43 results about "Lyapunov stability" patented technology

This invention provides a method for reconstructing actuator faults in a heterogeneous multi-agent integrated energy system, relating to the field of fault diagnosis. The invention establishes a unified dynamic model for the agents; defines intermediate variables and constructs their dynamic equations; builds a global error dynamic system for distributed intermediate observers and agents; derives the stability criterion for the global error system based on Lyapunov stability theory, obtaining the state observer gain matrix for state estimation; loads the state observer gain matrix and the gain matrix for state estimation into each agent; and during the real-time operation of the agents, obtains the current state vector estimate and intermediate variable estimate, and calculates the reconstructed actuator fault value. This invention utilizes an output space coupling mechanism, enabling distributed collaboration between different energy networks without requiring a unified model dimension, thus breaking down the physical barriers to actuator fault reconstruction in integrated energy systems.

The application discloses a kind of anti-network attack's multi-vehicle safety platoon control method and system, the method first establishes multi-vehicle queue dynamics model;Second, design fuzzy state observer to estimate vehicle state and unknown nonlinear dynamics;Then, introduce funnel function to build time-varying performance boundary, and the double constraints of vehicle anti-collision and keep communication are converted into funnel constraints of spacing tracking error;Then, based on backstepping method and funnel performance error, design virtual controller and adaptive law, and obtain adaptive safety controller to obtain control input;Finally, through Lyapunov stability analysis, the tolerance threshold condition that system can still maintain final consistent bounded stable after suffering replay attack is quantitatively obtained.The application simplifies design through funnel control, avoids complex barrier lyapunov function, and for the first time clearly provides quantified anti-interference ability to replay attack, enhances the network security and practicality of multi-vehicle queue system.

This invention discloses a method for improving the synchronization stability of a grid-connected inverter based on backstepping, comprising: setting a low-pass filter in the active power-frequency droop control loop of the grid-connected inverter; establishing a corresponding state model based on the dynamic characteristics of the low-pass filter; constructing intermediate state variables according to the state model; and recursively deriving the control input expression based on the intermediate state variables using the backstepping control method to obtain a control law that satisfies the Lyapunov stability condition; introducing the control law into the active power-frequency droop control loop to adjust the output frequency of the grid-connected inverter, thereby optimizing the active power-frequency droop control strategy of the grid-connected inverter. This invention only requires superimposing the designed controller on the synchronization loop to effectively enhance the dynamic stability of the system and avoid instability and oscillations caused by factors such as temperature drift and component aging.

The application discloses a model-unknown unmanned vehicle cluster system identification and self-learning cooperative control method and relates to the technical field of multiple unmanned systems. Firstly, aiming at the complete unknown system model, the application uses a reinforcement learning and data-driven control joint use to design a reinforcement learning system model identification and reconstruction method. Secondly, an optimal cooperative control scheme based on reinforcement learning is designed, and an online adaptive learning cooperative control algorithm depending only on online state information and input information is proposed to learn the optimal control strategy. Under the action of the optimal control strategy, the follower unmanned vehicle can realize optimal tracking of the leading unmanned vehicle through Lyapunov stability analysis method. The model identification and reinforcement learning cooperative control method provided by the application completely relaxes the requirement of vehicle dynamics in the traditional tracking control protocol and realizes efficient cooperative path tracking control.

The application discloses a kind of asymptotic stability attraction domain calculation method applied to thermal power combined market, the calculation method includes the following steps: based on game theory, establish energy supplier production strategy model, terminal user optimal energy purchase model and transaction center market clearing model;According to the optimal strategy model constructed above, design dynamic market transaction mechanism and based on KKT condition and gradient method depict the optimal economic behavior model of each market subject;Based on the optimal economic behavior model established above, the state space equation of thermal power combined market is constructed;According to Lyapunov stability theorem, derive the attraction domain calculation formula.The calculation method of the application describes economic behavior based on game theory, converts economic behavior into state space equation describing market operation, and then derives the explicit expression of the attraction domain, which shows the influence of system parameters on the size of the attraction domain, can quantify the robustness of the market to maintain stable operation, and has high academic value and engineering guiding significance.

The application provides a deep network-based MSWI process ignition point temperature control method, which comprises the following steps: taking a key manipulated variable as an input and an ignition point temperature as an output, establishing an ignition point temperature controlled model by using a linear regression decision tree with interpretability, and simulating and predicting the ignition point temperature in an actual combustion system; calculating an error between a predicted value of the ignition point temperature and a set value, taking the error and an error change as inputs, using an LSTM controller to extract time sequence characteristics of the error information, and outputting a corrected feeding speed, so that the ignition point temperature output by the model can track the set value; meanwhile, each gate weight and a bias parameter of the LSTM controller are adaptively updated by introducing Lyapunov stability constraints, so that stable calculation of a control increment of the feeding speed is realized at each sampling time. The method can ensure stable adjustment of the ignition point temperature without an accurate system model.

A constructive certificate-based neural network multi-agent consensus control method includes: 1) constructing a distributed controller based on a residual neural network, employing parallel linear shortcut branches and nonlinear residual branches to enhance nonlinear transient regulation capability while maintaining baseline stability; 2) establishing a stability constraint mechanism based on constructive certificates, calculating the upper bound of the Lipschitz constant of the residual branches in real time during training, and dynamically adjusting the shortcut gain accordingly to ensure that the stability margin of the closed-loop system is always positive; 3) performing end-to-end training based on differentiable simulation, combining spectral pruning techniques and a comprehensive loss function to jointly optimize controller parameters. Experiments show that this invention achieves comprehensive performance comparable to the optimal linear controller without requiring manual grid search for parameters, significantly reducing control energy consumption, and providing rigorous Lyapunov stability theory guarantees throughout the training process.

This invention discloses a time-delay compensation control method for wave energy converters, belonging to the field of wave energy converter control technology. Based on the physical model of the wave energy converter, this method establishes the dynamic equations of the wave energy converter system. After defining the maximum power control problem of the wave energy converter, the dynamic equations of the wave energy converter system are transformed into a second-order linear system form, obtaining the system state variables, system parameters, and input time delay. After constructing an auxiliary system, an error variable is defined. Based on the error variable, a virtual control law is designed, and then an actual control law is constructed. By constructing the characteristic equation, the critical time delay threshold of the wave energy converter system is calculated. Lyapunov stability analysis proves that the system tracking error converges to a preset small neighborhood, thus completing the time-delay compensation control method for the wave energy converter. This invention can ensure that the critical time delay threshold of the system is always greater than the actual input time delay by adjusting the control parameters, guaranteeing system controllability.

This invention discloses a low-carbon scheduling method for airport refueling trucks based on an enhanced backpropagation (BP) neural network. The method includes: acquiring airport network topology information and vehicle operation status data; establishing a low-carbon optimization scheduling model for airport refueling trucks; proposing a method based on an enhanced BP neural network to transform the multivariate parameter optimization problem of the model into three single-variable sub-optimization problems; initializing three single variables; sequentially updating the three single variables; using the updated three single variables as input to the enhanced BP neural network; iteratively optimizing until the convergence condition is met to obtain the weight vector; using Lyapunov stability theory and mathematical derivation, proving the stability and convergence of the proposed enhanced BP neural network method; and proposing a refueling truck path scheduling model based on an enhanced BP neural network, introducing a lower bound function for the learning rate to avoid premature convergence of the adaptive gradient descent algorithm and improve the prediction accuracy of path scheduling.

This invention discloses a dual non-fragile control method for non-cooperative spacecraft based on electromagnetic capture, comprising: S1, establishing a dynamic model of the relative motion of the non-cooperative spacecraft under electromagnetic capture based on the T-H equations; S2, considering comprehensive disturbances and rewriting the dynamic model into state-space form; S3, designing a dual non-fragile observer / controller to address the coexistence of additive / multiplicative gain perturbations in the observer and controller; S4, substituting the dual non-fragile observer / controller into the state-space equations in step S2 to establish a state-space model of the closed-loop electromagnetic capture system; S5, deriving sufficient conditions for the system to be uniformly eventually bounded based on the LMI method and Lyapunov stability theorem, and solving for the observer / controller parameters in step S3. This invention fully considers the complex situation of coexistence of additive / multiplicative gain perturbations in the observer and controller, and can solve the electromagnetic capture control problem of non-cooperative spacecraft under multi-source disturbances such as external interference, elliptical orbit eccentricity, and unknown mass.

A kind of robust H∞ control strategy suitable for forklift linear control drum brake system, specifically includes the following implementation steps: 1) the mechanical structure and its dynamics characteristics of forklift linear control drum brake system are analyzed, and then the dynamics model is constructed;2) according to the model established in step 1, an adaptive robust H∞ control gain K is designed to ensure that the system state can quickly tend to be stable;3) based on the control gain K designed in step 2, a robust H∞ control algorithm for forklift linear control drum brake system is designed;4) based on Lyapunov stability theory, it is proved that the control algorithm designed in step 3 can make the system state stable convergence, and numerical simulation is carried out in MATLAB / Simulink environment to verify the actual control effect of the control algorithm in the brake system. The control accuracy and robustness of forklift linear control drum brake system can be significantly improved.

The present application relates to a model prediction and preset performance constraint based unmanned aerial vehicle trajectory tracking control method, which introduces Lyapunov stability constraint in the model prediction control framework and integrates preset performance control mechanism, thereby realizing the unification of performance constraint and system stability analysis, including the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; defining position error and attitude error and constructing preset performance function, converting error with performance constraint into unconstrained error through error normalization and nonlinear transformation; establishing model prediction optimization problem under the premise of considering input saturation and stability constraint; designing auxiliary control law based on transformed error to construct stability constraint; proving that the control strategy can guarantee that the error meets preset performance constraint and system local asymptotic stability. The present application can realize stable and reliable trajectory tracking control of unmanned aerial vehicle system, and has high tracking precision and good dynamic performance.

The application provides an aero-engine optimal safety control method under time-varying temperature boundary, and relates to the technical field of aero-engine control. The method considers safety boundaries faced by multiple controlled variables in the working process of the aero-engine, and can process the aero-engine safety control problem under multiple safety constraints. Firstly, multiple continuous differentiable functions are designed according to a control target, different relative orders of the continuous differentiable functions for the aero-engine system are considered, a soft minimum function is used, and a control barrier function is used to ensure the safety of the system; the second method in the Lyapunov stability theory is used to ensure that the error system of the aero-engine researched realizes asymptotic tracking; and a corresponding performance index is constructed to realize optimization while meeting the constraint conditions.

The application provides a mobile robot self-learning robust control method based on a hybrid Kalman filter and belongs to the technical field of robots.The technical scheme comprises the following steps: step 1, a dynamic model of an uncertainty factor;step 2, real-time estimation of state parameters and model parameters of the mobile robot to optimize the original dynamic model;step 3, introduction of an adaptive forgetting factor recursive least square method to dynamically adjust filter parameters;step 4, design of a non-singular fast terminal sliding mode controller based on a saturation function as a system main controller;step 5, establishment of an error correction RBF neural network and introduction of an adaptive error learning strategy;step 6, completion of stability analysis and proof of the control system by means of Lyapunov stability theory and simulation experiment of the optimized model.The application effectively suppresses the chattering of the sliding mode control and significantly improves the control precision and robust performance of the four-wheel robot.

The application provides a hydraulic anchor drill global sliding mode displacement control method and system based on PSO and adaptive ESO, and relates to the technical field of coal mine roadway supporting equipment control. The method comprises the following steps: obtaining state variables of a hydraulic anchor drill displacement system; constructing an adaptive extended state observer based on the state variables, estimating system total disturbance in real time, and outputting a total disturbance estimation value; constructing a pre-compensation type global sliding mode surface based on the state variables, and constructing a global sliding mode control law based on Lyapunov stability theory; introducing the total disturbance estimation value into the global sliding mode control law to construct a controller and output a control input variable; constructing a target function, optimizing and setting the controller parameters to obtain an optimal parameter combination; and applying the controller to the hydraulic anchor drill displacement system based on the optimal parameter combination to realize closed-loop control of displacement. The application enhances control precision and system robustness, and improves the safety and reliability of coal mine roadway supporting operations.

This invention discloses a disturbance-resistant cubic sliding mode position control method for a deep-sea robot drive motor. The method includes acquiring the actual operating parameters of the deep-sea robot drive motor and reconstructing the generalized lumped disturbance; estimating the generalized lumped disturbance of the motor under deep-sea operating conditions in real time; constructing a cubic nonlinear sliding mode surface containing linear and cubic error terms; designing a sliding mode reaching law based on a saturation limiting function and performing feedforward compensation in conjunction with the generalized lumped disturbance estimate to calculate the final q-axis current control command; and determining the robust gain coefficient of the lumped disturbance observer and the switching gain condition of the sliding mode controller based on Lyapunov stability theory. This invention effectively solves the problem of position tracking performance degradation caused by parameter perturbations and load mutations under complex deep-sea conditions. Without adding a traditional low-pass filter, it effectively overcomes steady-state phase lag and improves the dynamic response speed, tracking accuracy, and robustness of the drive motor position servo system.

The application provides a wind turbine power smoothing method based on hybrid weighted filtering and self-disturbance variable pitch, and belongs to the technical field of wind power generation control. The technical problems of wind turbine output power fluctuation under high wind speed turbulence and wind speed sudden drop turbulence conditions are solved. The technical scheme comprises the following steps: step S1: establishing a nonlinear mathematical model of a permanent magnet direct drive wind power generation system; step S2: generating a smooth power reference instruction; step S3: calculating a corresponding smooth reference speed instruction; step S4: adopting a linear self-disturbance variable pitch control algorithm based on Lyapunov stability; and step S5: realizing comprehensive power smoothing of the wind turbine under complex turbulence conditions. The application overcomes the insufficient adaptability of the fixed bandwidth controller under time-varying conditions, and significantly improves the power smoothing effect and operation reliability of the wind turbine under complex turbulence.

This invention discloses a spherical joint variable impedance control method based on force field perception and biomimetic strategy learning, belonging to the field of robot control technology. The method includes: establishing a spherical joint dynamic model; constructing an adaptive control module based on Lyapunov stability in a Jacobi-free inverse Cartesian space; designing a perturbation observer based on angular momentum conservation and a recursive least squares force field parameter identification module with an adaptive forgetting factor; designing a retention rate regulator based on the difference between estimated and adaptive impedance to achieve intelligent impedance modulation; designing a biomimetic strategy learning mechanism to use historically optimal parameters for repetitive task initialization; and conducting simulation verification. This invention improves computational efficiency and singularity robustness through inverse-matrix Cartesian space impedance adaptation, solves overfitting problems through intelligent retention rate regulation, and achieves human-like learning-saving characteristics through an explicit memory mechanism, significantly enhancing the robot's adaptability to dynamic and unknown environments.

The application discloses a multi-harmonic suppression method for a dual three-phase permanent magnet synchronous motor, and comprises the following steps: S1, a model of a dual three-phase permanent magnet synchronous motor driving system is established; a six-dimensional voltage vector is constructed through a switching state generated by a voltage source inverter, and a stator voltage vector is decomposed by adopting a vector space decomposition method to establish a voltage equation; S2, multi-source harmonic distortion characteristics are analyzed; current harmonic components caused by phase group asymmetry, non-sinusoidal distribution of counter electromotive force and nonlinearity of the inverter are respectively identified and analyzed; S3, a harmonic suppression controller based on an online neural network assisted linear extended state observer (ONN-assisted LESO) is designed; a super-local model is constructed to describe stator current dynamics; an auxiliary neural network compensator is introduced; a weight adaptive law is designed in combination with Lyapunov stability analysis, a forward Euler method is adopted to realize discretization control, and a voltage reference value is generated to realize real-time suppression of multi-harmonics. The application can effectively realize multi-harmonic suppression under different operating conditions, and meanwhile, fast dynamic response and robust current performance are maintained.

The application discloses an event-triggered multi-robot arm system random pre-defined time control method, belongs to the technical field of control, and is used for time control of a multi-robot arm system. The method comprises a dynamic equation of the multi-robot arm system, introduces a trajectory error tracking system, and obtains consensus error of the multi-robot arm system. Unknown functions in the dynamic equation of the multi-robot arm system are estimated, a virtual control signal, an adaptive rate and an event-triggered mechanism are designed according to an adaptive backstepping method, and stability analysis is performed in combination with Lyapunov stability theory. The application simultaneously adopts the event-triggered mechanism, reduces the update frequency of the controller, and in combination with experiments, the consensus error of the multi-robot arm system can converge into a small neighborhood within a pre-defined time of 1s.

The application belongs to the technical field of direct-current micro-grid control, and specifically discloses a method and system for improving the plug-and-play performance of a network-constructed converter in a direct-current micro-grid; the method adds an additional controller Q on the basis of the original PI double closed-loop controller of each network-constructed DC-DC buck converter in the direct-current micro-grid, and establishes a state space model of a multi-converter interconnection control system containing the additional controller. The system stability condition is decomposed through Lyapunov stability theory, the gain parameters of the Lyapunov matrix and the additional controller Q are designed, and the overall stability of the system is ensured. Meanwhile, the gain of the additional controller Q is optimized, the response performance of the distributed power supply is improved, and the amplitude of the control signal is limited. The application also discloses a corresponding control system, a computing unit and a computer readable storage medium.

This invention discloses a sliding mode control method based on a neural network adaptive observer, belonging to the field of hydraulic position servo system control technology. The method involves: establishing a nonlinear mathematical model of the hydraulic servo system; designing a state observer based on a radial basis function neural network to estimate the states in the system, and designing the system's sliding mode control law using the observed parameters; designing a neural network adaptive law using Lyapunov stability theory; smoothing the controller output using a filter; and selecting appropriate controller hyperparameters to enable the system to achieve the desired control effect and accurately track the desired signal. The control strategy designed in this invention can estimate nonlinearities that are difficult to model online, accurately observe the system state, exhibit good steady-state performance, and is easy to apply in engineering.

The application provides a detection and defense method and system for a DoS attack on a power information physical system, a power information physical system model is established based on a power flow equation of a power system and a swing equation of a generator; a MAFFKF algorithm is used on a sensor side to perform state estimation on system operation, and a residual detection algorithm is designed according to the state estimation result to realize attack detection; an MPC algorithm is used on a controller side to calculate future control signals by using the most recently received measurement signals, and to compensate for data loss caused by a DoS attack; the functions of attack detection and prediction control of the power information physical system under a DoS attack are realized. The application proposes a KF algorithm based on multiple fading factors, a detection method, a control algorithm based on MPC, and a prediction control sequence is designed based on Lyapunov stability theory and LMI, and the problem of system instability caused by data loss is solved, and the stability of the system is improved.

The present application relates to permanent magnet synchronous motor technology field, especially the PMSLM motor time delay control method, system and medium based on adaptive gain power, the dynamics order model of the position-current double closed loop system of PMSLM motor is established, the position tracking error is defined and the position error dynamics equation is derived, the expected position tracking error dynamics is designed, and the time delay equivalent control law is solved based on the equivalent control idea, the system lumped uncertainty is estimated and filtered on line by using time delay estimation method, the effective compensation of parameter uncertainty, external disturbance and unmodeled dynamic is realized, and the time delay control law under fixed gain is obtained, the adaptive gain dynamics based on Lyapunov stability analysis is introduced, and the on-line adaptive adjustment of control gain is realized.The present application realizes the adaptive gain dynamics PMSLM motor servo control and optimization, and significantly improves the position tracking accuracy, robustness and working condition adaptability of the motor.

The application discloses an active suspension control method based on optimal backstepping, which comprises the following steps: 1, introducing a performance index function into a backstepping control framework, solving an HJB equation and an HJB estimation equation based on an Actor-Critic structure reinforcement learning method; 2, designing an adaptive update law through a negative gradient of Bellman residual square, respectively constructing an Actor network for executing a control behavior and a Critic network for evaluating system performance, and designing a virtual control law and an actual control law as optimal solutions; 3, designing a Lyapunov function based on a tracking error; the application introduces a performance index function into a backstepping control framework, solves an HJB equation based on an Actor-Critic structure reinforcement learning method, designs an adaptive update law through a negative gradient of Bellman residual square, respectively constructs an Actor network for executing a control behavior and a Critic network for evaluating system performance, so that the virtual control law and the actual control law can both approximate optimal solutions of a subsystem, and optimal control of an active suspension system under Lyapunov stability is realized.

This invention provides an active vibration isolation method for cold atom gravimeters based on an adaptive model, relating to the field of active vibration isolation technology for cold atom gravimeters. The method includes the following steps: establishing an active vibration isolation reference model for the cold atom gravimeter; designing a proportional-derivative feedback control law; designing a Lyapunov stability function; and designing an adaptive law to address the problems of ground vibration and random vibrations caused by the natural environment encountered by the cold atom gravimeter during measurement. Through the adaptive model-based control method, the vibration velocity and displacement of the active vibration isolation system of the cold atom gravimeter converge rapidly, thereby improving the control accuracy of the active vibration isolation system.

The application discloses an inverted pendulum preview tracking control method and system based on double nonlinear constraints, and belongs to the field of robot control. The method establishes an inverted pendulum nonlinear model, and the nonlinear term satisfies the one-sided Lipschitz and quadratic inner bounded condition; a preview expanded error system is constructed, and the tracking problem is converted into a stability problem; a state feedback or static output feedback controller architecture is selected according to the measurable information type of the system; the Lyapunov stability theory and double nonlinear constraint lemmas are used to realize the asymptotic stability and H ∞ The disturbance attenuation performance requirement is converted into a linear matrix inequality, and the optimal feedback gain and preview compensation gain are obtained by solving; finally, the gain is configured in a control unit, and a control instruction is generated in combination with real-time feedback and preview information. The application reduces the conservativeness of nonlinear control, eliminates the phase lag of time-varying signal tracking, adapts to the composite output working condition, and significantly improves the tracking accuracy and robustness of the inverted pendulum.

The application discloses a PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification, which comprises the following steps: step 1, a current loop model of a PMSM in a d-q axis coordinate system is established based on actual control system motor parameter variation or disturbance, and a first-order differential of a q-axis stator current and a first-order differential of a d-axis stator current are obtained; step 2, an ANTSMC is constructed by defining a current loop error, the ANTSMC comprises an adaptive reaching law and a non-singular terminal sliding mode surface, derivatives of the two are obtained in parallel, and a q-axis output voltage and a d-axis output voltage are obtained, the current loop of a permanent magnet synchronous motor field vector control is controlled through the ANTSMC, and the system stability of the ANTSMC is proved through a Lyapunov stability criterion; and step 3, a parameter identifier based on an Adaline neural network is used to identify actual motor parameters of the current loop model of the PMSM and then applied to the ANTSMC.

This invention discloses a high-order all-drive motor modeling and control method based on a strict feedback structure, belonging to the technical field of motor control. The method includes: establishing fundamental electrical-mechanical-force coupling equations to describe the coupling relationship between the electrical subsystem, mechanical motion subsystem, and electromagnetic thrust subsystem; performing Taylor expansion on the fundamental coupling equations and constructing coupled dynamic equations by combining instantaneous electric power; converting the coupled dynamic equations into a high-order strict feedback nonlinear system model; converting the high-order strict feedback nonlinear system model into a high-order all-drive model; constructing a trajectory tracking control law; solving for the feedback gain vector using a parameterized configuration method; and performing Lyapunov stability analysis on the all-drive closed-loop LSRM system to ensure system stability and tracking performance. This invention provides a unified modeling of the high-order nonlinear coupling relationship of linear switched reluctance motors, effectively preserving the physical characteristics of the system, improving model accuracy, and expanding the applicable operating conditions of the system.