17 results about "Lagrangian dynamics" patented technology

The invention discloses a real-time estimation and optimization analysis method for the joint torque of an operation mechanical arm. The method comprises the following steps that S1, joint position data and torque data of the mechanical arm are collected; s2, a kinetic model and a torque calculation model of the mechanical arm are constructed based on a Lagrange kinetic equation, and a system error equation is obtained by combining the kinetic model and the torque calculation model; s3, constructing a fuzzy neural network model based on a fuzzy control rule and a neural network, and estimating and compensating the moment of force of the mechanical arm by taking the joint position error and the speed error of the mechanical arm as input; s4, a mechanical arm self-adaptive control strategy is constructed based on the torque calculation model and the fuzzy neural network model, and mechanical arm joint torque fluctuation is optimized; according to the method, the fuzzy neural network model is constructed on the basis of the mechanical arm dynamic model, the uncertain part of the mechanical arm is optimized and compensated, and high-precision control and stable operation of the mechanical arm are achieved.

The application discloses a wheeled armored vehicle dynamics simulation platform and a construction method, equipment and medium thereof, relates to the field of wheeled armored vehicle simulation, and comprises the following steps: based on a Lagrange dynamics method, a dynamics model of a wheeled armored vehicle is constructed; the dynamics model of the wheeled armored vehicle comprises a sprung mass dynamics model, an unsprung mass dynamics model and a turret system dynamics model; and the dynamics model of the wheeled armored vehicle is iteratively simulated and solved to obtain a simulation result of the wheeled armored vehicle. The application improves the refinement degree of the construction of the dynamics model of the wheeled armored vehicle.

The invention discloses a time sequence damaged fine-grained action recognition method based on skeleton decomposition and sequence completion, and the method comprises the steps: recovering a missing frame in a time sequence damaged skeleton sequence through a context learning module by employing a prompt pair in a prior skeleton database and a query pair of a to-be-repaired sequence, and generating a basically complete skeleton sequence; dividing the articulation points into a plurality of semantic regions according to a human body structure, dividing the semantic regions into dynamic and static regions according to the motion intensity of each region, respectively applying strong and weak different space-time perturbation and recombination, and constructing an enhanced dynamic sequence and an enhanced static sequence; based on the Lagrange dynamics principle, an acceleration sequence is obtained through MLP modeling and fusion; and finally, performing action classification on the cross fusion features through a graph convolutional network. According to the method, the robustness of the model under the condition that the time sequence is incomplete is effectively improved, the distinguishing capability on subtle action differences is enhanced, and the recognition accuracy superior to that of an existing method is obtained on a public data set.

The application discloses a kind of simulation dinosaur three-dimensional modeling optimization method and system, method constructs rigid body skeleton system based on Lagrange dynamics;According to the muscle layer of super-elastic material of anatomical attachment point, the displacement field is calculated using position-based dynamics method;Skin layer is constructed outside, Cauchy stress is projected to grid vertex, and wrinkle and scale texture are dynamically generated according to principal strain;The grid simplification degree and physical simulation accuracy of corresponding area are dynamically adjusted by calculating perceptual importance weight;Establish the quadratic programming problem with anatomical constraint to realize motion reorientation and angular momentum distribution;And construct deformation prediction cache model to carry out online constraint correction.The application realizes the unity of high-precision biological feature restoration and high-efficiency real-time rendering.

The application discloses a kind of based on skeleton decomposition and sequence completion's time sequence damaged fine-grained action recognition method, through context learning module, the query pair of hint in priori skeleton database is used to the sequence to be repaired, restore missing frame in time sequence damaged skeleton sequence, generate substantially complete skeleton sequence;According to human structure, joint point is divided into multiple semantic regions, and according to the motion intensity of each region, it is divided into dynamic and static region, respectively, different spatiotemporal disturbance and reorganization are applied to strong, weak, and enhanced dynamic sequence and static sequence are constructed;Based on Lagrange dynamics principle, acceleration sequence is obtained by MLP modeling and fusion;Finally, cross fusion features are completed by graph convolution network Action classification.The application effectively improves the robustness of the model under the condition of incomplete time sequence, enhances the discriminant ability to subtle action difference, and achieves higher recognition accuracy than existing methods on public data sets.

A pneumatic humanoid dexterous hand collaborative planning and robust control method based on digital twinning belongs to the technical field of robot dexterous hand control and pneumatic driving, and comprises the following steps: establishing a digital twinning model and realizing virtual-real bidirectional mapping; constructing a task target and a constraint set, and obtaining a gesture and a capture intention; generating an initial reference trajectory by adopting a multi-quadratic radial basis function interpolation, and performing constraint optimization by taking the initial reference trajectory as a priori and combining full multiple targeting differential dynamic programming and an augmented Lagrange method to obtain a five-finger collaborative trajectory meeting joint boundary and task constraints; establishing a finger Lagrange dynamics and tendon rope transmission model; designing a nonlinear extended state observer to estimate coupling online; and constructing self-adaptive backstepping super-torsion sliding mode control and introducing disturbance compensation to realize high-precision robust tracking. According to the method, the planning feasibility is improved through assistance of the twin environment, and the precision and robustness of the dexterous hand in the grabbing process are remarkably enhanced.

The present application belongs to the technical field of mechanical arm polishing control, and provides a mechanical arm polishing control method and system based on adaptive impedance control, comprising establishing a Lagrange dynamics model of a mechanical arm; obtaining a desired trajectory of the end of the mechanical arm; based on the dynamics model, generating an adaptive impedance control method containing polishing workpiece environment uncertainty as a constraint; using the adaptive impedance control method, estimating unknown environment parameters, updating the reference trajectory in real time, adjusting the end position of the mechanical arm, and realizing the tracking of the end contact force. The present application solves the problem that it is difficult to complete the polishing task with high quality in the actual complex polishing process.

A kind of mechanical arm rigid-flexible coupling dynamics parameter identification method based on pattern search and swarm intelligence optimization, it contains: the establishment includes dissipative force rigid-flexible coupling multi-degree-of-freedom robot Lagrange dynamics model;Solve the minimum parameter set of dynamics, and obtain the observable matrix by reorganization through linear relationship, obtain the identifiable robot linearized dynamics model;Based on finite fourier series and quintic polynomial, establish dynamics parameter identification excitation trajectory model;With the condition number of observable matrix and the combination of singular value as the objective function of excitation trajectory optimization;The final excitation trajectory best parameter is obtained by iterative optimization using pattern search algorithm and swarm intelligence optimization algorithm;The state parameters generated by the best excitation trajectory operation are identified by least square method, and the dynamics parameters of the robot are obtained.The invention improves the search ability in the global feasible region and the late local search convergence ability, improves the anti-interference ability to measurement signal noise, and improves the accuracy of parameter identification.

The application belongs to the technical field of robot motion control, and particularly relates to a multi-joint robot arm adaptive tracking control method based on a cross-task learning driven deep neural network and a control system thereof; the method first constructs a Lagrange dynamics model of the multi-joint robot arm; a position and velocity tracking error is constructed based on a backstepping method framework. Secondly, a double-layer deep neural network composed of a tracking layer and an auxiliary learning layer is built; a gradient statistical matrix is constructed relying on multi-task errors and network mapping features, a neuron-level learning rate diagonal matrix is generated, and the double-layer network weight is updated; then, a control torque is calculated in combination with the tracking error, the network output and the weight difference of the two layers; finally, the closed-loop stability is analyzed through a composite Lyapunov function. The application can retain cross-task dynamic knowledge, shorten the convergence period of new task parameters, balance transient response and steady-state accuracy, guarantee the boundedness of the closed-loop signal, and improve the system operation stability and robustness.

The present application relates to the technical field of motion control of underactuated crane system, and particularly relates to a crane enhanced coupling positioning and sway control method and system for a distributed mass load. Based on Lagrange dynamics equation, a nonlinear model of a six-degree-of-freedom bridge crane with distributed load is established, and the dynamic characteristics thereof are analyzed; based on the nonlinear model of the bridge crane, an energy function of the model is established, a new coupling signal is constructed based on displacement signals of the model, and is used to enhance dynamic coupling among the trolley, the hook and the load; a new state vector is constructed based on the constructed coupling signal, the new state vector is substituted into the energy function of the model, and a new quasi-energy function is obtained; and a nonlinear coupling controller with sway angle information is established according to the proposed new quasi-energy function; in the controller design, linearization processing of the dynamics equation of the crane system is not required, and finally efficient trajectory tracking positioning and load sway suppression are realized.

The application discloses a kind of spacecraft ground microgravity simulation platform similarity analysis method, belong to space technology technical field.The following steps are included: the space dynamics model of spacecraft ground microgravity semi-physical simulation platform is established and the space dynamics model is rewritten to Lagrange dynamics, and the rewritten simulation platform is obtained;Similarity analysis is carried out on spacecraft ground microgravity semi-physical simulation platform and the rewritten simulation platform based on π theorem, and the space satellite platform parameters involved are obtained to determine the design parameters of the rewritten simulation platform;Space attitude scanning trajectory and attitude scanning PID control law are designed for the space satellite platform involved, and the space attitude scanning PID control law is obtained;Similarity analysis is carried out on space attitude scanning PID control law based on π theorem, and the ground microgravity simulation platform attitude scanning PID control law parameter is designed, to obtain ground space attitude scanning PID control law.The dynamics and control effect of the platform can be consistent with the effect of space actual satellite.

The invention discloses a high-precision automatic docking control method and system for an airport scene boarding bridge, and relates to the field of automatic control of airport ground equipment. Aiming at the interference problems of flexible vibration and the like caused by variable structure inertia characteristics, nonlinear dynamic coupling and a composite environment long cantilever structure in the docking process of the boarding bridge, the method comprises the following steps: firstly, establishing a three-degree-of-freedom Euler-Lagrange dynamic model considering a telescopic coupling effect; secondly, constructing a data guide learning control framework, designing a smooth soft projection operator to process physical parameter constraints, and updating a physical parameter estimation value, a repetitive interference estimation value and a non-repetitive interference boundary on line by utilizing iterative domain process data; and finally, designing a control law in combination with an instruction filtering backstepping method, and introducing a robust damping term to suppress random interference.

The application discloses a sea ice movement prediction method based on fusion of physical information and historical track, comprising the following steps: obtaining and arranging historical observation data of floating ice, pre-processing the historical observation data to form floating ice track segments sorted by time; constructing floating ice state variables including historical window position sequence, speed sequence, scale, shape, time characteristics and deep neural network coding representation thereof; establishing a shared forced dissipative floating ice dynamics model which is a fusion of a historical data driven deep neural network and a physical knowledge driven Lagrangian dynamics; learning, by the deep neural network, driving terms shared among multiple floating ice track segments, historical movement characteristics and non-negative dissipative terms adaptively generated from the current state from the historical track data; converting the track segments into supervised training samples to train the model; inputting the current floating ice state into the trained model to predict the floating ice track result at one or more future time points in a multi-step rolling recursive manner.

The application discloses a vehicle design optimization method and device based on collaborative optimal design and optimal control, and relates to the technical field of vehicle design optimization. In the method, a multi-degree-of-freedom vehicle dynamics model is constructed based on Lagrange dynamics, and a power assembly mass model is constructed. Before constructing a collaborative optimization problem, simulation control is performed under a preset working condition based on the multi-degree-of-freedom vehicle dynamics model and the power assembly mass model to obtain an initial solution. Then, a multi-objective collaborative optimization problem is constructed according to the initial solution, a design parameter set, a control parameter set and related constraints. Finally, the multi-objective collaborative optimization problem is converted into a nonlinear optimization problem, and a solver is used to solve the problem to obtain an optimal control parameter set and an optimal design parameter set. The application constructs a collaborative optimization problem for a vehicle, solves the problem after conversion into a nonlinear optimization problem, and can simultaneously optimize the design parameters and the control parameters of the vehicle.

The invention belongs to the technical field of agent formation control, and particularly discloses a distributed position and attitude dynamic adaptation and formation control method for a Lagrange multi-agent system with unknown kinematics and dynamics parameters, electronic equipment and a readable storage medium, and the method comprises the steps: S1, obtaining the state information of a neighbor node in a current scene, the position and the angle of the intelligent agent are processed according to the topology and formation information; s2, the position information and the steering angle information are processed in kinematics, and the estimated speed of the intelligent body is obtained according to the deviation of the attitude; s3, obtaining an estimated torque of the intelligent agent according to the estimated speed; and S4, constructing a Lagrange dynamical model, and obtaining new left and right wheel speed control quantities according to the estimated torque.

The application discloses a flexible hydraulic mechanical arm system multi-body dynamics modeling method. The core comprises the following steps: different mechanical structures of each arm rod of the mechanical arm are respectively simplified into common beam types or combinations thereof according to the different mechanical structures; specific base functions of the arm frames with different structures are respectively derived according to the Rayleigh-Ritz method; the Hamilton action of the arm rod is solved according to the Hamilton principle, and a characteristic vector is calculated and substituted into the base function group; the flexible arm is decomposed into an imaginary rigid part and a flexible part by adopting the rigid and flexible virtual separation idea and is processed respectively; the hydraulic driving force and the driving torque of the mechanical arm under the flexible deformation condition are derived and calculated; and each description quantity is integrated into the Lagrange equation according to the Lagrange dynamics modeling method, and a complete system mathematical model is derived. The application has small calculation amount, achieves the purpose of simplifying calculation and improving efficiency, and has clear derivation idea and good portability.

The present application relates to the technical field of bionic tendon, especially to a PID-based bionic tendon control method, a test method and a test platform, and discloses a PID-based bionic tendon control method, which comprises the following steps: S1: adopting a Lagrange dynamics modeling method to establish a dynamics model for a planar two-joint collaborative robot, S2: determining an error function according to an uncertainty boundary and a diagonal matrix, and obtaining a control model according to the error function, S3: obtaining angle data of a robot joint and air pressure data of a pneumatic muscle, and inputting the data into the control model to obtain control parameters. The present application determines an error function through an uncertainty boundary and a diagonal matrix, and obtains a control model according to the error function. The control model obtained in this way can obtain stable control parameters, can satisfy the strategy generation of single-arm or double-arm control, and has good effectiveness and robustness.