214 results about "System dynamics model" patented technology

The invention discloses a building equipment fault rapid attribution and self-optimization method, which relates to the field of intelligent operation and maintenance of building equipment, establishes a multi-dimensional association relationship among equipment, building space, sensors and fault modes, and deeply combines a fault attribution engine with system dynamics. Data driving flexibility and physical logic preciseness are simultaneously realized in fault attribution, and reasoning from abnormal data to root cause analysis is realized. According to the method, the optimization efficiency is remarkably improved through a specific incremental learning / local updating mode, the weight of the knowledge graph and the Bayesian network conditional probability table are adjusted according to the maintenance result, physical equation parameters are calibrated, and dynamic updating of node attributes of the knowledge graph is achieved; through dynamic weight optimization, the adaptive ability of real-time reasoning confidence evaluation and scene context is improved; a physical equation is used as an executable knowledge unit to be embedded into a knowledge graph through establishment and deep integration of a system dynamics model, and a safety and reliability mechanism is designed for verification.

The invention discloses a multi-unmanned-aerial-vehicle cooperative hoisting method and system, and the method comprises the steps: firstly, enabling each unmanned aerial vehicle to operate an extended state observer, regarding internal and external disturbances (such as airflow, unknown load parameters, internal coupling force and the like) as a lumped disturbance, and carrying out the real-time estimation; and then, through a communication network operation consensus algorithm between the unmanned aerial vehicles, fusing the local disturbance estimation values of the unmanned aerial vehicles into a global consistent system-level disturbance estimation. And finally, each unmanned aerial vehicle actively counteracts the influence of disturbance by using the accurate disturbance estimation value in a feedforward compensation controller, and accurately tracks the respective target trajectory in combination with feedback control. According to the method, active observation of disturbance, distributed fusion and feed-forward compensation are combined, dependence on an accurate system dynamic model is eliminated, and trajectory tracking accuracy, stability and stability of the system in a complex environment are remarkably improved.

The invention discloses a constraint following control method of a two-arm collaborative robot based on differential homeomorphic mapping, and relates to the field of robot control. According to the method, a nominal dynamical model of a robot is established, and a geometric constraint equation is defined according to a cooperative task; establishing global differential homeomorphic mapping by constructing constraint tracking variables and complementary coordinates, and decoupling a system dynamics model into a constraint tracking subsystem and a complementary subsystem; designing a robust adaptive controller fusing sliding mode control and an adaptive law, and estimating and compensating the uncertainty of the system on line; according to the method, the constraint processing complexity is effectively simplified through differential homeomorphic mapping, the constraint tracking precision and robustness of the system under model uncertainty and external interference are remarkably improved, and high-precision and high-stability cooperative task execution of the two-arm robot in the dynamic environment is achieved.

The invention belongs to the technical field of EL system control, and discloses a preset performance control method considering dynamic and constraint of an execution mechanism. The method comprises the following steps: firstly, establishing a system dynamics model considering dynamic and input saturation constraints of an executing mechanism; then, designing an asymmetric tubular performance boundary to restrain a system tracking error, and defining a conversion error according to the system tracking error; meanwhile, a disturbance observer based on immersion and invariance is designed, and composite disturbance is estimated. Furthermore, a tubular preset performance controller is designed by adopting a backstepping method, a virtual control law and an expected torque are constructed, and a multi-state auxiliary system is introduced to compensate the input saturation influence. And finally, a control instruction is generated by integrating the auxiliary system state, the virtual control law, the expected torque and the interference estimation value, and the control instruction acts on the system after being processed by a saturation function. According to the method, actuator dynamics, input saturation and external disturbance can be processed at the same time, and tracking errors are ensured to strictly follow a preset performance boundary.

The invention relates to the technical field of industrial robot control, and discloses a single-connecting-rod mechanical arm system fault-tolerant asynchronous control method based on double-variable decision, which comprises the following steps: establishing a semi-Markov jump system dynamic model for describing the operating characteristics of a single-connecting-rod mechanical arm; based on the dynamic model, designing a fault-tolerant asynchronous controller structure based on a double-variable decision and an experience-machine cooperative operation strategy; establishing sufficient conditions which enable the closed-loop system to be randomly stable and meet H infinity performance indexes; and the numerical value of the gain of the machine controller is obtained by solving the linear matrix inequality set, and a final control law is generated to conduct fault-tolerant asynchronous control on the single-connecting-rod mechanical arm. According to the fault-tolerant asynchronous control method for the mechanical arm, fault-tolerant asynchronous control of the mechanical arm is achieved through a double-variable decision and an experience-machine cooperation strategy in combination with modal transfer rate boundary processing, and the random stability and the H-infinity performance of the system are guaranteed under the modal mismatching and fault working conditions.

The invention discloses a GIS in-place robot coordination control method with a posture correction function, and belongs to the technical field of robot coordination control. The method comprises the following steps: establishing a 2D-3D coordinate mapping corresponding relation; fusing the corresponding relation with an operation state predicted by an IMU-based inertial measurement and system dynamics model; performing error calculation on the obtained 6D pose information and target pose information; constructing a nonlinear kinetic equation of the robot, and converting a control target into an optimization problem of various constraints; and converting the obtained control instruction into a torque signal for actually driving the robot to move. According to the method, the influence of mismatching on pose estimation is effectively inhibited by establishing the 2D-3D coordinate mapping relation; through a joint torque PID control algorithm, the torque required by each joint is calculated in real time, and high-precision tracking of an expected trajectory is realized; an MPC optimization control framework with multiple constraints is constructed, and precise, smooth and safe coordination control over the GIS in-place process is achieved.

The invention relates to the technical field of system dynamics, and discloses an electricity certificate carbon market price analysis method and system based on system dynamics, and the method comprises the steps: S1, determining the main body of a green certificate transaction market and the main body of a carbon emission permit transaction market, and determining a renewable energy power generation mode; s2, constructing a TCC cost control mechanism; s3, TCC cost is calculated according to a TCC cost control mechanism; s4, establishing stock flow dynamic model frames of the green certificate market and the electricity market and the carbon emission permit trading market and the electricity market, and combining the two dynamic model frames to generate an electricity certificate carbon market stock flow dynamic model frame; and S5, analyzing the electricity evidence carbon market price. The system comprises an information input unit, a mechanism construction unit, a calculation and analysis unit and a system dynamics model framework construction unit. According to the method, the system dynamics and the method for controlling and optimizing the TCC cost are combined, and the analysis accuracy and reliability are improved.

The invention provides an optimal primary frequency control method based on reinforcement learning. The method comprises the following steps: establishing a dynamic model of a power system; designing a frequency control strategy based on a deep reinforcement learning algorithm; the system frequency deviation is used as a reward signal, and a control strategy is trained through interaction with a power system; lyapunov function stability constraints are introduced into the reinforcement learning algorithm; a neural network is adopted to carry out parameterization design on the controller; discretizing the frequency dynamic equation, and proposing a multi-node collaborative optimization target; and deploying the trained control strategy to an actual power system, and adjusting control parameters in real time through an online optimization technology to adapt to the dynamic change of the system operation state. By means of the method, the frequency control accuracy and stability of the power system are improved, and the global optimal performance of frequency control of the whole power grid is achieved in actual deployment.

The invention discloses a wave energy prediction method based on optimized empirical mode decomposition and deep learning, and the method comprises the steps: optimizing an empirical mode decomposition process through an improved starfish algorithm, obtaining a limited number of intrinsic mode functions through the decomposition of wave data, and predicting each intrinsic mode function obtained through the decomposition through employing an improved model, according to the model, two layers of CNN are fused to enhance extraction of spatial features, three layers of LSTM network are fused to extract time features, a sparse probe and distillation operation are used to optimize a Transform decoding layer, each predicted intrinsic mode function is subjected to superposition reconstruction, and a final wave data prediction result is obtained. And inputting the predicted data into a system dynamics model of the wave energy device, and solving an optimal damping control sequence by combining real-time data and utilizing rolling optimization calculation so as to realize maximum power control.

The invention provides a time mark event triggering reinforcement learning multi-agent consistency control method and system, and relates to the technical field of multi-agent cooperative control. Establishing a multi-agent system dynamics model comprising a leader and a plurality of followers on the time mark; based on the state information of the leader and the follower, constructing a tracking error and consistency error dynamic equation between the intelligent agents; a reinforcement learning controller based on a neural network is constructed, and an optimal control strategy and a performance index function are approached; an event triggering mechanism is designed, and the controller is triggered to update only when the local error of the intelligent agent exceeds a dynamic threshold value; by constructing a Lyapunov function on a time scale and solving a linear matrix inequality condition, bounded consistency of the system is ensured, and a Zeno phenomenon is eliminated. Unified modeling and distributed safety control of the time scale multi-agent system in the non-ideal environment are achieved, communication resources are saved, the convergence speed is increased, and the robustness and practicability of the system are enhanced.

The invention provides an urban flood disaster dynamic risk assessment method under multi-scale model coupling, and relates to the technical field of urban flood disaster risk prevention and control and emergency risk avoidance. The method comprises the following steps: evaluating the urban flood disaster risk according to urban underlying surface type data, vertical elevation data, drainage pipe network data, hydrogeological data, actually measured rainfall data, hydrological monitoring data and disaster-bearing system instability failure threshold data; constructing a multi-agent system model covering a disaster-inducing factor agent, a disaster-pregnant environment agent and a disaster-bearing body agent, and outputting a dynamic risk value of a disaster-bearing body; constructing a dynamic model of a disaster-affected body disaster-affected system by using a system dynamic modeling technology, outputting a risk change process of a disaster-affected body and quantifying a risk value; the flood disaster emergency prevention and control effect is evaluated by combining engineering and non-engineering measures, and emergency risk avoiding measures are optimized. According to the flood disaster assessment method and device, the flood disaster assessment can be more accurate through the combined action of the macroscale, the mesoscale and the microscale.

The invention provides a hydrogen energy park electric power carbon emission decision optimization model algorithm for solving the problem that an existing park energy management model cannot accurately simulate and optimize the coupling relation between the special element hydrogen energy and an electric power system. The core lies in that a system dynamics model containing'hydrogen energy 'state variables is constructed, and the mutual conversion, storage and consumption processes of electric energy and hydrogen energy in the park are accurately described by defining three state variables of power storage, power generation and hydrogen energy and a plurality of flow variables of electrolysis, direct use, load and the like. According to the model, system dynamic behaviors under different strategies (such as water electrolysis hydrogen production load adjustment, energy storage scheduling optimization, renewable energy source introduction and the like) are simulated, and the influence of the system dynamic behaviors on park electric power carbon emission is quantitatively evaluated, so that data-driven low-carbon operation decision support is provided for park managers, and collaborative optimization of carbon emission and operation cost is realized.

The invention discloses a method and a device for designing a multi-anti-interference incremental backstepping observer based on specified time, and belongs to the technical field of signal tracking and processing. The method comprises the following steps: establishing a system dynamics model containing time-varying disturbance and model uncertainty items; scaling the tracking error and the estimation error by using a scaling function so as to realize the accurate convergence of the system state and the disturbance estimation within the specified time T; combining the scaled error, introducing an incremental backstepping control thought, and designing an incremental backstepping observer capable of processing model uncertainty and multiple disturbances at the same time; under the condition that the total disturbance and the derivative thereof are assumed to be bounded, stability analysis is carried out through a Lyapunov function, and constraint conditions of system stability convergence are deduced; and finally, verifying the effectiveness and superiority of the observer under various input and disturbance working conditions through simulation. The method is suitable for a high-dynamic and time-sensitive precision control system.

The invention relates to the technical field of water resource balance, in particular to a dynamic modeling method and system for regional water resource allocation. The invention discloses a dynamic modeling method for regional water resource allocation. The method comprises the following steps: acquiring regional hydro meteorological historical data; dividing water resource zones according to regional hydro meteorological historical data; hydrological condition extraction is carried out on the water resource subareas to obtain regional water resource feature data; acquiring regional water demand data; constructing a system dynamics model according to the regional water resource feature data and the regional water demand data; performing inter-industry water consumption interaction relationship simulation on the system dynamics model to obtain system dynamic balance data of regional water resources; and establishing a water flow network topological structure according to the dynamic balance data of the system. By considering the water demands, the supply capacity and the mutual influence of different industries, the water resource demands among the industries can be reasonably balanced, the resource allocation is optimized, the water contradiction is reduced, and the fairness and the efficiency of the water resource allocation are improved.

The invention relates to a Markov jump network system control method under DoS attack based on a phototropic growth algorithm. The method comprises the following steps: establishing a networked system model with Markov jump parameters; an energy-limited DoS attack model is constructed; introducing a dynamic event triggering mechanism, and determining a triggering moment based on a measurement error, a weighting coefficient, a design parameter and an internal dynamic variable; based on a networked system model and an energy-limited DoS attack model, a dynamic event triggering mechanism is fused to construct a closed-loop system dynamic model; establishing a hybrid anti-interference performance index; performing global optimization on gain parameters of the closed-loop system dynamic model by adopting a phototropic growth algorithm to obtain an optimal controller gain; and constructing a controller under the energy-limited DoS attack, and realizing the control of the Markov jump network system under the DoS attack. The finally constructed controller can realize high robustness, low communication burden and excellent dynamic performance control of the Markov hopping network system in an energy limited DoS attack scene.

A wind power generation system preset time maximum power point tracking control method comprises the following steps: firstly, establishing a wind power generation system dynamic model containing parameter uncertainty and external disturbance, and meanwhile, constructing a preset time convergence integral sliding mode surface; the method has the core advantages that the convergence time can be directly set by controller parameters and is irrelevant to the initial state of the system; secondly, designing a preset time self-adaptive controller based on the rotating speed tracking error and the sliding mode surface, and enabling the rotating speed tracking error to be quickly converged in preset time; the upper bound of lumped uncertainty of the system is estimated online through a self-adaptive mechanism, unknown dynamic and external disturbance are compensated in a unified mode, and dependence on an accurate model and disturbance prior knowledge is not needed; and finally, strictly proving the preset time stability of the closed-loop system through a Lyapunov stability theory. The method has good dynamic response performance and predictable convergence behaviors, and the robustness and the power generation efficiency of the system are improved.

The invention relates to the field of belt conveyor control model analysis, in particular to a multi-motor belt conveyor dynamic model analysis method, which comprises the following steps of: dispersing a belt conveyor system into a generation unit; establishing a system dynamics model; proposing constraint expectation; decomposing an uncertainty part in the kinetic model, and identifying matched and unmatched components in the uncertainty part; the influence of unmatched components on the belt conveyor system in the uncertainty part is verified; establishing a robustness control model based on the nominal part, the system error and the uncertainty part of the dynamic model of the belt conveyor; and the model is verified through dynamics simulation to achieve expected time and change trend. According to the method, modeling is carried out for the long-distance large-dip-angle belt conveyor, and the mismatching uncertainty of an under-actuated system is specifically decomposed; a self-adaptive robust controller is designed, and the speed of the conveyor is restrained by controlling the coordinated driving current of multiple motors; and the action effect of the controller is verified through dynamic simulation of dynamics.

The invention provides a sliding guide rail system kinetic analysis method considering abrasion and creep, and relates to the technical field of numerical control machine tool kinetic analysis. The method comprises the following steps: firstly, establishing a multi-source wear model of a joint surface of an angular contact ball bearing, a lead screw-nut pair and a sliding guide rail, and constructing a creep degradation model of a bearing end cover bolt based on a Norton creep law; then correcting the elastic restoring force of the guide rail system by combining the evolution laws of the abrasion loss and the creep, and constructing a dynamic model of the bidirectional sliding guide rail system considering the comprehensive influence of the abrasion and the creep; bidirectional influence among abrasion, creep deformation and dynamic response is achieved, and finally the dynamic evolution law of the bidirectional sliding guide rail system under the long-term service condition is obtained. The method is clear in modeling process, high in universality and high in calculation precision, the degradation process of the dynamic characteristics of the guide rail system along with the service time can be truly reflected, and an effective theoretical basis is provided for numerical control machine tool service life prediction, structure optimization and operation reliability improvement.

The application discloses a kind of based on multi-scale control technology's unmanned system cooperation privacy protection method, belong to unmanned system safety cooperation control field.This method solves the ill-conditioned numerical problem in the cooperative control of unmanned system with fast-slow coupling characteristics, and the state privacy leakage problem caused by communication eavesdropping.Scheme includes: establishing system dynamic model;Design the differential privacy leader-following multi-scale cooperative controller containing attenuation noise;Obtain the standard singular perturbation form of error system through matrix transformation;Based on multi-scale control theory, solve the state control gain that guarantees mean square consistency;Determine the noise control gain and privacy parameter in combination with probability theory and matrix spectral theory.The present application avoids the ill-conditioned numerical problem, and the controller is robust, while achieving accurate cooperative control, provides verifiable differential privacy protection for the initial state of the system, significantly improves the safety and reliability of unmanned system in the hostile environment.

The present application relates to a kind of planetary gear mechanism multi-point support bending-torsional coupling excitation dynamic characteristics estimation method, belong to planetary gear technical field, it is difficult to predict and inhibit the problem that multi-point support bearing under actual working condition dynamic load due to the bending-torsional vibration of transmission shaft and the dynamic coupling effect between the dynamic load of planetary gear set exists.Method includes: obtaining the parameters of each planetary gear component of planetary gear mechanism and each bearing parameter;Based on each bearing parameter, the time-varying nonlinear support stiffness of bearing is calculated;Based on the time-varying nonlinear support stiffness of bearing and the parameters of each planetary gear component, the subsystem dynamics model of planetary gear mechanism is established;When the inner and outer rings of intermediate bearing rotate simultaneously in the given time step, the dynamic load f o And deformation δ o Of bearing outer ring by planetary carrier / bearing seat, and the dynamic load f i And deformation δ i Of inner ring by transmission shaft, and the support stiffness and dynamic response of system at current time are calculated, the subsystem dynamics model of planetary gear mechanism is solved using four-order Runge-Kutta numerical iterative algorithm, and the dynamic response of planetary gear mechanism in next time step is obtained, and the dynamic characteristics estimation is completed by iterative cycle until.

The invention discloses a coastal wetland ecosystem steady-state conversion early warning signal method and system, and belongs to the technical field of coastal wetland ecosystem steady-state analysis, and the method comprises the steps: S100, determining key state variables and parameters in a coastal wetland ecosystem; s200, a system dynamic model is constructed based on a Bayesian equation, the model comprises a change point detection equation in the time sequence data, and a change point detection result is obtained; s300, on the basis of the change point detection result, critical moderation signal indexes of the state variables are calculated through a critical moderation signal quantification model, and the critical moderation signal indexes comprise variance, an autocorrelation function and a recovery rate; s400, constructing an EHI ecosystem health index prediction model based on PCA dimension reduction and an LSTM neural network, and outputting a time sequence prediction result; and S500, through Bayesian dynamic threshold discrimination, in combination with a critical moderated signal index and a prediction result, outputting a graded early warning signal, and the method can effectively predict sudden ecological transition.

The invention belongs to the field of automobile testing, and relates to a calibration method for automobile winter heating by using an environmental wind tunnel, which comprises the following steps: S1, collecting an original dynamic road spectrum, and processing to generate a standard dynamic road spectrum for low-temperature calibration of the environmental wind tunnel; s2, operating the environment wind tunnel based on the standard dynamic road spectrum, acquiring an actual environment control value of the environment wind tunnel in real time, and judging whether the environment wind tunnel meets the winter heating calibration requirement or not according to a preset inspection rule; s3, an air conditioner heating system calibration test is executed in the environment wind tunnel according to a preset winter steady-state working condition matrix, and an initial heating control parameter table is generated; and S4, on the basis of test data corresponding to the initial heating control parameter table, constructing a system dynamic model, optimizing a strategy network in the system dynamic model through imitation learning and course reinforcement learning, and generating a complete heating control parameter table through optimized strategy distillation. The problems of poor comparability and repeatability of calibration data and insufficient calibration precision are solved.

The invention belongs to the field of electro-hydraulic system motion control, and discloses a fixed time optimal synchronous control method for an aero-engine electro-hydraulic actuating mechanism. Constructing a double-cylinder hydraulic system dynamic model, and defining a tracking error and a synchronous coupling error; designing an optimal synchronous controller based on a reinforcement learning actor-commentator architecture, and introducing a fixed time convergence mechanism; and a self-adaptive robust compensation item of the enhanced neural network is designed, and unmodeled dynamics and disturbance are inhibited. According to the method, Lyapunov stability analysis proves that the fixed time stability is met, and comparison experiment verification is carried out on an aero-engine hardware-in-the-loop experiment platform. The method solves the problems that in traditional synchronous control, convergence time depends on initial conditions and is sensitive to uncertainty, and a nonlinear HJB equation is difficult to solve, high-precision synchronous motion control within fixed time is achieved, and the method is suitable for an aero-engine electro-hydraulic execution system with the high reliability requirement.

The invention discloses a suspension system dynamic model calibration method based on an optimization algorithm, and belongs to the technical field of software. The invention aims to solve the problems of existing Kamp; and C, a performance calibration method is low in efficiency and long in period, and the problem that calibration work is repeated and failed due to a parameter interaction effect exists. Therefore, the invention provides a method which comprises the following steps: carrying out a sample vehicle suspension Kamp; c, testing to obtain test data; the method is based on sample car parameters and Kamp; c, establishing a whole vehicle model according to test data; a suspension Kamp is used; carrying out DOE calculation and parameter sensitivity analysis by taking the characteristics C as variables, and selecting Kamp sensitive to the whole vehicle performance; c characteristics are used as benchmarking items; a suspension rigid-flexible coupling dynamic model is established in ADAMS software, and a secondary development script is written to realize automatic operation; carrying out Kamp by taking model part parameters as variables; c, performing DOE calculation and sensitivity analysis on each working condition, and selecting each key Kamp; c, taking characteristic sensitive part parameters as benchmarking variables; kamp of simulation and test is used; c curve matching is taken as an optimization target, an optimization function is established, and Kamp is carried out; the model C is subjected to benchmarking solution, and a high-precision suspension Kamp is output; and C model. Compared with the prior art, the method has the advantages that the optimization algorithm is introduced, so that the suspension Kamp is realized; due to automation and high precision of C model calibration, the working efficiency and benchmarking precision are greatly improved, and a high-precision suspension dynamical model can be output.

A method for constructing a dynamic model of a coupled system of high-speed railway train-track-substructure includes: experimentally obtaining key technical parameters with nonlinear and frequency-varying characteristics in the high-speed railway infrastructure system; constructing a novel wheel-rail contact model suitable for higher speed levels and integrating it into the vehicle-track coupled dynamic model; constructing a train vehicle model and track structure model based on the principles of multi-rigid-body dynamics and the finite element method; and using a self-developed program to realize the dynamic modeling of the train-track system; taking the ballastless track structure, subgrade, bridge, and tunnel structures of high-speed railways as research objects, and applying a finite element simulation platform to realize the construction of a refined finite element model of the track-substructure structure; this invention achieves a breakthrough in the theoretical modeling of the vehicle-track-substructure system of higher speed railways, providing a research method and platform for deepening the understanding of the coupled dynamic mechanism of complex systems of high-speed railway infrastructure with speeds of 400 km / h and above.

The application is suitable for the technical field of vehicle acceleration control, and provides a commercial vehicle acceleration closed-loop control method considering load and running resistance, which comprises the following steps: step 1, driving system dynamics modeling; step 2, driving process dynamic response transfer function modeling; step 3, acceleration dynamic response control parameter determination; and step 4, acceleration adaptive closed-loop control. The method derives the dynamic response transfer function of the driving process by constructing the driving system dynamics model; after the key parameters most sensitive to the acceleration dynamic response are determined through analysis, the method of combining feedforward control and closed-loop feedback adaptive control is adopted to realize the closed-loop adaptive control of the acceleration dynamic response. When the vehicle mass and the running resistance change, the method can effectively guarantee the consistency of the acceleration dynamic response, and further improve the driving quality.

The application discloses a transient fault ride-through and reactive power collaborative control method based on a bionic nerve reflex mechanism and belongs to the technical field of power systems, which comprises the following steps: a power system dynamic model under a large amount of new energy injection is established; a simulation nerve reflex control model is established, and target reference trajectories and control parameters of reactive power equipment in cooperation with traditional electric fields are confirmed; a low-voltage ride-through control system of a high-proportion new energy power grid under the cooperation of reactive power equipment and traditional electric fields in a fault is established; an optimal control sequence for recovery to a steady state after low ride-through under a fault is solved, and current control values are output; ride-through evaluation of reactive power equipment in cooperation with traditional electric fields in a high-proportion new energy system based on a Prony method is performed; and system control parameter collaborative optimization considering an expected disturbance scene is performed. The transient fault ride-through and reactive power collaborative control method based on the bionic nerve reflex mechanism provides an efficient and reliable transient fault ride-through solution for a high-proportion new energy power grid.

This invention belongs to the field of power system analysis and load forecasting technology, and discloses a method and device for forecasting residential electricity consumption. The method couples the population structure subsystem, socio-economic subsystem, and residential electricity consumption system to construct a closed-loop feedback system, forming a system dynamics model. Based on the current initial value of per capita electricity consumption and the calculated rate of change of per capita electricity consumption, the predicted per capita electricity consumption is calculated. This invention can effectively reflect the dynamic evolution of electricity demand under multi-factor coupling conditions, and can provide intuitive and reliable decision-making basis for power grid planning, power load management, and energy policy formulation. It is applicable to medium- and long-term forecasting scenarios of residential electricity consumption at different regional scales.

The application provides a kind of aircraft dynamics modeling method containing multi-degree-of-freedom actuator, the modeling method returns general mechanics and advanced theory and thought of mechanics basic disciplines, aircraft is regarded as multi-rigid body system, based on virtual power principle and multi-body system topological configuration recursive modeling method, the deformation multi-body system dynamics model suitable for aircraft containing multi-actuator is established, and the motion characteristics of multi-degree-of-freedom, strong coupling between the motion characteristics of aircraft external body attitude motion and internal component relative motion are accurately described.

This invention discloses a distributed optimization and safe formation control method for multiple robotic arms with nonlinear uncertainty. By establishing a dynamic model of the multi-robotic arm system with nonlinear uncertainty, a dynamic event triggering method with internal system information is constructed. Each robotic arm broadcasts its position information to its neighboring nodes only when the triggering condition is met. A predetermined reference trajectory is generated for each robotic arm system, converging to the global optimum and satisfying formation constraints. The position tracking error between the actual output of the robotic arm system and the predetermined reference trajectory at each moment is calculated. The virtual control law designed in the first step of the back-reasoning is used as the target to be optimized. A quadratic programming problem is constructed by combining the safety constraint boundary inequality built with the control obstacle function to obtain the safe virtual control law. In the second step of the back-reasoning, the velocity tracking error between the actual state of the robotic arm and the safe virtual control signal is calculated, constructing a back-reasoning controller based on the obstacle Lyapunov function. This invention overcomes the control instability risk caused by strong nonlinearity and achieves high-precision trajectory tracking.