851 results about "Mechanics model" patented technology

The invention discloses a cluster computing power energy efficiency perception scheduling and green computing system, which relates to the technical field of computers and comprises a multi-source energy efficiency perception and data acquisition module used for acquiring power consumption, utilization rate, temperature, cooling state, PUE index and environmental data of cluster nodes. According to the invention, through the multi-modal energy efficiency fusion sensing network and the multi-scale convolution and time sequence attention fusion network, multi-source heterogeneous energy efficiency data such as current, voltage, temperature, airflow and the like of a node level can be collected and fused in real time and with high precision, noise is effectively removed, abnormity self-correction is realized, the defect of energy efficiency sensing granularity in the prior art is made up, and the energy efficiency sensing precision is improved. And reliable input is provided for subsequent scheduling decisions. A cross-scale dynamic twinborn collaborative modeling mechanism is adopted, a physical information neural network and a computational fluid mechanics model are coupled, optimization is carried out through a generative adversarial network structure, and accurate prediction of a complex energy consumption evolution curve and a cooling flow field is achieved.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention relates to the field of constructional engineering, in particular to a selection method for a hoisting system of a steel box girder and related products, and the selection method comprises the steps that a parameterized mechanical model is established according to parameters of the hoisting system, and hoisting point positions are variable parameters; the hoisting system comprises a steel box girder and a sling; based on the parameterized mechanical model, carrying out stress analysis in the hoisting process, and calculating the stress conditions of the sling and the steel box girder; by taking minimization of the maximum stress of the sling, the maximum stress of the steel box girder or the maximum deformation in the hoisting process as an optimization target, determining an optimal hoisting point position and matched sling parameters by utilizing an optimization algorithm; according to the method, by establishing the parameterized mechanical model of the hoisting system and carrying out nonlinear finite element analysis in the hoisting process, the sling stress, the steel box girder stress and the system deformation under different hoisting point positions and sling parameter combinations can be accurately calculated.

The invention provides a mechanical arm predefined time trajectory tracking control method and system based on time delay estimation, and relates to the technical field of multi-degree-of-freedom mechanical arm trajectory tracking control. Constructing a predefined time error conversion function to define a system error and conversion error system, designing a barrier function and a feedback control rate based on the conversion error system, and designing a Lyapunov function to prove the stability of the system so as to track an expected trajectory; according to the method, time delay information is deeply fused, dependence on a complex model approximate structure is not needed, stable convergence of the mechanical arm system state within predefined time can be achieved when a mathematical model is not accurate, the system control performance and robustness are effectively improved, and the requirement of modern industry for mechanical arm high-performance control is met.

The invention discloses a robot arm control method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a pre-training dynamic model and a real visual depth map of a real robot arm visual angle, and the pre-training dynamic model is used for completing a specified task; determining a joint control value according to the real vision depth map and a pre-training kinetic model; hybrid action control is carried out based on the joint control value, and remote target navigation is carried out on the response action of the real robot arm through a visual navigation model; and the controlled simulation target image and the actual image are obtained, feature matching and closed-loop estimation are carried out based on the simulation target image and the actual image, and pose error compensation is carried out on the action of the real robot arm. The motion is observed and deduced through a real visual depth map, then real and simulated mixed motion control is carried out to reduce a visual and dynamic gap, and finally pose error compensation is carried out. And the pose error of the arm is reduced, and a start pose guarantee is provided for downstream control.

The invention belongs to the technical field of vehicle steering control, and relates to a van semitrailer steering synchronization angle closed-loop test calibration system, which comprises the following steps of: firstly, executing a static steering test on a no-load combined vehicle, and constructing a mapping reference table of a saddle space position and an ideal hinge angle; then multi-working-condition dynamic steering testing is carried out on the load combination vehicle, vehicle frame deformation data are collected in real time, the deformation data are converted into a hinge angle compensation value through a pre-trained deformation mechanical model, selective correction is carried out on the actually-measured hinge angle based on a deformation compensation starting threshold value, a calibration target is generated to reversely analyze the target saddle position, and the target saddle position is corrected; the problem that in the prior art, inevitable elastic structure deformation and real mechanical steering asynchronous faults of the van semitrailer under the actual dynamic load working condition cannot be fully considered and distinguished is effectively solved, and then multi-working-condition driving verification data are collected, deformation mechanical model parameters are iteratively updated, and a compensation threshold value generation strategy is generated. And closed-loop optimization is realized.

The invention provides a classroom simulation method for virtual reality, and relates to the technical field of data processing, and the method comprises the steps: obtaining a high-dimensional data matrix with a time-space correlation characteristic; constructing an electrical connection relation graph; obtaining a quantum state evolution result of the transient process of the power system; calculating to obtain a multi-physical field distribution parameter corresponding to the equipment; based on the multi-physics field distribution parameters, combining molecular dynamics simulation and a fluid mechanics model to obtain arc dynamic characteristic data; collecting an electromyographic signal of an operator, and generating a force feedback control instruction through a neural network mapping model in combination with mechanical parameters of equipment; and according to the eye movement tracking data, the arc dynamic characteristic data and the force feedback control instruction, generating a virtual reality scene adaptive to the visual response and the operation state of the operator by using a dynamic rendering engine. According to the embodiment of the invention, the authenticity and interactivity of virtual reality classroom simulation can be improved.

The invention relates to the technical field of modeling, in particular to an aero-engine main bearing kinetic model correction method based on sensitive feature fusion, which comprises the following steps: constructing an aero-engine main bearing kinetic model; simulation data are generated according to the collected actual measurement data, time-frequency domain features of the actual measurement data and the simulation data are extracted, and an initial feature set is constructed; based on a binary improved horse swarm optimization algorithm, sensitive features are screened out from the initial feature set, a sensitive feature set is constructed, a component matrix coefficient of the sensitive feature set is calculated in combination with a principal component analysis method, and a fused sensitive feature expression is obtained; calculating fusion sensitive features of the measured data and the simulation data by using the fusion feature expression, and taking a difference calculation formula of the two as a target function for model correction; and performing optimization based on a horse group optimization algorithm and the target function, updating model parameters of the aeroengine main bearing dynamic model, and realizing correction of the aeroengine main bearing dynamic model.

The invention belongs to the technical field of intelligent control, and provides a double-fan parallel operation control system and method based on dynamic flow balance, and the system comprises a flow guide assembly, an electric control valve, a flow sensor and a control assembly; the flow sensors are used for collecting air flow data at the inlets and outlets of the corresponding fan branches and sending the air flow data to the control assembly; the control assembly is used for inputting the air flow data into the three-dimensional dynamic fluid mechanics model to obtain a flow deviation value between the current air flow and the target air flow, inputting the flow deviation value into the control decision model to obtain a control instruction, and issuing the control instruction to the flow guide assembly and / or the electric adjusting valve. And the flow guide direction of the flow guide assembly and / or the opening degree of the electric control valve are / is controlled. According to the scheme, the three-dimensional dynamic fluid mechanics model and the control decision model can be combined, the accurate control instruction is obtained, and the accuracy, reliability and convenience of the double-fan parallel operation control link are improved.

Provided are a planning and control method for a legged robot, an apparatus, a robot, and a storage medium. The method includes: determining, based on the movement state and the swing parameter of each leg at the next moment, a foot-end reference state of each leg, and planning, based on a foot-end dynamic model and a discrete collision model, an impact-aware swing leg trajectory for a leg that starts to swing at the next moment; and performing, based on the movement state, the reference state sequence, and the foot-end reference state, whole-body control on the legged robot, and performing, based on a whole-body dynamic model and the discrete collision model, impact-aware whole-body control for a leg that is a supporting leg in planning but does not touch the ground.

The invention discloses a double-foot wheel-legged robot pose control method and device based on hierarchical cooperative control, a medium and a product, and relates to the field of robot control, and the method comprises the steps that a hierarchical cooperative control framework is constructed; the layered cooperative control framework comprises a balance controller and a whole body attitude controller; the balance controller determines a reference wheel joint driving torque based on a simplified inverted pendulum dynamic model according to the pose information of the upper mass center of the biped wheel-leg robot; the whole-body attitude controller performs optimization control on all joints of the biped wheel-leg robot by adopting an incremental model prediction control algorithm combined with a reference wheel joint driving moment based on a whole-body dynamic model; state information of the biped wheel-legged robot is obtained; according to the state information, a driving torque instruction of each joint is obtained based on a hierarchical cooperative control framework; and driving the double-foot wheel-leg robot to move according to the driving torque instruction of each joint. The precision, robustness and real-time performance of robot motion control can be improved.

The invention relates to an indoor airship-oriented finite time trajectory tracking control system, platform and method, the system comprises an airship dynamic model, a disturbance estimation module, a sliding mode filter and a self-adaptive controller, the airship dynamic model is formed by constructing a motion stipulation inertial coordinate system and an airship body coordinate system of an airship; the disturbance estimation module estimates unknown disturbance based on a double-layer neural network according to an airship dynamic model and updates the network in real time; the sliding mode filter estimates the influence of noise on the motion speed of the airship according to the unknown disturbance condition of the airship and updates the network in real time; the adaptive controller adjusts the virtual control input of the airship motion trail by controlling the convergence condition of the tracking error within the finite time; according to the method, the disturbance problem caused by uncertainty and dead zones in the airship and the deviation problem of speed signal estimation caused by noise are solved.

According to the mechanical arm control method and system based on the self-adaptive force field, the motion state of a patient is monitored, the motion state is compared with a demonstration space trajectory, and training performance information of the patient is obtained. Based on the training performance information and the constructed adaptive force field model, auxiliary force applied to the patient is obtained through calculation, wherein the auxiliary force comprises normal force, tangential force and viscous force. And the applied auxiliary torque is calculated based on the tail end posture of the mechanical arm and the target posture, the joint control torque is obtained according to the auxiliary force and the auxiliary torque, and the dynamic torque and the null space control torque of the robot dynamic model are obtained. And the joint control torque, the dynamic torque and the null space control torque are combined to obtain the comprehensive control torque for controlling the mechanical arm. Therefore, the self-adaptive adjustment of the auxiliary force is realized based on the position, speed and direction of the patient, and the time degree of freedom can be obtained in the motion of the self-adaptive force field, so that the patient obtains more freedom of active motion under the assistance of the space force field.

The invention provides a configuration optimization method before capturing of a space double-arm robot, and the method comprises the steps: constructing a kinematics and dynamics model of the space double-arm robot according to a three-dimensional model of the space double-arm robot; constructing a kinetic model of the to-be-captured target by using the motion parameters of the to-be-captured target in the space; according to the dynamic model of the space double-arm robot, the relative mass of the space double-arm robot under non-centering collision is obtained, and a maximum pre-contact collision force performance evaluation index is constructed; further constructing a double-arm closed chain inertia matching index performance evaluation index; constructing a configuration optimization model before the space double-arm robot captures the target; and through a multi-target optimization algorithm, the optimal configuration of the space double-arm robot before capturing the target is solved. The pre-contact collision force peak value of the double-arm robot during target capturing can be effectively reduced, and meanwhile the operability of a closed chain system formed after capturing is guaranteed.

The invention provides a wind field inversion method and device based on multi-source prior data, equipment and a medium, and the method comprises the steps: building a target function of a three-dimensional fusion wind field based on the multi-source prior data and a fluid mechanics model simulation wind field, carrying out the iterative optimization of the target function, and determining the three-dimensional fusion wind field; constructing an initial deep learning neural network model, taking the three-dimensional fusion wind field as a truth value label, inputting the urban underlying surface features, the terrain elevation and the numerical weather forecast wind field into the deep learning neural network model, and training to obtain a target deep learning neural network model; and inputting the new numerical weather forecast wind field, the urban underlying surface features and the terrain elevation into the target deep learning neural network model, and outputting a refined wind field. According to the technical scheme provided by the embodiment of the invention, through the trained deep learning neural network model, high-precision rapid inversion of the low-altitude wind field is realized without depending on laser radar and ground observation data and depending on prior information such as numerical prediction and terrain.

The invention provides a hub variable pitch bearing bolt joint optimization method, system and device and a medium, and belongs to the technical field of bearings. A mechanical model and a dynamic coupling relation of a wind wheel system are constructed; establishing a multi-objective optimization function, and setting constraint conditions; based on an adaptive variation strategy optimization algorithm, setting a multi-stage search strategy, and defining a multi-layer constraint fitness function to embed a multi-objective optimization function and constraint conditions into an algorithm solving framework in combination with global search and local optimization; the improved spider bee optimization algorithm is applied to an operation characteristic model of the double-drive variable pitch system, a hub parameter set is generated, and a local stress concentration area occurring in the optimization process is recognized and avoided; performing dynamic simulation verification on an optimization result; and outputting the design scheme after the simulation verification is passed. According to the invention, a combined optimization mode based on an intelligent algorithm of a wind wheel key component and a multi-objective optimization mechanism is provided, and the balance of the design performance of the overall structure of the hub and the engineering applicability are improved.

The invention discloses a wheel-foot humanoid robot operation control method and system based on maneuverability optimization, and relates to the technical field of robot intelligent control. According to a mechanical arm tail end trajectory tracking task and mechanical arm joint motion constraints, a weight matrix is calculated in a self-adaptive mode through state vectors of a base and a mechanical arm joint; a robot dynamic model is constructed, and the tail end contact force of the mechanical arm is estimated based on a disturbance observer; the direction of the control force is determined according to an estimation result, and then force maneuverability enhancement is carried out; a speed instruction of an end effector is obtained, the expected base speed and the expected mechanical arm joint speed are obtained through a weight matrix, a hierarchical control frame of a model prediction controller and a whole-body controller is constructed based on an integrated dynamic model, the optimal wheel-leg interaction force spinor and the optimal joint torque are obtained through solving, and the optimal wheel-leg interaction force spinor and the optimal joint torque are obtained. Therefore, the whole-body motion control of the robot is driven. Through organic combination of disturbance compensation and force maneuverability optimization, the motion stability and operation efficiency of the robot are enhanced.

The invention discloses a robot Cartesian force damping control method and system based on a process energy mechanism, and the method comprises the steps: building a robot kinetic model, constructing an impedance control law and a force control law of a Cartesian space, and achieving the dynamic adjustment of control energy through combining a process energy container mechanism, global passivity and stability of the system in contact and non-contact states are maintained; the invention relates to the technical field of robot intelligent control and man-machine interaction. According to the robot Cartesian force damping control method and system based on the process energy mechanism, high-precision, safe and stable force control and damping adjustment can be achieved in the complex processes of polishing, assembling, wiping and the like, and a universal scheme with the energy self-management capacity is provided for safe man-machine cooperation.

The invention discloses a heavy commercial vehicle chassis virtual load spectrum efficient twin system, and relates to the technical field of automobile chassis. The method specifically comprises the following steps: S1, a load spectrum twinning method based on a virtual iteration method; comprising the following steps: S11, constructing a whole vehicle multi-body dynamic model; s2, an AM-P-sLSTM model is constructed; and S3, a load spectrum twinning method based on the AM-P-eLSTM model is carried out. According to the method, refined construction of the whole vehicle MBD model is achieved, the high-precision rigid-flexible coupling vehicle frame multi-body dynamic model with the flexible body as the main body is established mainly through the vehicle mass center, the rotational inertia and actual measurement data of chassis suspension connecting pieces, and a basis is provided for subsequent virtual iteration and load decomposition; by iterating the road surfaces with different characteristics, the time domain and relative damage error of the iteration signal and the target signal under each road surface are small, the iteration precision is high, and the requirements of subsequent virtual load spectrum twinning and fatigue analysis are met.

The invention discloses an aggregate random distribution concrete PFC (Power Factor Correction) simulation method, which comprises the following steps: a model selection step: determining a parallel bonding model as a mechanical model for concrete material simulation; a cement mortar matrix generation step of generating a cement mortar matrix by using particles in a set size range in a set space range based on the model, and setting pore density, particle density and contact model parameters; a coarse aggregate random distribution simulation step: randomly covering and grouping original particles in the matrix in a grouping and range mode; a concrete numerical model construction step: determining particle classification so as to distinguish cement mortar and coarse aggregate; and a model reliability verification step: comparing indoor test results through a uniaxial compression test. According to the method, the simulation accuracy is improved, the randomness of aggregate is reduced, the model reliability is ensured, the matrix simulation refinement degree is improved, and a method is provided for concrete modeling and performance research.

The invention discloses a collaborative robot joint external force estimation method, and relates to the technical field of collaborative robot joint external force estimation, and the method comprises the steps: building a joint kinetic model, and obtaining the basic kinetic parameters of a collaborative robot joint; the angular displacement deviation between the input shaft and the output shaft of the speed reducer is obtained, and the compensated torsional angle difference is calculated according to the angular displacement deviation; a joint variable stiffness model is established, multiple sets of calibration data are obtained through experiments, and parameters in the joint variable stiffness model are identified according to the multiple sets of calibration data; based on the basic dynamic model and the joint variable stiffness model after parameter identification, a nonlinear disturbance observer is constructed in combination with introduced auxiliary variables; and system disturbance is observed and compensated through a nonlinear disturbance observer, so that an external torque estimated value acting on the tail end of the joint is calculated and output in real time. According to the method, high-precision and real-time estimation of the external torque can be effectively considered, and meanwhile, the system hardware cost and the structure complexity are greatly reduced.

The invention discloses a fracture prediction method based on well-to-seismic joint inversion and heterogeneous mechanical modeling, and the method comprises the following steps: collecting and preprocessing basic geological data of a target block; building a high-resolution reservoir rock physical model; establishing a three-dimensional geologic model of the heterogeneous mechanical body; carrying out fracture sensitivity prediction and spatial distribution modeling; and crack network three-dimensional visualization and development decision support. According to the method, well-to-seismic joint inversion, heterogeneous geomechanical modeling and artificial intelligence prediction technologies are deeply fused, and a systematic, intelligent and refined crack prediction solution is formed. Compared with a traditional method, the method has remarkable advantages in the aspects of crack space identification precision, multi-field coupling analysis capability and intelligent level, and provides scientific basis and technical support for efficient development of oil and gas reservoirs.

The invention discloses a direct coal liquefaction pump thermal wear prediction method considering a high temperature effect, and belongs to the technical field of fluid machinery and reliability engineering.The method comprises the steps that the high temperature effect is considered, a liquid phase control equation and a solid particle dynamic model are subjected to coupling iteration, and a solid-liquid two-phase flow numerical model is obtained; the method comprises the following steps: performing full flow field modeling on a fluid domain formed by flow passage components of a pump for direct coal liquefaction, and performing grid division on the fluid domain; constructing a heat-flow-solid multi-field coupling calculation system, and solving the solid-liquid two-phase flow numerical model by adopting a large eddy simulation method based on temperature correction and a discrete element method coupling method to obtain information of flow fields and particle fields at all positions in the pump; operation condition parameters are obtained, the thermal wear rate is obtained through a thermal wear rate calculation formula on the basis of flow field and particle field information of all positions in the pump, and the predicted wear amount of the flow passage component is obtained; and the prediction precision and applicability of the wear model are improved.

The invention relates to the technical field of dynamic simulation teaching, in particular to a dynamic simulation teaching system for urban and rural space planning, which comprises a data acquisition unit, an enhanced analysis unit and a multi-modal feedback processing unit. The method comprises the following steps: calculating the load deformation quantity of a component through a finite element algorithm, generating a ventilation efficiency cloud picture and a shadow coverage rate curve in combination with a computational fluid mechanics model and a ray tracing algorithm, analyzing a user operation instruction to trigger stress over-limit early warning, and realizing force feedback and multi-physics field data visualization through VR equipment. The problems of insufficient multi-source data fusion and lack of physical authenticity in traditional teaching are solved, dynamic physical verification and interactive teaching of a planning scheme are realized, and the planning decision ability and engineering practice cognition of students are improved.

The invention discloses an excavator operation action control method and device based on bucket trajectory planning, and belongs to the technical field of engineering machinery automation. The method comprises the steps that excavator attitude sensing data and excavator environment sensing data are obtained; the excavator attitude sensing data comprises all joint angles of the excavator; according to the excavator attitude sensing data, obtaining the position and attitude of the bucket and constructing a dynamic model; the dynamic model represents a mapping relation between the bucket stress information and the bucket force output constraint information; acquiring a three-dimensional construction model according to the excavator environment sensing data; acquiring a three-dimensional deviation field according to the three-dimensional construction model and a preset building information model; planning a target construction track of the bucket according to the position and posture of the bucket, the three-dimensional deviation field and the dynamic model; and the target pose sequence of the joint space of the excavator is obtained based on the target construction track of the bucket, action control is conducted on all joints of the excavator according to the target pose sequence, and the high-precision excavator control method capable of achieving real-time adjustment according to the operation quality is achieved.

The invention discloses a dynamic behavior generation method, system and device for a humanoid robot and a medium, and the method comprises the steps: carrying out the dynamic modeling processing of the multi-joint dynamic characteristics of the humanoid robot, and obtaining a multi-joint dynamic model; the robot state of the humanoid robot, the operation state of an operator operating the humanoid robot and environment task information are obtained, and a state parameter set is constructed by fusing the multi-joint dynamic model; a multi-dimensional reward function of the humanoid robot is constructed according to the multi-joint kinetic model; performing strategy optimization processing on the humanoid robot according to the multi-joint kinetic model and the multi-dimensional reward function, and outputting to obtain a dynamic game action conforming to a physical rule; and performing motion control on joints of the humanoid robot according to the dynamic game action. The working efficiency of the humanoid robot can be improved, and the humanoid robot can be widely applied to the technical field of robots.

The invention relates to a mechanical arm self-adaption compliance control method and system based on model reference self-adaption and impedance parameter online identification, and belongs to the field of robots. The method comprises the steps that a mechanical arm task space dynamics model is established; designing a reference model; a recursive least square method is adopted, and an equivalent stiffness matrix and a damping matrix of the interaction environment are identified online; designing a composite adaptive law; the expected rigidity of the reference model is dynamically adjusted according to the environment rigidity obtained through identification; and according to sensor data collected in real time, the mechanical arm task space dynamics model, the robot parameter self-adaption law, the environment rigidity, the environment damping and the environment parameter identification robust item, control force is generated, and the mechanical arm is driven through the control force. According to the method and the system, the environment characteristics can be sensed online, and the compliant behaviors can be adaptively adjusted, so that safe, efficient and high-robustness interaction of the mechanical arm in a complex and unknown environment is realized.

The invention discloses a mechanical arm excitation trajectory optimization-based kinetic parameter identification method, which comprises the following steps of: constructing a corresponding Newton-Euler kinetic model based on a target mechanical arm, and obtaining a linear kinetic model through linear transformation and parameter recombination; based on the linear dynamics model, a staged periodic Fourier series excitation track based on all joint positions of the target mechanical arm is constructed; a quantum behavior and Levy flight hybrid particle swarm optimization algorithm is adopted to optimize the excitation trajectory, and constraint conditions of a regression matrix in the linear dynamic model are obtained; and based on the constraint conditions, adopting an improved hyperbolic sine and cosine optimization algorithm, and identifying the minimum parameter set and the joint torque of the linear dynamics model. According to the method, continuous excitation of the kinetic parameters in robot system recognition is achieved by improving the meta-heuristic algorithm, the decoupling problem of the kinetic parameters is solved, and therefore the precision of the mechanical arm kinetic model is improved.

The invention relates to the technical field of automobiles, in particular to a four-wheel steering vehicle track control method based on model predictive control. Firstly, a rear wheel proportionality coefficient changing along with the longitudinal speed of the vehicle is constructed; expressing a rear wheel steering angle as a function of a front wheel steering angle based on the proportionality coefficient, and substituting the function into a traditional three-degree-of-freedom four-wheel steering model to construct a simplified three-degree-of-freedom dynamic model with lower control dimension; performing linearization and discretization processing on the simplified model, and constructing an MPC prediction model; constructing a control objective function and related constraint conditions; and performing rolling optimization solution on the control target function to obtain an output rotation angle control instruction. According to the method, the complexity of a control problem is effectively reduced through model simplification, and the real-time performance and stability of a control system can be remarkably improved while the vehicle trajectory tracking precision is ensured.

The invention relates to the technical field of mechanical arm modeling, and particularly discloses a picking mechanical arm coupling dynamics modeling method and device, a terminal and a readable storage medium. The method comprises the steps that the structural composition of the picking mechanical arm is obtained, and a structural model is established; constructing a connecting rod coordinate system of adjacent joints based on an improved D-H parameter method; establishing forward and inverse kinematics models based on the connecting rod coordinate system; parameters of flexible elements in the mechanical arm are collected, and a joint flexible connection model and an elastic dynamic model are constructed; and further introducing a coupling constraint condition, and forming a coupling dynamics modeling framework by combining the elastic dynamics model and the overall dynamics model. According to the model, joint flexibility, structural vibration and dynamic response nonlinear characteristics can be considered, the modeling precision and control robustness of the picking mechanical arm under complex working conditions are improved, and the model is suitable for modeling and control algorithm design of the mechanical arm of a flexible structure.