42 results about "Singular perturbation" patented technology

The invention relates to the technical field of intelligent fire stations, in particular to an intelligent modularized prefabricated cabin type tunnel fire station terminal dynamic early warning system based on edge calculation and multi-source perception. Comprising a multi-source data acquisition unit; the edge calculation processing unit is used for performing real-time fusion and dynamic modeling on the acquired data, fusing multi-source data based on Kalman filtering and LSTM, and constructing a risk evolution model with fast and slow dynamic components by adopting a singular perturbation theory; an intelligent early warning analysis unit; and a data management unit. According to the method, a multi-scale feature extraction framework is constructed by adopting parallel GRU branches through an improved long-short-term memory network: a short-term branch captures second-level rapid features such as flame flicker and sudden change of smoke concentration, and a long-term branch captures minute-level slow change trends such as temperature gradient rise; dynamic weights are given to different time scale features in combination with an adaptive attention mechanism; the design improves the pertinence and accuracy of feature extraction in a complex scene.

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.

This invention discloses an asynchronous, non-fragile H∞ control method and system for a rotating inverted pendulum, belonging to the field of robust control technology for DC motor rotating inverted pendulums. Addressing the problems of modal asynchrony, controller gain perturbation, time-varying delay, and quantization error in DC motor rotating inverted pendulum systems, this invention proposes an asynchronous, non-fragile H∞ control framework. The main steps include: constructing a Markov jump singular perturbation system model with time-varying delay and variable quantization density; designing an asynchronous, non-fragile output feedback controller for the rotating inverted pendulum, compatible with controller gain perturbation and quantization uncertainty; and deriving the linear matrix inequality conditions through modal-dependent Lyapunov functions to ensure the system's exponential stability in finite time and to meet the preset H∞ performance index. This invention can effectively improve the robustness and stability of DC motor rotating inverted pendulums under attack and parameter perturbation scenarios.

The invention discloses a vulnerability analysis method of a multi-time-scale micro-grid under FDI attack, which comprises the following steps: decomposing system dynamics of a direct current micro-grid into a fast boundary layer subsystem and a slow order reduction subsystem by using a singular perturbation theory, respectively deducing stability conditions of each subsystem under a false data injection attack condition, and analyzing the vulnerability of the multi-time-scale micro-grid under the false data injection attack condition; and the input state stability of the low-speed subsystem under the FDI attack is proved. The worst deviation of the system state under the attack condition is calculated by constructing a zootope reachable set analysis framework, and the maximum allowable attack amplitude capable of ensuring safe operation of the system is quantified. According to the method, through numerical simulation and hardware experiment verification, a novel attack surface introduced by multi-time scale characteristics can be effectively revealed, and theoretical support and guidance are provided for safe and stable control of the DC micro-grid.

This invention provides a temperature balancing control method for high-temperature superconducting magnets using circulating cold helium gas. This method, which belongs to the field of superconducting magnet temperature control, includes: calculating the difference between the target temperature and the actual measured temperature to obtain the steady-state error for each magnet group; constructing a mathematical model of the cold helium cooling system based on singular perturbation theory; clarifying control objectives to improve the overall performance and stability of the cold helium cooling system; and constructing a singular perturbation control model based on the variable domain Smith. This invention can improve the response speed and accuracy of the cold helium cooling system.

The invention discloses a flexible connecting rod mechanical arm fixed time adaptive neural network compound control method which comprises the following steps: decomposing a flexible connecting rod mechanical arm model into a slow subsystem and a fast subsystem based on a singular perturbation theory, and defining a slow subsystem state variable and a fast subsystem state variable in the decomposition process; and the slow subsystem comprises an unmodeled dynamic state, and meanwhile, the control moment in the model is decomposed into a tracking controller used for controlling the slow subsystem and a flexible suppression controller used for controlling the fast subsystem. According to the technical scheme, elastic vibration of the flexible mode is relieved, meanwhile, tracking of the time-varying expected position by the mechanical arm is achieved, and the mechanical arm is stable within the fixed time.

The application provides a method and system for offshore wind turbine integrated dynamic feature reservation. The full state time series data of the offshore wind turbine multi-physical field coupling system is obtained, the modal parameters of the system are extracted by using a dynamic modal decomposition method, the participation factor matrix is calculated, the participation degree of each state quantity and the corresponding physical link to each mode is quantified, the dominant state quantity of the extracted mode and the corresponding physical link are located, the dominant state set is constructed, the time scale distribution of each mode is quantified, the time scale distribution characteristic atlas of different physical links of the overall system is formed, the fast time scale, analysis time scale and slow time scale boundaries in the atlas are divided according to different research needs, and the offshore wind turbine integrated reduced order model is constructed based on the singular perturbation theory. The application can effectively reserve the dynamic features of the offshore wind turbine multi-field coupling characteristics, and provides a precondition and quantitative basis for physical-based model reduction modeling.

The present invention provides a preset time control method and device for a flexible linkage manipulator, relating to the field of robotics. The method comprises: constructing a dynamic model of the flexible manipulator; performing a dual-time-scale model decomposition of the flexible manipulator dynamic model based on singular perturbation theory to obtain a slow-varying subsystem and a fast-varying subsystem; designing a preset time trajectory tracking controller based on the slow-varying subsystem using a composite learning algorithm based on a neural network and a disturbance observer, using a time transformation function and a state transformation function; designing a vibration suppression controller based on the fast-varying subsystem by constructing sliding mode variables; and obtaining control inputs for the flexible linkage manipulator based on the preset time trajectory tracking controller and the vibration suppression controller. This method can effectively suppress flexible vibrations and improve the robustness and adaptability of the system.

The application relates to a flexible mechanical arm control method and system based on a fractional order complementary sliding mode, and the method comprises the following steps: constructing a flexible mechanical arm dynamics model according to mechanical arm related parameters, a hypothetical modal method and a Lagrange method; decoupling the flexible mechanical arm dynamics model according to a singular perturbation method to generate a slow variable subsystem model and a fast variable subsystem model; establishing a super-helix extended state observer according to the slow variable subsystem model and generating an observed disturbance value; generating a fractional order complementary sliding mode control law according to a flexible mechanical arm position tracking error; generating a trajectory tracking control law according to a tracking differentiator, the observed disturbance value and the fractional order complementary sliding mode control law; generating a vibration suppression control law according to the fast variable subsystem model, a super-helix algorithm and a mechanical arm end vibration amount; and combining the trajectory tracking control law and the vibration suppression control law to generate a total control law. The control precision is improved through the fractional order complementary sliding mode, and chattering is effectively suppressed.

The invention belongs to the technical field of power systems, and particularly discloses a singular perturbation-reactive decoupling-based order reduction method and device for a network construction type converter. According to the invention, the original state space model is converted; on the basis of a singular perturbation strategy, order reduction processing is carried out according to target variable characteristics; order reduction processing is carried out on a voltage dynamic equation in a reactive power control outer ring, reactive power decoupling is carried out in the mode that a voltage state variable is introduced into an active power control state equation, and a differential equation based on the phase angle relation is established. Through the above mode, aiming at the electromagnetic dynamic process of the fast subsystem, order reduction processing is carried out on the electromagnetic dynamic state variable based on the singular perturbation strategy, order reduction processing is carried out on the voltage dynamic equation in the reactive power control outer loop, high-order dynamic items are eliminated, main dynamic characteristics in the state space are reserved, the model order and the solving complexity are obviously reduced, and the reliability of the system is improved. Therefore, the model order can be systematically reduced on the premise of ensuring key dynamic characteristics and stability analysis precision.

The present invention discloses a coordinated control method for a slow-dynamic unknown coal-fired power generation system based on T-S fuzzy and TD3, comprising the following steps: first, the coal-fired power generation system is decomposed into a fast subsystem and a slow subsystem using singular perturbation theory, and the original control task is decomposed into a stabilization task of the fast subsystem and a tracking task of the slow subsystem. For the fast subsystem, the slow-varying characteristics of steam pressure are utilized to select fuzzy sets on its definition domain to construct a T-S fuzzy model. The fast subsystem controller is obtained by solving the algebraic Riccati equations corresponding to multiple linear systems. For the slow subsystem, a dual-Q network is used to reduce the overestimation of the Q value, and a dynamic learning rate and batch size adjustment mechanism are introduced to accelerate training convergence. The control input of the slow subsystem is obtained by learning under the TD3 algorithm framework. The control inputs of the fast and slow subsystems are combined and acted on the original system to obtain the state information at the next moment. The intelligent agent interacts with the original system to complete collaborative optimization.

The application relates to the technical field of intelligent fire station, in particular to an intelligent modular prefabricated cabin type tunnel fire station terminal dynamic early warning system based on edge computing and multi-source sensing. The system comprises a multi-source data acquisition unit, an edge computing processing unit, an intelligent early warning analysis unit and a data management unit. The edge computing processing unit is used for real-time fusion and dynamic modeling of the collected data, multi-source data are fused based on Kalman filtering and LSTM, and a risk evolution model with fast and slow dynamic components is constructed by adopting singular perturbation theory. The improved long short-term memory network adopts a parallel GRU branch to construct a multi-scale feature extraction architecture: a short-term branch captures second-level fast features such as flame flickering and smoke concentration sudden change, a long-term branch captures minute-level slow change trends such as temperature gradient rise, and an adaptive attention mechanism is combined to give dynamic weights to different time scale features. The design improves the pertinence and accuracy of feature extraction in complex scenes.

The present invention belongs to the field of industrial Internet and control technology. In view of the phenomenon that continuous dynamic processes, discrete events, and the diversity of controlled objects and multiple time scales coexist in industrial Internet systems, a hybrid singular system modeling and stability analysis method based on multiple sampling rates is proposed to solve the stability analysis and controller design problems of the proposed model, while considering the influence of multiple sampling rates and singular perturbation parameters on system stability. The present invention can achieve the complex characteristics of multiple system couplings such as continuous dynamics, multi-sampling, discrete events and multiple time scales, that is, effectively improve the system operation performance and anti-interference performance. At the same time, the hybrid singular perturbation system model established by the Lyapunov function method is applicable to more complex actual systems and also provides a better compromise between system performance and execution cost, that is, it has strong feasibility, which means that it has good practical application prospects.

The invention provides a time-lag industrial process tracking control method based on inner and outer ring singular perturbation decomposition, and the method comprises the steps: firstly building a cascade singular perturbation time-lag system model for an industrial process containing fast and slow dynamic states and time lag, and decomposing a tracking problem into an optimal tracking problem of an outer ring slow subsystem and an inner ring fast subsystem based on a singular perturbation theory; linear quadratic tracking is converted into an adjustment problem by constructing an auxiliary system, and an optimal controller theoretical form is deduced. The condition that model parameters are difficult to obtain is considered, a data-driven reinforcement learning algorithm is designed for inner and outer ring operation layers, a compact equation least square solution converted by a Riccati equation is solved through input and output data, and an optimal control strategy is learned. And finally, the learned controller is utilized to realize the optimal tracking of the given signal by the system. The method does not depend on system prior information, solves the problem of traditional control in a multi-scale and time-delay coupling system, improves the control precision and response speed of a complex industrial process, and has important application value in the field of production automation.

To overcome the current technical problems of difficulty in analyzing the causes of bridge anomalies and the inability to actively prevent bridge resonance, this invention provides a Fourier method for anomaly reproduction and resonance prevention based on a bridge twin dynamic model. This method relies on the fact that the system matrix related to all bridge nodes and the observation coefficient matrix related to nodes are only related to the bridge parameters and do not change significantly during a single continuous bridge test. The reproduction of bridge anomalies or the simulation of bridge resonance and abnormal loading tests are relatively short. Based on the characteristics of slowly changing matrices, a singular perturbation method is used to set the slowly changing matrix as a constant matrix in a single continuous test. Fourier series approximation is used to approximate the fast-changing variable over time, obtaining the ambiguous excitation input when bridge anomalies occur. The system's reproduction of anomalies is verified through equivalent bridge simulation; thus, the artificial excitation input for simulating bridge resonance or anomalies is obtained, solving the technical problems of difficulty in analyzing the causes of bridge anomalies and the inability to actively prevent bridge resonance.

The application provides an iterative learning impedance control algorithm for a flexible driving exoskeleton, and the algorithm comprises the following steps: a dynamics model of an upper limb rehabilitation robot is established by measuring parameters such as the length, mass and stiffness of a series elastic actuator of an exoskeleton joint; secondly, the model is processed by using singular perturbation theory to simplify the complexity of the system; on this basis, a human-computer interaction control strategy is designed by combining impedance control and iterative learning algorithm, and collaborative rehabilitation training of the machine active and human active modes is realized; finally, the effectiveness of the algorithm is verified through simulation and experiment platform, and the results show that the method can adapt to the flexible driving characteristics, and improve the tracking accuracy and human-computer interaction safety of the exoskeleton.

The invention discloses a motor sliding mode control method and system based on an equivalent input disturbance observer. The method comprises the following steps: firstly, proposing a basic dynamic equation of the surface-mounted permanent magnet synchronous motor on a two-phase synchronous rotation orthogonal coordinate system, introducing a speed tracking error, and establishing a mathematical model of a singular perturbation system; then, an equivalent input disturbance observer is constructed and used for estimating lumped disturbance of the servo system in real time, and convergence conditions of the observer are given; and finally, obtaining the H-infinity performance of the closed-loop system based on the Lyapunov stability theory, and determining the gain matrix of the non-cascade sliding mode controller according to the corresponding linear matrix inequality. The method effectively improves the control performance and the anti-interference capability of the driving system, and is suitable for the design of a high-performance servo control system of the permanent magnet synchronous motor.

The invention discloses a fuzzy adaptive control method for reduced order output of an arm support electro-hydraulic execution system, and belongs to the technical field of arm support electro-hydraulic execution system control. The method comprises the steps that firstly, a full-state model of a system is established for a boom electro-hydraulic execution system, secondly, order reduction processing is carried out based on the singular perturbation theory, and an order reduction model is obtained; then, designing a state observer based on the reduced-order model, and constructing a fuzzy adaptive state observer in combination with a fuzzy adaptive strategy; and finally, designing a fuzzy adaptive sliding mode controller based on the reduced-order model, and combining the fuzzy adaptive sliding mode controller with a fuzzy adaptive state observer to obtain a reduced-order output fuzzy adaptive controller. According to the method, the system state is observed through the fuzzy adaptive state observer, and system adaptive control is carried out, so that the robustness of the system can be effectively improved.

The application discloses a kind of distributed time-varying optimal resource allocation methods based on cluster neural dynamics, it is related to distributed control technical field, and the specific steps of this method are as follows: define problem: to N recurrent neural network with set formation a recurrent neural network RNN set, for solving distributed optimal time-varying resource allocation problem, in the problem, non-quadratic local objective function and global equality constraint condition are changed with time, some global information is estimated using dynamic average consensus protocol, a new type of neural dynamics method with double time scale is proposed, and the time-varying optimal resource allocation of given problem is asymptotically tracked in a distributed manner, analysis shows that the proposed neural dynamics model is regarded as singular perturbation system, and converges to time-varying optimal resource configuration by singular perturbation theory, finally, the results are verified by two examples.

A control method for vehicle magnetorheological suspension based on dual-timescale separation includes: establishing an equivalent drive circuit model with time delay; separating the equivalent drive circuit model into two timescales based on singular perturbation theory to construct a fast-changing current subsystem and a slow-changing control subsystem; designing a slow-changing control subsystem controller for the slow-changing control subsystem, calculating the reference current output by the slow-changing control subsystem, and then outputting the reference current to the fast-changing current subsystem; designing a fast-changing current subsystem controller for the fast-changing current subsystem, designing a Smith predictor to compensate for the time delay, and calculating the compensated duty cycle output by the fast-changing current subsystem by combining the reference current and the non-singular reaching law; and outputting the compensated duty cycle to the equivalent drive circuit model to achieve real-time control of the magnetorheological suspension. This invention can effectively handle the multi-source time delay problem of magnetorheological suspension in two timescales, improving vehicle ride comfort and handling stability.

The application discloses a quad-rotor unmanned aerial vehicle attitude tracking double-time scale model predictive control method, relates to the quad-rotor unmanned aerial vehicle control technical field, and aims at the double-time scale characteristics and input constraint problems existing in the quad-rotor unmanned aerial vehicle attitude system. First, the singular perturbation theory is used to decompose the attitude system into two slow and fast reduced-order subsystems. Then, a model predictive controller matched with the time scale of each subsystem is designed for each subsystem, and a contraction constraint based on the auxiliary control law design is introduced in the optimization problem to ensure the recursive feasibility and closed-loop stability of the controller. Finally, the control amount of the two subsystems is combined to construct the composite control input of the attitude system. The application reduces the complexity of the quad-rotor unmanned aerial vehicle attitude system controller design through the decomposition-combination design strategy, and can explicitly process the input constraint while improving the attitude tracking performance and robustness through online optimization.

The application discloses a model-free coordinated adaptive optimal output regulator design method and a control system. Firstly, a classic master-slave structure is adopted, torque synchronization is maintained through field-oriented control; then, a singular perturbation theory is applied to model order reduction of a double permanent magnet synchronous motor system, and a low-order model can effectively reduce learning time and calculation complexity of a speed loop controller design. Then, a model-free coordinated adaptive optimal output regulator is designed under an adaptive dynamic programming framework, so as to drive gear speed to asymptotically track a reference signal and inhibit bounded unknown load torque disturbance. The application can improve transient response performance.

The invention provides a state space type rotor free wake model design method, which comprises the following steps: firstly, establishing a blade motion model, a rotor aerodynamic force model and a rotor free wake nonlinear model, then establishing a rotor free wake linear time-varying model, and finally designing a rotor free wake model through a harmonic decomposition method and a singular perturbation theory. And finally, establishing a rotor wing free wake state space model, and verifying the established rotor wing free wake state space model. According to the method, the state space form representation of the rotor wing free wake is realized, the calculation precision of the rotor wing wake and the aerodynamic force is ensured, and a universal solution is provided for multi-rotor wing aerodynamic interference analysis and aerodynamic force response prediction.

This invention relates to a speed control method and electronic device for permanent magnet synchronous motors (PMSMs) based on singular perturbation theory. The method includes: establishing a singular perturbation model of the PMSM and decoupling it into a fast-changing subsystem and a slow-changing subsystem; constructing an amplitude control set model predictive controller to obtain a voltage amplitude candidate set and establishing a current prediction model; calculating a q-axis current reference value based on a deadbeat controller; constructing a cost function based on the q-axis current reference value; using the cost function and the current prediction model to traverse the voltage amplitude candidate set; selecting the voltage combination with the minimum cost function as the optimal voltage control signal; and driving the PMSM based on the optimal voltage control signal. Compared with existing technologies, this invention has advantages such as improved current tracking accuracy, suppression of harmonics and torque ripple, ensuring rapid tracking of speed commands, and simultaneously meeting the requirements of high dynamics, high precision, and robust control of motor speed.

The invention discloses a grid-connected system model order reduction method based on a singular perturbation method and a reduction margin, and the method comprises the steps: precisely calculating a state variable time constant through the analysis of a system characteristic value and a participation factor, and classifying variables according to a time scale separation ratio. The method comprises the following steps: approximately decoupling fast and slow subsystems of a system by using an SPM to obtain a preliminary order reduction model; the influence of the reduced-order model on the critical oscillation mode is measured by means of the MRM, key state variables are reserved through iterative optimization, and further dimension reduction of the model is achieved. According to the method, the fast and slow dynamic characteristics of the system state variables can be accurately divided, so that the order reduction model meets the error requirement, the order reduction precision and the simulation efficiency are effectively balanced, and an efficient tool is provided for analysis and control of the power electronic interface grid-connected system.

The invention discloses a GFL-VSC transient stability analysis method and system considering LVRT control, and belongs to the technical field of power system safety and stability analysis. The method comprises the following steps: decomposing a full-order model into a low-order fast subsystem and a low-order slow subsystem by using small parameters based on a singular perturbation theory according to a time scale difference between a current loop and a phase-locked loop; in the slow subsystem, solving a nonlinear differential equation of a phase-locked loop by using a multi-scale method to obtain an approximate analytical solution based on initial conditions, system parameters and fault depth, and establishing a stable boundary criterion according to the approximate analytical solution; and in the fast subsystem, establishing a dynamic model comprising a time-varying item, solving to obtain an analytic solution of the dynamic model, and determining the transient stability of the GFL-VSC under the influence of the current loop control parameters according to the stability boundary criterion and the analytic solution. According to the method, the transient stability of the GFL-VSC considering the LVRT control can be effectively analyzed.

The invention provides an iterative learning impedance control algorithm for a flexible driving exoskeleton, and the algorithm comprises the steps: building a kinetic model of an upper limb rehabilitation robot through measuring the length and mass of an exoskeleton joint, the rigidity of a series elastic driver and other parameters; secondly, the model is processed through the singular perturbation theory, and the system complexity is simplified; on the basis, an impedance control and iterative learning algorithm is combined, a man-machine interaction control strategy is designed, and collaborative rehabilitation training of a machine active mode and a human body active mode is achieved. Finally, the effectiveness of the algorithm is verified through a simulation and experiment platform, the result shows that the method can adapt to the flexible driving characteristic, and the tracking precision of the exoskeleton and the man-machine interaction safety are improved.

The application discloses a hybrid reduced-order control method for large-scale dynamic systems and relates to the technical field of system reduced-order control.The application builds a multi-time-scale state space model, quantitatively divides fast and slow subsystems based on a preset time scale threshold value, accurately screens dominant state variables by using eigenvalue dominant factors and participation factor analysis, performs energy-optimal reduction on the slow subsystem by adopting balance truncation and singular perturbation principles, and introduces a closed-loop deviation evaluation and automatic optimization mechanism, thereby solving the problems that the division of fast and slow subsystems is subjective in the prior art, dominant modes are easily lost, transient accuracy is poor, decoupling and reduction links are mutually fragmented and depend on artificial trial and error, and realizing automation, high precision and strong universality of the reduction process, so that the model order can be reduced while the dominant modes and transient characteristics of the system are completely retained, and an efficient and reliable technical scheme is provided for analysis, simulation and control design of large-scale dynamic systems.

The invention discloses a novel high-dimensional system simplification method, which utilizes the advantage of high numerical calculation speed of a simulation platform, combines a singular perturbation theory with a numerical analysis method, and replaces the complexity of full-order system nonlinear modeling with the complexity of polynomial parameters, thereby realizing order reduction of a DDWF system. The method comprises the following steps: firstly, analyzing an operation range of a full-order model of a DDWF incorporated weak alternating current system, secondly, randomly screening linearization points, and respectively establishing small signal models under different working conditions at the linearization points; then reducing the order of the small signal model according to a singular perturbation theory to obtain an order-reduced small signal model set under different working conditions; and fitting a non-linear relationship between the operation parameters and the reduced-order model through numerical analysis. The result shows that the order reduction method saves the calculation cost to a great extent while keeping the dynamic characteristics of the system, and is suitable for the modeling requirements of the complex system of the electromagnetic simulation platform.

The invention discloses a positive security control method and system for a singular perturbation multi-agent system under DoS attack, and belongs to the field of multi-agent system control. Firstly, a state space mathematical model of the singular perturbation multi-agent system under the DoS attack is established, and a distributed security consistency control protocol is designed by using neighbor information of intelligent individuals; the method comprises the following steps: according to a positive system control theory and a corresponding closed-loop control system expression, obtaining sufficient and necessary conditions for keeping the positive property of the closed-loop control system according to the positive system control theory, decoupling the closed-loop control system by using a network decoupling technology, and finally controlling the closed-loop control system according to a security consistency protocol, thereby realizing network attack resistance and security consistency. And a control gain matrix for realizing the targets and constraints which need to be met by attack signals are obtained. According to the method, a positive state keeping method and a safety control technology are combined, DoS attacks are resisted on the premise of ensuring that the system state is always positive, and the consistency of the system is ensured.