device, and an optimal stack type is matched for the to-be-stacked package in combination with historical stack type data of an automatic stacking system; carrying out point cloud segmentation on the tray with the stacked parcels through a preset segmentation model to obtain a segmentation result, calculating a tray gap in combination with the optimal stack type and a preset path planning algorithm, and generating a stacking path; the flexible clamping jaw device is controlled to grab the parcels to be stacked, the grabbing posture is corrected in real time in combination with feedback of the torque sensor, and the mechanical arm is controlled to execute stacking according to the stacking path; and when the segmentation result of the tray is matched with the optimal stack type, a stacking three-dimensional model is generated, the stacking three-dimensional model is bound with the AGV below the tray, and the AGV is controlled to be automatically put in storage. According to the method, stacking and warehousing are completed through stack type matching and path planning, and the adaptability and the space utilization rate of a stacking system are improved.

The invention discloses an autonomous navigation and obstacle avoidance method for an intelligent aircraft. The autonomous navigation and obstacle avoidance method comprises the following steps: S1, multi-modal data acquisition; s2, generating a motion path; s3, predicting an obstacle path; s4, flight obstacle avoidance; the invention discloses an autonomous navigation and obstacle avoidance system for an intelligent aircraft. The system comprises a sensor group, an edge calculation unit, an execution mechanism, an environment sensing and positioning module, a decision control module and a communication module, the autonomous navigation and obstacle avoidance capabilities of the aircraft are remarkably improved through multi-modal data fusion, three-dimensional environment map generation, dynamic obstacle prediction, path planning and attitude control; semantic tags are introduced to identify obstacle types, geometric consistency scores ensure authenticity and reliability of obstacles, and radar reflection intensity and point cloud density provide spatial distribution and material information; and the dynamic obstacle prediction model dynamically adjusts an obstacle avoidance strategy in combination with semantic tags, geometric consistency scores, radar reflection intensity and point cloud density.

An autonomous flying device achieving a large payload and a long continuous flight time and also accurately adjust position and orientation while flying. The device includes: a main rotor and the like that provide main thrust; a sub rotor and the like that controls the orientation; an engine that generates energy for rotating the main rotor and the like and the sub rotor and the like; and an arithmetic control device that controls rotation of the sub rotor and the like. Also, the main rotor and the like are rotated by being drivingly connected to the engine, whereas the sub rotor and the like are rotated by motors driven by electric power generated from generator and the like operated by the engine. Further, when orientation control to tilt the fuselage is performed, the arithmetic control device increases the output distribution ratio of the sub rotor to above the output distribution ratio of the sub rotor when hovering is performed.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a high-precision hovering system and method for an omni-directional flying robot, and the method comprises the steps: collecting the connection state of a module assembly, and allowing a flying action when the connection state is judged to be normal; collecting GNSS signal intensity of the positioning sensor, determining a positioning decision mode according to the GNSS signal intensity, and generating flight attitude control data according to the positioning decision mode; according to the flight attitude control data, the flight attitude of the flying robot is adjusted based on closed-loop PID control of a harmonic reducer and a servo motor; obtaining predicted wind speed data, determining disturbance torque, and performing feed-forward compensation on the flight attitude according to the disturbance torque; and controlling the central double rotors of the flying robot to continuously operate, and controlling the outer side rotors in the flying robot to start and stop based on Q-learning. According to the invention, the anti-wind hovering performance is guaranteed, the energy utilization rate is improved, and the environmental adaptability, the anti-wind stability and the cruising ability of the aircraft are enhanced.

The invention provides a box culvert jacking attitude intelligent control method and system based on digital twinning, and relates to the technical field of attitude control. Structural parameters of a box culvert entity, parameters of a construction environment, and elevation change data and axis offset data in a jacking process are obtained, and the two kinds of data are integrated to obtain a box culvert jacking attitude intelligent control system; generating a box culvert jacking attitude data frame sequence; according to box culvert entity reference information in the model and the updated box culvert real-time state information, combining the box culvert jacking attitude data frame sequence to determine box culvert actual attitude parameters, comparing the box culvert actual attitude parameters with box culvert design attitude parameters, determining attitude deviation types and attitude deviation values corresponding to the box culvert axis direction and the box culvert height direction respectively, and calculating the box culvert jacking attitude data frame sequence according to the attitude deviation types and the attitude deviation values. According to the method, the attitude adjustment control instruction is generated according to the attitude of the box culvert, so that the equipment drives the jack to adjust the jacking attitude of the box culvert according to the instruction, dynamic synchronization and accurate mapping of the structural state in the jacking process of the box culvert are realized, and the real-time performance, the accuracy and the control robustness of attitude adjustment are improved.

The present disclosure relates to a MEMS inertial sensor device in a semiconductor chip package that includes an integrated circuit configured to process inertial sensor data. Preferred implementations utilize inertial sensors having different sensitivity ranges to adjust operation of dynamic system control such as motion and or attitude control of autonomous vehicles.

The invention relates to the technical field of flight control, in particular to a hybrid control FPV unmanned aerial vehicle flight control system and method. The method comprises the following steps: acquiring triaxial angular velocity data of the unmanned aerial vehicle in real time by using an inertial navigation system, eliminating high-frequency noise and performing zero offset calibration; constructing a corresponding linear extended state observer to adjust a gain parameter in real time based on an absolute value of an observation error, and filtering tiny noise disturbance through a dead zone function to realize high-precision estimation of the system state and total disturbance of the unmanned aerial vehicle so as to obtain an error and an error change rate; and designing a self-adaptive PID control law to dynamically adjust PID control quantities corresponding to proportion, integral and differential gains, carrying out dynamic weighted stacking according to a flight mode, generating a three-axis control quantity, carrying out flight control on the FPV unmanned aerial vehicle, and meanwhile, realizing real-time visual verification of a control effect corresponding to the flight control. According to the invention, high-precision and high-robustness attitude control can be realized.

The invention relates to the technical field of path planning, in particular to a path planning method and system for a commercial service robot, and the method comprises the following steps: analyzing regional attributes to obtain passage levels, constructing task response coefficients to adjust a task sequence, evaluating path node conflicts, planning an execution path, recognizing obstacles, and correcting the path. And ground parameters are extracted to adjust the attitude control parameters, and robot attitude adjustment configuration is generated. According to the method, the regional function state and the real-time time are dynamically corresponded, the regional traffic attribute is adjusted in real time, the task response coefficient is constructed, the task priority is dynamically optimized, the traffic frequency and the stay duration of the cross nodes are comprehensively quantized, and the path execution priority is finely evaluated; according to the method, the obstacle displacement track and the contour feature are combined, behavior recognition and path dynamic adjustment are achieved, and the refinement and real-time performance of path planning of the robot in a dynamic scene are effectively improved by evaluating the ground structure parameter value and the interference level in real time.

The invention discloses a dynamic sea condition ship-machine cooperative attitude control system and method for sea grass transplantation, and belongs to the crossing field of marine ecological restoration and intelligent ship control. The system comprises a mother ship attitude control layer, a communication management layer, an underwater operation end and a sensor network layer, the mother ship attitude control layer, the communication management layer, the underwater operation end and sensor network layer and the mother ship attitude control layer are sequentially connected through lines; the method integrates inertial navigation, visual measurement, event-triggered communication and adaptive filtering technologies, realizes high-precision attitude synchronization and stable operation of the ship and the underwater operation device in sea conditions of four levels and below, and can be widely applied to marine ecological engineering scenes such as seaweed bed restoration, coral transplantation and submerged vegetation reconstruction. And the operation efficiency and the environmental adaptability are effectively improved.

A work machine includes a work apparatus configured to perform work on a ground surface, an aerial vehicle configured to fly by generating thrust, a connection mechanism configured to connect the work apparatus and the aerial vehicle, and a posture controller configured or programmed to control at least one of the aerial vehicle or the connection mechanism to control a posture of the work apparatus.

The invention discloses an underwater propeller thrust distribution control method and system based on QP optimization, which are used for avoiding redundant power consumption caused by improper distribution under redundant propeller configuration and prolonging underwater operation time and endurance mileage of a robot. The method comprises the steps that a preset attitude control instruction of the underwater robot and actual attitude information fed back by a current sensor are acquired, total disturbance acting on the underwater robot is estimated in real time based on the active disturbance rejection control principle, and expected total control force and total control torque instructions are calculated; constructing a quadratic programming problem based on the total control force and the total control moment instruction; solving a quadratic programming problem to obtain a first thrust instruction of each propeller after optimization; whether a propeller fault exists or not is diagnosed in real time, and when the fault is detected, a control efficiency matrix or a physical limitation constraint in the quadratic programming problem is dynamically adjusted; and solving again based on the adjusted quadratic programming problem to obtain a second thrust instruction of each propeller under fault tolerance.

The present disclosure discloses a method for attitude control of a quadrotor UAV, comprising establishing an attitude dynamics model of the quadrotor UAV, establishing a motion equation and a state-space equation of a UAV control system, determining an LADRC-CFO, and establishing a differential tracker for reducing a system overshoot.

The invention discloses an offshore rocket recovery platform attitude stability control method based on particle swarm optimization, and the method comprises the following steps: S1, collecting and preprocessing multi-source attitude data, and generating a time series data set; s2, constructing a fuzzy PID controller, and setting nine three-axis control parameters; s3, performing global optimization by adopting an improved particle swarm algorithm, and outputting an initial parameter solution; s4, introducing a zebra optimization algorithm to carry out local refined optimization; s5, constructing a collaborative optimization architecture, and fusing and outputting optimal control parameters; s6, the optimal parameters are input into a controller, and a servo system is driven to adjust the three-axis attitude; s7, monitoring disturbance amplitude, dynamically triggering a zebra strategy and feeding back the zebra strategy to the particle swarm; and S8, evaluating the control performance, and feeding back iterative optimization if the control performance does not reach the standard. According to the method, the improved particle swarm optimization algorithm and the zebra optimization algorithm are fused, so that self-adaptive optimization and accurate attitude stable control of attitude control parameters of the offshore rocket recovery platform are realized.

The invention relates to the technical field of attitude control, and discloses a method for realizing self-adaptive active leveling compensation, which comprises the following steps: acquiring real-time data of a vehicle, the data being provided by a plurality of sensors; processing the real-time data through a data fusion algorithm; according to the inclination state and the motion parameters of the vehicle, hydraulic supporting leg adjusting parameters are calculated through a multi-objective optimization algorithm; according to the calculated hydraulic supporting leg adjusting parameters, the action of the hydraulic supporting leg is adjusted through a PI D controller for vehicle balance recovery; the stability of the hydraulic supporting leg adjusting system is verified through the Lyapunov stability theory, and the hydraulic supporting leg adjusting system is used for enabling the adjusting process of the hydraulic supporting leg to be completed in all operation states; and the vehicle inclination state is fed back in real time. By fusing multi-sensor data and adopting a Kalman filtering or particle filtering algorithm, high-precision perception of the real-time inclination state and dynamic parameters of the vehicle is realized, and a more accurate and reliable vehicle attitude judgment effect is obtained.

The invention provides a PPO-based spacecraft attitude and orbit integrated game control method and device, and the method comprises the steps: building a spacecraft attitude and orbit integrated model, and carrying out the modeling of the interaction process of a tracking spacecraft and a target spacecraft as a non-cooperative zero-sum game; setting a multi-agent deep reinforcement learning framework, wherein the tracking spacecraft and the target spacecraft are respectively configured with an independent strategy network and an independent value network; training is carried out in a double-agent PPO mode, control instructions of orbit thrust and attitude torque are generated through the strategy network, and the state value is evaluated through the value network; and controlling and guiding the behavior of the spacecraft according to the trained strategy. According to the method, the autonomous decision-making capability and cooperative control performance of the spacecraft in complex tasks are effectively improved, and the method is suitable for high-precision aerospace tasks such as interception of non-cooperative targets, autonomous trajectory planning and attitude control.