26 results about "Singular perturbation" patented technology

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.

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 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 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 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.

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.

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 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 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 application discloses an intelligent power distribution room AI decision analysis system based on edge computing, which comprises a data acquisition preprocessing module, a friction state modeling module, a comprehensive state construction module, a fast and slow state evolution module, an operation decision determination module and a control instruction generation module.The data acquisition preprocessing module forms standardized multi-dimensional operation state data.The friction state modeling module constructs an improved GMS friction model and forms a friction state variable set.The comprehensive state construction module constructs a comprehensive operation state vector.The fast and slow state evolution module forms fast variable state evolution sequences and slow variable state evolution sequences.The operation decision determination module executes transient operation state determination and operation degradation trend determination, and generates edge side operation decision analysis information.The control instruction generation module generates operation control instructions.The application realizes stable operation decision analysis of the intelligent power distribution room on the nonlinear behavior of the actuator and the fast and slow evolution state by introducing the improved GMS friction model and the singular perturbation theory.

The application discloses a preset performance control method based on singular perturbation for a multi-motor driving system, realizes effective control of slow and fast states, and guarantees the performance and stability of the system in the presence of singular disturbance.The steps of the method are as follows: a dynamic model is established for the multi-motor driving system, the dynamic equation of the system is decomposed into two subsystems of fast and slow by using a singular perturbation system; the tracking error is converted by using a preset performance boundary, and a dynamic equation of the converted error is obtained; the original limited tracking error is converted into unlimited tracking error by error conversion, the originally limited tracking control problem is converted into unlimited tracking control problem, and the converted tracking error is guaranteed to be bounded, so that the original tracking error is kept within the preset performance boundary; a sliding surface and a disturbance observer are designed to estimate the external disturbance suffered by the system; and a sliding mode controller is designed to compensate the control by using the estimated external disturbance.

The invention discloses a model order reduction method based on a first-order singular perturbation theory and a grid-connected system stability analysis method, and belongs to the field of transient stable operation and control of a novel power system, and the method comprises the steps: determining a differential equation set and an algebraic equation set of a new energy grid-connected system; calculating a time constant of a system state quantity, and dividing the new energy grid-connected system into a fast subsystem and a slow subsystem based on the time constant; according to the first-order singular perturbation theory, after derivation is conducted on the differential equation set of the fast subsystem, a second-order differential item in the second-order differential equation set of the fast subsystem is made to be 0, then the second-order differential item is substituted into the differential equation set and the algebraic equation set of the fast subsystem, and a first-order perturbation equivalent model algebraic equation is obtained; therefore, the first-order perturbation dynamic state-considered order reduction model of the new energy grid-connected system is obtained by combining the differential equation set and the algebraic equation set of the slow subsystem, the dominant dynamic state in the new energy grid-connected system can be effectively identified, and model order reduction is realized, so that the stability analysis efficiency is improved, and the accuracy is ensured.

This invention relates to an industrial process method for optimal tracking control of singularly perturbated systems across multiple time scales. By employing singular perturbation theory and reinforcement learning techniques, the invention decomposes the system into fast and slow subsystems, designs optimal control strategies for each subsystem, and then designs a combined suboptimal control strategy for the singularly perturbated system, thus solving the optimal tracking problem constrained by singularly perturbated systems. A novel data-driven ridge regression reinforcement learning control technique is proposed to reduce the impact of perturbation parameters on the system. This invention designs an optimal tracking control strategy for singularly perturbated systems, thereby achieving an ideal trajectory through a suboptimal method, and designs a control strategy for the decomposed slow-varying subsystem using measurable data.

This invention discloses a reinforcement learning tracking control method for multimodal tunnel diodes, belonging to the field of automatic control technology. For tunnel diode circuit systems with multi-timescale characteristics and stochastic mode jumps, a discrete-time Markov jump singular perturbation system model is established. To address the numerical instability caused by singular perturbation parameters, the system is decomposed into slow and fast subsystems using timescale separation techniques. Reference signal dynamics are introduced to construct slow and fast augmented systems including tracking errors. A hetero-policy reinforcement learning algorithm, completely independent of the system dynamics model, is proposed. It iteratively solves the coupled algebraic Riccati equations using collected system input-output data to obtain the optimal control law. This method eliminates the dependence on precise mathematical models, effectively suppresses the adverse effects of multimodal stochastic jumps and fast-slow timescale coupling, ensures strong system robustness, and achieves tracking of the reference signal by the tunnel diode circuit.

The application discloses an AISMC compensation control method for a flexible joint mechanical arm under singular perturbation decomposition and belongs to the field of flexible joint mechanical arm control, and comprises the following steps: S1, a dynamic model of the flexible joint mechanical arm containing uncertain composite disturbance is established, and is decoupled into a slow subsystem and a fast subsystem; S2, a lumped matching disturbance in the slow subsystem is estimated by using a compensation function observer; S3, for the slow subsystem, an integral sliding mode surface and an adaptive approaching law with a dead zone correction are constructed; for the fast subsystem, a linear quadratic regulator is designed, a performance index is defined, and Riccati equation is solved to obtain an optimal control law; S4, the control law of the slow subsystem and the control law of the fast subsystem are superposed to obtain an overall control input; and S5, simulation verification is performed. The AISMC compensation control method for the flexible joint mechanical arm under singular perturbation decomposition has wide application prospects by taking into account the trajectory tracking accuracy, robustness and stability.

The invention discloses a dual-time scale model prediction control method for attitude tracking of a quad-rotor unmanned aerial vehicle, relates to the technical field of quad-rotor unmanned aerial vehicle control, and aims to solve the problems of dual-time scale characteristics and input constraints in an attitude system of the quad-rotor unmanned aerial vehicle. The method comprises the following steps: firstly, decomposing an attitude system into a slow order reduction subsystem and a fast order reduction subsystem by using a singular perturbation theory; then, a model prediction controller matched with the time scale of each subsystem is designed for the subsystem, and contraction constraints designed based on an auxiliary control law are introduced into an optimization problem, so that the recursion feasibility and closed-loop stability of the controllers are guaranteed; and finally, combining the control quantities of the two subsystems to construct composite control input of the attitude system. The design complexity of the quadrotor unmanned aerial vehicle attitude system controller is reduced through a decomposition-composition design strategy, and the attitude tracking performance and robustness can be improved through online optimization while the input constraint is explicitly processed.

The invention discloses a multi-time scale event trigger consistency control method for a discrete time multi-agent system. Aiming at the problem that a discrete time multi-agent system is insufficient in cooperative adaptability due to multi-time scale dynamic coupling, a singular perturbation theory is adopted to decompose the system into fast and slow subsystems, an adaptive dynamic event triggering mechanism is designed to realize on-demand triggering, and a distributed controller is designed based on a consistency theory. A closed-loop stability condition is established by constructing a discrete time Lyapunov function, and controller gain is solved by using a linear matrix inequality technology. According to the method, on the premise of ensuring the stability and consistency of the system, the communication and calculation burden is remarkably reduced, a multi-time-scale coupling scene is effectively adapted, and the system expansibility and robustness are high.

The application belongs to the technical field of power systems, and specifically discloses a network type converter reduced order method and device based on singular perturbation-reactive decoupling. Through the application, the original state space model is converted; based on the singular perturbation strategy, the order reduction processing is performed according to the target variable characteristics; the voltage dynamic equation in the outer loop of reactive power control is processed, the reactive power decoupling is performed by introducing the voltage state variable into the active power control state equation, and the differential equation based on the phase angle relationship is established. Through the above mode, for the fast subsystem electromagnetic dynamic process, the electromagnetic dynamic state variable is processed based on the singular perturbation strategy, the voltage dynamic equation in the outer loop of reactive power control is processed, the high-order dynamic term is eliminated, the main dynamic characteristics in the state space are retained, the model order and the solving complexity are significantly reduced, so that the model order can be systematically reduced under the premise of ensuring the key dynamic characteristics and the stability analysis accuracy.

The application discloses a flexible connecting rod mechanical arm fixed time adaptive neural network composite control method, a flexible connecting rod mechanical arm model is decomposed into a slow subsystem and a fast subsystem based on a singular perturbation theory, and slow subsystem state variables and fast subsystem state variables are defined in the decomposition process, and the slow subsystem comprises unmodeled dynamics, and the control torque in the model is decomposed into a tracking controller for controlling the slow subsystem and a flexible suppression controller for controlling the fast subsystem. The technical scheme simultaneously reduces the elastic vibration of the flexible mode, realizes the tracking of the time-varying desired position of the mechanical arm, and also enables the mechanical arm to achieve fixed time stability.