5161 results about "Kinetic model" patented technology

The invention discloses a heterogeneous double-mechanical-arm tail end pose mapping method, system and device and a storage medium, and the method comprises the following steps: S1, obtaining the angular velocity, position and tail end pose information of each joint of a main mechanical arm, and calculating the spatial position and pose of the tail end of the main mechanical arm; s2, motion trail data in the teaching process of the main mechanical arm is collected, a long-short-term memory network model is established, the pose of the future time step length is predicted, and the motion trail of the auxiliary mechanical arm is planned; s3, data information is transmitted, and the problem of communication delay is solved; s4, the pose information of the tail end of the main mechanical arm is solved, the joint angle is adjusted, and the error is adjusted in combination with proportional-integral-differential control; s5, the position and speed signals are filtered, a mechanical arm dynamic model is established, an adaptive control law is designed, and a trajectory smoothing algorithm is adopted; and S6, establishing a virtual obstacle map, and limiting a safe working space. According to the method, comprehensive and accurate mapping and prediction of the tail end poses of the heterogeneous double mechanical arms are achieved.

The invention discloses an unmanned aerial vehicle trajectory planning and tracking method, system and device based on deep reinforcement learning and adaptive nonlinear model predictive control, and a medium. The method comprises the following steps: constructing various static multi-obstacle and dynamic multi-obstacle simulation environments; constructing a kinetic model of the unmanned aerial vehicle; constructing an adaptive nonlinear model predictive control (ANMPC) algorithm; constructing a reward function of the tracking performance of the unmanned aerial vehicle to the reference trajectory generated by the adaptive nonlinear model predictive control algorithm; constructing a network framework based on deep reinforcement learning and an adaptive nonlinear model predictive control algorithm; setting network parameters; training a network framework, and selecting an optimal weight file; outputting a test result; the system, the device and the medium are used for realizing the unmanned aerial vehicle trajectory planning and tracking method. The method can effectively cope with changes of targets and environments, shows strong obstacle avoidance capability and anti-interference performance when facing dynamic obstacles and wind noise interference, and embodies a high intelligent decision-making level.

The invention provides an intelligent networked automobile chassis drive-by-wire simulation method and system based on multi-domain modeling, and relates to the technical field of adaptive control, and the method comprises the steps: obtaining structural parameters and kinetic parameters of an intelligent networked automobile chassis, constructing a finite element grid, building a multi-flexible-body kinetic model corresponding to the intelligent networked automobile chassis, and constructing a multi-flexible-body dynamic model corresponding to the intelligent networked automobile chassis; introducing a coupling coordination equation, establishing a multi-flexible-body kinetic equation, and solving to obtain vibration response and stress-strain distribution under different working conditions; generating a plurality of finite element models, performing multi-physical field fusion and multi-error source fusion models, performing online error identification and active compensation, performing all-condition drive-by-wire chassis closed-loop control, and generating a drive-by-wire chassis closed-loop integrated simulation model; constructing a road three-dimensional parameterized model, generating a vehicle-road coupling model, generating a simulation test case, identifying a performance short board, constructing a knowledge graph and an optimization rule base, and generating an optimal control strategy.

The invention belongs to the technical field of robot control, and particularly relates to a mechanical arm RBF network dynamic self-adaptive control method under the constraint of a time-varying mechanism, which comprises the following steps of: constructing a mechanical arm dynamic model, determining a system specified time convergence standard, combining a joint motion reference trajectory of a mechanical arm, defining a trajectory tracking error, and determining a mechanical arm dynamic model based on a dynamic nominal model. Constructing a stable robust control law of the nominal dynamical model under the specified time; and selecting a radial basis function, and designing an RBF network adaptive control law in a specified time to dynamically fit a comprehensive nonlinear disturbance term in the system. The problem that the convergence time of a traditional method is uncontrollable is solved, the RBF neural network is adopted to dynamically approach comprehensive disturbance, the network weight is updated online through the adaptive law of the time-varying gain, the anti-jamming capability is remarkably improved, a model driving method and a data driving method are combined, and the convergence time of the model driving method and the convergence time of the data driving method are greatly improved through a dynamic model decoupling and feedforward compensation strategy. And more accurate and low-delay trajectory tracking control is realized.

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

The invention relates to an intelligent water temperature control system and method based on machine learning. The method comprises the steps that water temperature, flow, pressure and environmental parameter data of a constant water temperature control system are collected, and a standardized time sequence data set is generated; constructing a time-varying dynamic model based on the standardized time sequence data set, and performing parameter identification by using a variable forgetting factor generalized subspace tracker to obtain a time-varying pseudo-modal temperature parameter; training a multi-branch long short-term memory network according to the time-varying pseudo-modal temperature parameters and the standardized time sequence data set to obtain an initial water temperature prediction model; performing system operation anomaly detection and prediction based on the initial water temperature prediction model to obtain an anomaly detection and prediction result; solving a control strategy based on the anomaly detection and prediction result to obtain an optimal water temperature control strategy; and executing water temperature control based on the optimal water temperature control strategy, and updating the initial water temperature prediction model to obtain a target water temperature prediction model. According to the intelligent water temperature control system, accurate control over intelligent water temperature is achieved.

The invention discloses an unmanned aerial vehicle-mechanical arm system cooperative control method for precise spraying. According to the method, through a depth camera and a point cloud reconstruction algorithm, three-dimensional modeling of a target surface and semantic recognition of a spraying area are achieved, and a spraying path and spraying posture data are generated. Based on spraying task requirements, a spraying film thickness experience estimation model is established, and path point-level spraying parameters are obtained. Load mass change, mechanical arm mass center change and spraying reaction force of each path point are calculated, and a coupling dynamic model is constructed in combination with the system state. And dynamic compensation control input is generated by using an adaptive sliding mode control algorithm. After spraying is completed, the actual spraying effect is collected through image and point cloud detection, color, film thickness and texture features are extracted and compared with task requirements, error feedback is generated, and control parameters are adjusted online. According to the method, the problems of dynamic change and error compensation in the spraying process are effectively solved, and the method is suitable for a high-precision spraying task in a complex environment.

The invention relates to the technical field of parameter adjustment, and discloses a low-temperature evaporative crystallization machine parameter adjustment method, device and equipment and a storage medium, and the method comprises the steps: collecting M groups of temperature sensor data and N groups of pressure sensor data of a low-temperature evaporative crystallization machine, calculating a first uncertainty representation of temperature parameter measurement and a second uncertainty representation of pressure parameter measurement; performing data fusion to obtain an optimal estimated value of a temperature state and an optimal estimated value of a pressure state; parallel prediction is carried out through the thermodynamic equilibrium model, the crystallization kinetic model and the system response model, and a crystallization process prediction result is obtained; according to the technical scheme of the invention, the interference of the feeding fluctuation, the environmental change and the like can be effectively handled, and the real-time updating of the control strategy and the accurate adjustment of the temperature and pressure parameters can be realized.

The invention relates to a mechanical arm safe obstacle avoidance control method and system based on a high-order control obstacle function, aiming at a multi-degree-of-freedom mechanical arm system with an obstacle in a task trajectory space, the high-order control obstacle function suitable for a high-relative-order system is used, and the method comprises the following steps: establishing a dynamic model of a multi-degree-of-freedom mechanical arm; designing a stable tracking controller based on the task trajectory in combination with a backstepping control method; an obstacle collision detection model is established, and safety constraints are provided for a control system through a high-order control obstacle function; and in combination with the control constraint and the optimal control problem, solving the optimal control input through quadratic programming. According to the method, the optimal task trajectory tracking can be completed on the premise that the tail end of the mechanical arm meets the safety constraint, the calculation complexity is effectively reduced, and the real-time performance is guaranteed.

A legged robot planning and control method and apparatus, a robot, and a storage medium. The method comprises: according to the motion state of a legged robot and swing parameters of each leg at the next moment, determining a foot-end reference state of each leg; for a leg to swing at the next moment, according to a foot-end dynamics model and a discrete collision model, planning a swing-leg trajectory having collision awareness; and, according to the motion state, a reference state sequence and the foot-end reference states, controlling the whole body of the legged robot, wherein for a leg which should be a planned supporting leg but is not in contact with the ground, whole-body control having collision awareness is performed according to a whole-body dynamics model and the discrete collision model. Thus, the problems are solved that legged robots in the prior art are limited in passive flexible buffering in collision, involves a large amount of control algorithm calculation and relatively low control precision, exhibit a relatively high degree of hardware wear and relatively short service lives, and cannot meet actual needs.

The invention discloses a covariance self-adaptive control method for a quadruped robot for spine joint adjustment. The method comprises the following steps that 1, a spine quadruped robot kinematics model with spine joints, a robot trunk dynamics model with the spine joints and a robot leg multi-rigid-body dynamics model are established; 2, based on the energy stability margin theorem and the spine joint coupling effect, a posture control method of the spine joint quadruped robot is provided by combining a covariance self-adaptive evolutionary strategy, and the angle, rigidity and damping coefficient of spine joints are estimated and adjusted according to the terrain gradient; 3, extracting the spatial relationship between the trunk mass center and the foot ends of the robot as an expected reference, updating the expected mass center position in real time, and constructing a model prediction control method to control the posture of the spinal joint quadruped robot; 4, the position of the foot falling point of the swing leg is designed in advance in combination with the additional freedom degree provided by the spine joint and MPC prediction state information. The motion stability of the robot in the complex terrain is improved.

The invention relates to an autonomous navigation method for lunar satellite formation and a second-order filtering navigation architecture, and the autonomous navigation method comprises the steps: obtaining orbit prediction information based on a kinetic model of the lunar satellite formation, and obtaining collaborative theory observation information based on a collaborative observation model, carrying out fusion processing on orbit prediction information, collaborative theory observation information and multi-source observation data of in-orbit observation by using an extended Kalman filtering algorithm, and estimating an absolute orbit state of the lunar satellite formation; establishing a relative motion model of the slave satellite under the local coordinate system of the master satellite, and establishing a relative measurement model; and taking the absolute orbit state of the master satellite as a reference datum, obtaining a preliminary prediction relative orbit state based on the relative motion model of the slave satellite, and obtaining relative measurement data including distance measurement and angle measurement based on the relative measurement model, and fusing the relative measurement data and the preliminarily predicted relative orbit state through an extended Kalman filtering algorithm to estimate the relative orbit state of the slave satellite. Navigation may be independent of ground support.

The invention discloses a dynamic control method for a complex curved surface five-axis numerical control machining tool vector, which comprises the following steps of: scanning the surface of a target workpiece by using a high-precision three-dimensional scanner to obtain point cloud data; based on the point cloud data, establishing a mathematical model S (u, v) of the target complex curved surface; a mathematical model based on the target complex curved surface; establishing a comprehensive dynamic model of the five-axis numerical control system; optimizing the initial tool posture in combination with the kinetic model; collecting dynamic operation data of the cutter and the workpiece; on the basis of the dynamic operation data, a model predictive control (MPC) algorithm is used for adjusting the machining path and the cutter direction in real time; according to the method, a comprehensive dynamic model of a five-axis numerical control system is established, dynamic coupling effects of a machine tool kinematic chain, cutter flexibility, cutting force, a temperature field and a stress field are brought into a unified framework, and complex dynamic behaviors are accurately described and controlled through multi-body dynamic modeling and an optimization algorithm.

A deep reinforcement learning-based vehicle platoon control method and system. The method comprises: on the basis of a vehicle dynamics model, constructing a motion simulation model for vehicle platoon operation; on the basis of the motion simulation model for vehicle platoon operation and vehicle operating states, by using a constant time headway strategy, obtaining a state space and an action space for vehicle platoon control; and establishing a DDPG network on the basis of the state space and the action space for vehicle platoon control, using a reward function mechanism to train the DDPG network, stopping training when the total reward obtained per episode during the training process no longer increases, so as to obtain a trained control strategy network, and by using the trained control strategy network, performing platoon control.

The invention provides a low-altitude airspace collaborative management method and system based on digital twinning, and the method comprises the steps: analyzing the maneuvering characteristics and flight envelope characteristic parameters of an aircraft based on the obtained real-time state data of the aircraft, and calculating the maneuvering characteristics and flight envelope characteristic parameters of the aircraft by using kinematics and dynamics models, describing motion laws of different aircrafts; real-time flight data of the aircraft are combined, the motion track and attitude change of the aircraft are synchronized in real time in a virtual environment through a digital twin technology, the dynamic behavior of the aircraft is accurately reproduced, and a high-fidelity digital model is provided for flight conflict detection and flight path prediction; a multi-level and multi-granularity digital twin model is constructed according to the difference of the motion characteristics of the aircrafts, and the digital twin model comprises an airspace structure model of a macroscopic level, an aircraft group behavior model of a mesoscopic level and a single aircraft dynamic model of a microscopic level.

The invention provides an intelligent anesthesia depth monitoring method and system. A plurality of physiological parameter sensors are used for synchronously collecting composite physiological signals of a patient, and meanwhile brain wave signals of the patient in the anesthesia process are obtained; analyzing and processing the anesthesia state by fusing an electroencephalogram spectrum analysis algorithm and a nonlinear kinetic model to obtain a consciousness state map of anesthesia depth and consciousness edge fluctuation; performing real-time evaluation on the current anesthesia depth by using an adaptive threshold algorithm in combination with a Bayesian reasoning framework, and performing prediction processing on the trend change of the anesthesia depth in a future short time by using a long-short term memory neural network to obtain an evaluation result and a prediction trend; the anesthetic infusion rate is automatically adjusted so as to maintain the ideal anesthesia depth, meanwhile, potential risks are subjected to intelligent grading early warning processing, and an anesthesia management strategy is generated. According to the technical scheme provided by the invention, the safety and personalized management level of the anesthesia process are improved.

The invention discloses a saline-alkali soil dynamic precise irrigation method based on a water-salt-carbon flux coupling model, and relates to the technical field of saline-alkali soil improvement and precise irrigation, and the method comprises the following steps: dividing soil into a surface layer, a middle layer and a deep layer, building the coupling model based on a Darcy law, a convection-dispersion equation and a first-order dynamic model, and simulating a water-salt-carbon flux dynamic state; collecting data such as soil water content, conductivity and organic carbon content through a layered sensor network, and inputting the data into the model after cleaning and fusing; the irrigation time, the irrigation amount and the water quality are optimized through a genetic algorithm in combination with the crop growth period requirements; irrigation is executed through an intelligent valve and a variable frequency water pump, and model parameters are adjusted based on real-time monitoring feedback. According to the method, multi-element cooperative regulation and control of saline-alkali soil irrigation are achieved, the soil ecological process can be accurately simulated, the water resource utilization efficiency is improved, the soil environment is improved, and crop yield increase is promoted.

The invention relates to the field of risk assessment of intelligent driving vehicles, in particular to a dynamic instability risk assessment method for a driving track of an intelligent driving vehicle, a dynamic instability risk assessment system for the driving track of the intelligent driving vehicle, computer equipment and a storage medium. And the future vehicle trajectory information given by the trajectory planning and predicting module is used as input, and the front wheel turning angle and the vehicle speed required by the vehicle tracking trajectory are obtained. And then the model is used as the input of a nonlinear two-degree-of-freedom vehicle dynamic model based on a UniTile-Ctrl tire model to describe the nonlinear dynamic response of the vehicle under the complex working condition, and the prediction of the dynamic state of the vehicle is realized. On the basis, a vehicle stability evaluation method based on a phase plane is introduced, and evaluation and pre-judgment of the dynamic instability risk of the vehicle driving track are achieved. The method is of great significance in solving the dynamic instability risk evaluation of the driving track of the intelligent driving vehicle under the complex working condition.

The invention relates to a real-time monitoring and optimizing method and system for resource utilization of construction waste. The method comprises the following steps: carrying out data acquisition on vibration frequencies, material images and weight data of a crusher, a screening machine and a sorting device through a multi-source sensor network to obtain an original parameter data set; performing feature extraction to obtain a standardized feature data set; establishing a crushing kinetic model, a screening efficiency model and a sorting process model, carrying out coupling through a material balance equation and an energy balance equation, and carrying out model parameter identification by adopting a least square method and a genetic algorithm to obtain a technological process mathematical model; inputting the current process parameter combination and the historical quality data into an epsilon-constraint optimizer to obtain a Pareto optimal solution of the resource conversion rate and the energy consumption; and calculating the control quantity of the execution mechanism through the model prediction controller, and generating an optimal process parameter combination. According to the implementation of the method, real-time optimization and adjustment of the process parameters for resource utilization of the construction wastes are realized.

The invention discloses a robot hierarchical reinforcement learning variable impedance control method based on vision and touch, and belongs to the field of robot control. According to the method, an upper-layer planning system and a lower-layer variable impedance control system are included, the upper-layer planning system fuses information from a visual sensor and a tactile sensor, feature coding is conducted on a visual image through a variational self-coding model, tactile feedback and mechanical arm state information are combined, and an action strategy adapting to the current environment and corresponding impedance parameters are formulated. And the lower-layer variable impedance control system realizes variable impedance control of the robot based on a depth kinematics and dynamics model, and allows the robot to dynamically adjust the motion and contact force according to the sensed information when interacting with the environment, thereby improving the adaptability to the environment change and the task execution efficiency. According to the method, the success rate and execution efficiency of the robot for executing the complex assembly task in the unstructured environment are remarkably improved, and high robustness is shown.

The invention relates to the technical field of plastic mold parameterized simulation, in particular to a parameterized simulation model design method and system for a plastic mold. The method comprises the following steps: carrying out raw material fine preparation on a target plastic raw material to obtain a homogeneous dry material; performing multi-field rheological characteristic scanning on the homogenized and dried material, and extracting viscosity model parameters to obtain viscosity model initial parameters; precisely measuring thermodynamic characteristics of the plastic material on the homogenized and dried material, and analyzing crystallization kinetic behaviors to obtain crystallization kinetic model parameters; and carrying out material gene spectrum integration according to the initial parameters of the viscosity model and the parameters of the crystallization kinetic model to obtain a material gene spectrum. According to the method, through performance-driven parameter optimization and intelligent mold blueprint generation based on the material physical property and the microenvironment, the mold manufacturing efficiency and quality are remarkably improved, and the manufacturing cost is reduced.

The invention discloses a virtual marshalling multi-train cooperative adaptive sliding mode control method based on an extended state observer, and the method comprises the steps: firstly constructing an urban rail multi-train tracking error dynamic model based on a unit train dynamic equation; secondly, establishing a nonsingular terminal sliding mode surface according to the urban rail multi-train tracking error dynamical model to obtain an equivalent control law, combining the equivalent control law with a switching control law to obtain a nonsingular terminal sliding mode control law, and introducing a gain adaptive law and a saturation function into the nonsingular terminal sliding mode control law to obtain an adaptive nonsingular terminal sliding mode control law; and finally, an extended state observer is adopted to estimate external interference, an observation value of the interference is added to the output of the adaptive nonsingular terminal sliding mode controller to serve as feed-forward compensation, and the adaptive nonsingular terminal sliding mode control method based on the extended state observer is constructed. According to the method, the multi-train cooperative operation tracking control precision and stability can be remarkably improved under the external interference condition.

The invention provides a method and system for predicting the uplift bearing capacity of a mud mixed pile. According to the method, modeling is carried out by integrating geomechanics and monitoring data. The method comprises the following steps: firstly, acquiring geometric parameters of a multi-layer heterogeneous soil layer interface, constructing a database through a mechanical test, and improving the reliability by combining gradient recognition and cooperative verification of pile deformation and load data; dividing longitudinal segmentation units, adjusting anti-slip parameters to generate a space mapping table, establishing a debonding dynamic model containing an interlayer retardation effect, simulating a debonding expansion process, and outputting load evolution data. And judging the maximum load of the critical state based on data convergence analysis, and taking the maximum load as a predicted value of the uplift bearing capacity. According to the technical scheme, through dynamic parameter mapping and debonding process simulation, accurate prediction of the uplift bearing capacity of the pile foundation in a complex soil layer is achieved, and the defect that a traditional model is insufficient in characterization of interlayer heterogeneity and dynamic debonding behaviors is overcome.

The invention discloses a forklift operation control optimization method and system based on a disturbance observation technology, and relates to the technical field of electric forklift control, and the method comprises the steps: collecting the operation data of a forklift operation process in real time, sensing and fusing through multi-source sensing information, analyzing the state variable of forklift movement, and building a dynamic parameter mapping relation; taking the measured value and the predicted value of the forklift all-condition dynamic model as input, constructing a high-order sliding-mode observer, evaluating disturbance source interference, and generating a motion compensation correction amount; a feed-forward-feedback fused composite controller is constructed, a yawing moment pre-compensation instruction is generated based on a disturbance source disturbance evaluation result, and a forklift trajectory tracking error is dynamically corrected; and monitoring the distance between the operation point of the forklift and the instability boundary in real time, and triggering a torque redistribution strategy when the distance is close to the instability boundary. Through deep fusion of real-time sensing data, dynamic modeling and an intelligent control algorithm, the working performance of the forklift under complex working conditions is remarkably improved.

The invention discloses a plastic effect simulation display method and system based on a 3D simulation technology, and relates to the technical field of medical information, and the method comprises the steps: collecting the facial data of a patient, and constructing a multi-level facial structure initial model; constructing a hybrid deformation model; individual characteristic parameters of a patient are extracted, and an individual biomechanical parameter set is generated through the biomechanical parameter prediction network; applying the personalized biomechanical parameter set to the mixed deformation model; operation input parameters of plastic surgery are received, and the deformation result of each layer of structure is calculated and displayed in real time based on a GPU parallel computing framework; constructing a tissue healing and recovery kinetic model, and generating appearance prediction data at different postoperative time points in combination with the deformation result; and performing three-dimensional visualization processing on the appearance prediction data. According to the method, CT / MRI, 3D structured light scanning and expression dynamic video sequences are creatively integrated, the limitation of traditional single-mode data modeling is broken through, and the soft tissue boundary recognition precision is remarkably improved.

The invention provides a maglev train multi-suspension system predictive control method based on event triggering, which comprises the following steps: establishing a multi-electromagnet suspension system continuous dynamic model based on electromagnet parameters of a maglev train; in combination with the input time delay of the multi-electromagnet suspension system, the continuous dynamic model of the multi-electromagnet suspension system is sequentially processed based on a fourth-order Runge-Kutta method, and a discrete coupling time delay dynamic model of the multi-electromagnet suspension system is obtained; establishing a model prediction controller of the multi-electromagnet suspension system by combining the discrete coupling time delay dynamical model of the multi-electromagnet suspension system and the optimization problem; and updating a control signal of the model prediction controller according to a preset composite event triggering mechanism to obtain an initial control quantity, designing a time delay compensation strategy to carry out input time delay compensation on the initial control quantity, and obtaining a standard control quantity to control the multi-suspension system of the maglev train. According to the method, the system energy consumption is remarkably reduced while the good operation performance of the suspension system in a complex environment is guaranteed.

The invention discloses a walking method of a biped robot in a complex terrain, which belongs to the technical field of robot walking and comprises the following steps: step 1, establishing a preliminary kinetic model of walking of the biped robot, and optimizing to obtain an improved kinetic model; 2, generating a reference state track of the biped robot according to the input information; step 3, optimizing the reference trajectory; 4, further optimizing and distributing the contact force, calculating the joint torque of the walking of the biped robot, and sending the joint torque to each joint motor; 5, the position, speed and posture of the biped robot are estimated in real time according to the data and fed back to a track planning and whole body control module, and planning and control of the next period are carried out; and 6, the steps 2-5 are continuously repeated, so that the biped robot can walk under the complex terrain. The adaptability and the energy efficiency of the robot under the complex terrain can be improved.

The invention relates to the technical field of mechanical arm control, and particularly discloses a multi-axis motion control method based on a six-axis hydraulic mechanical arm, and the method comprises the following steps: establishing a kinetic model and a hydraulic system model of the six-axis hydraulic mechanical arm, generating a motion track in a Cartesian space or a joint space based on a preset task demand, and generating a multi-axis motion model of the six-axis hydraulic mechanical arm; the track smoothness is optimized through an S-type acceleration and deceleration algorithm; a deviation coupling synchronous control strategy is adopted, and position, speed and pressure signals of all hydraulic cylinders are collected in real time. According to the multi-axis motion control method based on the six-axis hydraulic mechanical arm, through multi-layer modeling, multi-algorithm fusion and intelligent optimization, the core problems of synchronous errors, non-linear interference, high energy consumption and the like in multi-axis cooperative control of the hydraulic mechanical arm are solved, high-precision, high-robustness and low-energy-consumption industrial-grade motion control is achieved, and the multi-axis motion control method is suitable for industrial production. The method is suitable for heavy-load carrying, precision assembling and other complex scenes.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.

The invention discloses an unmanned ship obstacle avoidance path planning method and system based on global optimum, and belongs to the field of unmanned ship navigation obstacle avoidance. Comprising the steps that S1, a kinetic model of the unmanned ship is constructed, a motion coordinate system is defined, and the unmanned ship has corresponding speeds in the surging direction, the swaying direction and the yawing direction; s2, improving and simplifying the dynamic model of the unmanned ship through the influence of the water environment influence factors on the dynamic characteristics of the unmanned ship; s3, formulating a collision avoidance strategy based on predictive control, performing discretization processing, calculating parameters encountering other ships and a collision risk, and determining a target function in the collision avoidance strategy; and S4, determining a final collision avoidance path method, and searching a path global optimal solution of the unmanned ship through a loop iteration mode. According to the technical scheme provided by the invention, the problem that unmanned ship path planning and obstacle avoidance cannot be accurately and efficiently carried out in the prior art is effectively solved.