442 results about "Lyapunov stability" patented technology

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention discloses a control method applied to self-piercing riveting, and relates to the technical field of self-piercing riveting control. According to the method, riveting material data are collected in real time through a multi-source data fusion interface, the high-dynamic deformation process of a riveting head is captured, deformation rate and rebound value characteristic values are extracted, and a multi-dimensional process parameter set is constructed. And comprehensively analyzing the hardness, thickness and plastic dissipation characteristics of the material, and establishing an anti-deformation capability evaluation function. By introducing the fluctuation characteristics of the punching force, the nonlinear relation between the punching force and the elastic deformation of the riveting rod is analyzed, a hybrid compensation model is constructed, and a riveting dynamic coupling compensation vector is output. And generating a compensation weight matrix through an adaptive optimization algorithm, and dynamically adjusting the stamping force and the displacement of the riveting rod in combination with an anti-saturation control strategy. And dynamically adjusting a gain parameter through multi-scale filtering and a Lyapunov stability criterion, and triggering iterative updating of a compensation weight parameter during out-of-tolerance so as to optimize a control effect. Riveting precision and stability are improved, and the service life of equipment is prolonged.

The invention discloses a six-rotor unmanned aerial vehicle control method and system with disturbance and performance constraints. The method comprises the steps that 1, a six-rotor unmanned aerial vehicle power model is built; 2, errors are determined based on the dynamic model, functions are combined, and a finite time scheme is determined; step 3, based on the dynamic model, establishing an observer, and knowing a disturbance state; step 4, a performance control scheme and an unknown disturbance state are stipulated in finite time, an error dynamic model is established, and the error dynamic model is established based on reinforcement learning; 5, setting a base scheme, setting a virtual controller and an actual controller, and setting an adaptive law for the base neural network; 6, the controller follower can track the leader, and tracking control is completed; establishing a controller by adopting a backstepping method, establishing a disturbance observer, and compensating negative effects; a reinforcement learning algorithm and a backstepping method technology are combined, and a controller is arranged to realize control; according to the method, the stability is proved by using a Lyapunov judgment stability theory, and the feasible effectiveness is verified by a simulation example.

The invention provides a multi-mode control method and system for a brushless doubly-fed motor, and relates to the technical field of motor control, and the method comprises the steps: extracting torque-rotating speed slopes and dynamic response time constants of two points based on a steady-state working point of a current mode and an expected working point of a target switching mode, and constructing a linear correction function; fusing an output value of the linear correction function with an optimization result to generate a modal transition compensation coefficient, and injecting the compensation coefficient into a rotor side voltage control ring based on a feedforward control channel to generate an anti-disturbance dynamic compensation control strategy; and for a non-smooth adjustment item in the dynamic compensation control strategy, performing smoothing processing on a discontinuous boundary of a control quantity by adopting a subgradient projection algorithm, verifying continuous conductivity of a control curved surface in combination with a Lyapunov stability criterion, and generating a final control instruction. According to the invention, adaptive smooth switching control of the brushless doubly-fed motor can be realized.

The invention relates to the technical field of power electronic control, and discloses a voltage and current stabilization and surge suppression cooperative control method for power equipment, and the method comprises the following steps: dynamically segmenting an output current frequency band, and remodeling the output impedance characteristics of a system; predicting and controlling a hierarchical optimization control target based on a multi-time scale model; dynamically switching an energy feedback path; identifying aging parameters of the device on line and adaptively adjusting a control strategy; global stability verification and fault-tolerant protection: verifying the stability of the control system based on the Lyapunov stability theory, and triggering a protection mechanism when the magnetic flux is saturated or the aging of the device exceeds the limit. By means of dynamic impedance frequency domain segmentation, the impedance characteristic of the system is divided into a low-frequency voltage stabilization area and a high-frequency surge suppression area according to frequency bands, the contradiction that in a traditional method, it is difficult to consider steady-state precision and transient surge suppression at the same time due to the single impedance characteristic is solved, and collaborative optimization of voltage stabilization control and surge energy blocking is achieved.

The invention provides a mechanical arm dynamic obstacle avoidance trajectory planning method based on a dynamic motion element and a steering force field, and belongs to the technical field of robots. Secondly, learning the teaching track preprocessed in the step 1 by using an improved dynamic motion primitive (DMP) algorithm, so that the DMP can generate a motion mode similar to the teaching track; through a depth camera installed around a mechanical arm or at a fixed position, obstacle information, shape and motion state are obtained in real time, obstacle avoidance repulsive force is calculated, steering force is quantified, and obstacle avoidance of free motion obstacles is achieved; a Lyapunov equation is constructed, based on the Lyapunov stability theory, the stability of the improved DMP algorithm is proved, and it is ensured that the motion trail of the mechanical arm in the dynamic environment is converged to a target point; and finally, carrying out an obstacle avoidance experiment to verify the dynamic obstacle avoidance capability of the mechanical arm. According to the invention, the trajectory generalization ability of the mechanical arm in a narrow working space is obviously improved.

A preset time robust sliding mode control method for a mechanical arm system comprises the steps that (1) under the condition that parameter perturbation, joint friction and external disturbance are considered, a dynamic model and a tracking error system model of the mechanical arm system are established; (2) a robust preset time controller is designed to ensure that the speed tracking error of the mechanical arm converges to zero within preset time; 3) designing a non-singular fast terminal sliding mode controller based on a preset time reaching law so as to ensure that a position tracking error can also converge to zero within the preset time; 4) the feasibility and effectiveness of the method are verified by using a Lyapunov stability analysis method; aiming at a mechanical arm system with parameter perturbation, joint friction and external disturbance, the invention develops a robust nonsingular fast terminal sliding mode control method in preset time so as to ensure that the mechanical arm system can accurately track an expected trajectory signal in the preset time, so that the tracking speed of a mechanical arm is improved, and the tracking precision is improved. And the tracking accuracy and safety of the mechanical arm are ensured.

The invention discloses a stable control method for a deformation process of an unmanned aerial vehicle in a complex environment, and relates to the technical field of unmanned aerial vehicle flight control. The state-space equation is used for describing flight dynamics parameters in the deformation process of the unmanned aerial vehicle in the complex environment; constructing a disturbance observer based on the state-space equation; the disturbance observer is used for observing a lumped disturbance quantity caused by a complex environment and wing tip folding deformation; constructing a sliding mode surface equation according to the attitude angle tracking error; constructing an attitude stability controller according to the disturbance observer and the sliding mode surface equation; and based on the Lyapunov stability theory, performing stable control on the unmanned aerial vehicle by using an attitude stability controller. The lumped disturbance quantity caused by the complex environment and wing tip folding deformation is considered during flight control of the unmanned aerial vehicle, and the control precision of the deformable unmanned aerial vehicle under the complex environment condition can be improved.

The invention discloses a cascaded auto-disturbance rejection rotating speed stability control method with a quasi-resonance link based on propeller load, and the method comprises the following steps: S1, building a speed ring mathematical equation containing disturbance according to the basic structure and working principle of a surface-mounted permanent magnet synchronous motor; s2, according to aerodynamic characteristics of the propeller, establishing a mathematical model of tension and load torque of the propeller; s3, establishing a quasi-resonance cascade active-disturbance-rejection speed controller containing disturbance compensation according to the S1 and the S2, and performing stability analysis on a cascade expansion observer equation according to a Lyapunov stability theorem; and S4, a cascade active disturbance rejection control method with a quasi-resonance link is used to realize suppression of periodic and non-periodic disturbance of the rotating speed so as to realize high-performance speed control. According to the method, the motor and the propeller are integrally modeled, the influence of periodic disturbance and non-periodic disturbance on the rotating speed is considered at the same time, and the method has better dynamic performance and steady-state performance.

The invention discloses a security boundary consistency control method for a multi-agent spatio-temporal dynamic system. The method specifically comprises the following technical steps: firstly, constructing a leader-follower spatio-temporal dynamic mathematical model of the multi-agent system based on a partial differential equation; then establishing an abnormal working condition mathematical model containing an actuator fault and a network spoofing attack; deducing to obtain an error dynamic system equation by defining a system consistency control target; constructing an energy function by using a Lyapunov stability theory, and deducing to obtain a system stability criterion; finally, a security boundary consistency control method is designed, and multi-agent space-time dynamic consistency control is achieved. The method effectively solves the problem of high cost caused by arrangement of actuators in a whole spatial domain in a traditional scheme and the problem that a multi-agent system is easily interfered by network attacks in a communication process, and has remarkable application value in engineering practice.

The invention relates to the technical field of digital control switching power supplies, and particularly discloses a switching power supply dynamic response adjusting system based on digital control, which comprises a digital controller, a multi-channel analog-to-digital conversion module, a parameter identification module, a self-adaptive control law adjusting module, a pulse width modulation driving module and a stability monitoring module. On-line identification of key parameters of a system is realized by constructing a nonlinear state space model and combining an extended Kalman filtering algorithm, dynamic compensation is carried out for non-minimum phase characteristics such as a right half plane zero point, and a self-adaptive control law adjustment module optimizes a control strategy in real time according to an updated model. A Lyapunov stability criterion and a predictive control mechanism are introduced, the robustness and response speed of the system are improved, and a stability monitoring module identifies an abnormal state through sliding window variance analysis and peak detection and triggers a fault-tolerant mechanism to guarantee safe operation of the system.

The invention relates to the technical field of attitude control, and discloses a method for realizing self-adaptive active leveling compensation, which comprises the following steps: acquiring real-time data of a vehicle, the data being provided by a plurality of sensors; processing the real-time data through a data fusion algorithm; according to the inclination state and the motion parameters of the vehicle, hydraulic supporting leg adjusting parameters are calculated through a multi-objective optimization algorithm; according to the calculated hydraulic supporting leg adjusting parameters, the action of the hydraulic supporting leg is adjusted through a PI D controller for vehicle balance recovery; the stability of the hydraulic supporting leg adjusting system is verified through the Lyapunov stability theory, and the hydraulic supporting leg adjusting system is used for enabling the adjusting process of the hydraulic supporting leg to be completed in all operation states; and the vehicle inclination state is fed back in real time. By fusing multi-sensor data and adopting a Kalman filtering or particle filtering algorithm, high-precision perception of the real-time inclination state and dynamic parameters of the vehicle is realized, and a more accurate and reliable vehicle attitude judgment effect is obtained.

The invention provides an optical storage multi-mode control method. The optical storage multi-mode control method comprises the following steps of 1, collecting multi-source data of photovoltaic output, energy storage SOC, load requirements and electricity price signals in real time; 2, dividing operation modes based on fuzzy logic, such as an economic mode, an emergency power supply mode and a peak value adjustment mode; 3, generating an optimal power distribution strategy in each mode through model predictive control (MPC); and step 4, designing a mode switching logic based on Lyapunov stability to realize non-impact transition, and relates to the technical field of photovoltaic power generation and energy storage system cooperative control, and through multi-source data fusion, fuzzy logic mode division, model prediction control and the Lyapunov stability switching logic, the mode switching logic based on the Lyapunov stability is realized. Self-adaptive efficient operation of the optical storage system under complex working conditions is achieved, the problems that a traditional control method is single in mode, lagged in response and low in energy utilization rate are solved, and the method is suitable for improving the economical efficiency and stability of the optical storage system.

The invention relates to a multi-machine cooperative dynamic balance driving method, system and device for a spiral lifting machine, and the method comprises the steps: carrying out the compensation of the time lag of an actuator through employing a non-linear state observation and time lag compensation combined method, and obtaining a feedforward control quantity; performing Lyapunov stability feedback processing on the vibration acceleration signal to obtain a vibration suppression control quantity; designing a multi-section S-shaped speed curve, and calculating a closed-loop feedback control quantity by adopting PID (Proportion Integration Differentiation) combined with a robust control algorithm; and according to the feedforward control quantity, the vibration suppression control quantity and the closed-loop feedback control quantity, multi-machine cooperative dynamic balance driving of the spiral lifting machine is carried out. According to the invention, a non-linear state observation and time delay compensation technology is adopted, so that the synchronization error is reduced to a millisecond level; the low-frequency vibration amplitude is reduced through dynamic feedback control designed based on the Lyapunov theory; the hydraulic impact is obviously reduced by adopting a multi-section S-shaped speed curve; external disturbance is observed in real time through Kalman filtering, feed-forward compensation is carried out, and the robustness of the system is greatly enhanced.

The invention discloses an unmanned agricultural machine path tracking preset performance control method and system, and the method comprises the steps: collecting the pose information of an unmanned agricultural machine, building an unmanned agricultural machine path tracking error kinematic model, and designing a finite time preset performance function based on the unmanned agricultural machine path tracking error kinematic model. The constraint is converted through nonlinear mapping; based on the Lyapunov stability theory, adopting a backstepping method to recursively design an actual control law, and according to the actual control law, performing inverse transformation on the input of the unmanned agricultural machine tracking control system in the error kinematics model of the unmanned agricultural machine tracking control system to obtain the front wheel steering angle of the unmanned agricultural machine. According to the method, convergence is completed within finite time, and transient control performance and steady control performance of a path tracking control system are considered.

The invention discloses a doubly-fed induction generator fault detection method combining a sliding-mode observer and a bilateral cumulative sum algorithm, and the method comprises the following steps: designing a doubly-fed induction generator rotor current sliding-mode observer based on the combination of a novel reaching law and a doubly-fed induction generator state equation; carrying out stability and convergence analysis on the designed novel reaching law by adopting a Lyapunov stability theory; and the fault detection method of the doubly-fed induction generator is designed in combination with the optimized bilateral accumulation sum algorithm. A rotor current sensor fault, an input interference voltage fault, a voltage drop fault and a stator turn-to-turn short circuit fault are introduced. The rotor current tracking performance of the designed sliding-mode observer under different doubly-fed induction generator faults is verified by using a residual value obtained by comparing a doubly-fed induction generator rotor current value estimated by the sliding-mode observer with an actual rotor current output value; and meanwhile, the reliability of the designed doubly-fed induction generator fault detection method based on the optimized bilateral cumulative sum algorithm is verified. The doubly-fed induction generator fault detection method combining the sliding-mode observer and the bilateral cumulative sum algorithm has the advantages of being high in tracking precision and accurate in fault state characterization, meanwhile, weak fault features can be effectively converted into digital signals to be output, and faults of the doubly-fed induction generator are detected.

The invention relates to the field of mine geological disaster monitoring and early warning, and discloses a mine geological disaster early warning method based on artificial intelligence, and the method comprises the steps: describing the dynamic change of a state variable and a multi-physics coupling effect in a disaster evolution process through constructing a kinetic model; carrying out discrete solution on the model by adopting a numerical method to obtain a disaster evolution path; a disaster instability critical point is accurately identified in combination with a bifurcation theory and Lyapunov stability analysis; designing an optimal early warning strategy by using a reinforcement learning algorithm, and dynamically selecting an alarm signal triggering opportunity; fusing monitoring data and a model in real time, and dynamically updating a disaster state and a risk level; and outputting an alarm signal and providing emergency suggestions. According to the method, real-time response of accurate disaster prediction and early warning is realized, and the mine geological disaster prevention and control capability is improved.

The invention relates to a heterogeneous neural network acceleration device based on cooperation of a DSP (Digital Signal Processor) and a memristor, which adopts a hybrid computing architecture design of a DSP and memristor cross array, such as dynamic precision switching, a real-time closed-loop feedback mechanism based on a Lyapunov stability theory, a self-adaptive cross-domain quantization module, hardware instruction set optimization and the like. The hybrid architecture has good balance performance and universality, stability is remarkably improved through conductivity monitoring, self-adaptive PWM pulse generation and quantization table updating, a higher signal-to-noise ratio is achieved at lower cost through combination of a self-adaptive cross-domain quantization module and noise reduction, hardware level optimization is achieved through customization of an instruction set, the hybrid architecture is more suitable for an HNN calculation task, and the method is suitable for the HNN calculation task. The energy efficiency ratio and the anti-drifting capability of the HNN acceleration circuit are improved, and the acceleration performance is improved.

The invention relates to an unmanned aerial vehicle trajectory tracking control method based on model prediction and preset performance constraint, and the method proposes to introduce Lyapunov stability constraint into a model prediction control framework and integrate a preset performance control mechanism, thereby achieving the unification of performance constraint and system stability analysis. Comprising the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; position errors and attitude errors are defined, a preset performance function is constructed, and errors with performance constraints are converted into unconstrained errors through error normalization and nonlinear transformation; establishing a model prediction optimization problem on the premise of considering input saturation and stability constraints; designing an auxiliary control law based on the transformation error to construct a stability constraint; it is proved that the control strategy can ensure that errors meet preset performance constraints and system local asymptotic stability. According to the invention, stable and reliable trajectory tracking control of the unmanned aerial vehicle system can be realized, and the method has high tracking precision and good dynamic performance.

The invention relates to a passive fault-tolerant control method and system for a tilt-rotor unmanned aerial vehicle based on a nonlinear variable parameter model. The method comprises the following steps: S1, establishing an actuator fault model; s2, constructing a longitudinal dynamic model of the tilt-rotor aircraft, and converting the longitudinal dynamic model of the tilt-rotor aircraft into a nonlinear variable parameter model of the tilt-rotor aircraft; s3, introducing an actuator fault model into the nonlinear variable parameter model to obtain a nonlinear variable parameter model containing an actuator fault; s4, designing a fault-tolerant controller by adopting a quadratic sum convex programming method; s5, based on the Lyapunov stability theory, establishing a fault-tolerant controller solvability condition of the nonlinear variable parameter system model containing the actuator fault, enabling the nonlinear variable parameter model to have exponential stability, and solving the solvability condition to obtain the fault-tolerant controller; and S6, applying the fault-tolerant controller to a flight control system of the tilt-rotor aircraft.

The invention discloses a driving information security management system and method based on a memristor neural network, and relates to the technical field of information security, and the method comprises the steps: S1, monitoring the synchronism of a response system and a driving system in real time, and collecting feedback signal data; s2, analyzing and designing a synchronous controller through a Lyapunov stability theory; s3, dynamically selecting an information encryption mode by using the memristor neural network according to the synchronization error, the feedback signal and the signal encryption level; s4, ensuring that the probability that the encrypted information is decrypted in the driving feedback interval is lower than a set threshold value; and S5, extracting and analyzing the historical encryption signal, and updating an information encryption mode and a network security level. Based on the Lyapunov stability theory, the synchronous controller is constructed, the signal encryption level is dynamically adjusted by using the memristor neural network, and high-level safety is ensured to be kept in the dynamic change process.

The invention discloses an air-ground cooperative system model prediction formation control method in an underground pipe gallery environment, and the method comprises the steps: constructing an air-ground cooperative system nonlinear model, and constructing an underground pipe gallery model; designing a high-order interference observer to estimate current unknown external disturbance, and establishing an interference prediction model to estimate future unknown external disturbance; a rolling optimization control strategy based on state prediction is designed, control signals of all followers are optimized according to the current state of the system and the predicted future state in each control period, and the tracking precision is ensured while the tracking precision is improved. Collision between followers, collision between the followers and the wall of the pipe gallery and obstacle avoidance between the followers and static and dynamic obstacles in the pipe gallery are achieved; and solving is carried out based on a distributed model prediction formation control algorithm combined with a Lyapunov stability theory, so that each follower keeps an expected formation configuration while tracking a leader trajectory. And the driving safety of the formation in the underground pipe gallery environment is ensured.

The invention belongs to the field of artificial intelligence, particularly relates to an agent strategy generation and online optimization method based on dynamic scene perception, and aims to solve the problems that in a dynamic environment, strategy response is slow, optimization is difficult under sparse rewards, and online learning is unstable. The method comprises the following steps: constructing a multi-modal fused dynamic scene semantic perception module, and generating a high-dimensional scene representation; historical behaviors and environment contexts are fused through an attention mechanism, and an initial probabilistic strategy is output; executing an action and collecting real-time feedback to form an experience tuple; performing incremental strategy updating by adopting a non-parametric Bayesian framework and combining with a multi-peak exploration operator; a Lyapunov stability criterion and a strategy distillation mechanism are embedded to guarantee convergence robustness. According to the scheme, strategy generation within 50 milliseconds is achieved, the convergence speed of sparse reward tasks is increased by three times, strategy fluctuation is controlled within 8%, the system availability reaches 99.5%, and the real-time decision-making capacity and group cooperation efficiency of an intelligent agent in a complex dynamic scene are remarkably improved.

The invention discloses a self-adaptive neural network continuous reaction kettle control method based on an observer, and the method comprises the following steps: constructing a kinetic model used for estimating the conversion rate of reaction components in a continuous reaction kettle and the change rate of reaction temperature, and calculating the change rate of the reaction temperature as the conversion rate of the reaction components and the change rate of the reaction temperature are unmeasurable system states; a state observer needs to be used for estimation; introducing a nonlinear mapping method to ensure that all states meet designed time-varying constraints; an unknown nonlinear dynamic function in a continuous reaction kettle is approximated by using a neural network, and a self-adaptive controller is designed based on a Lyapunov stability theory so as to deal with faults of an actuator and a sensor. According to the method, the internal states such as the conversion rate of reaction components of the continuous reaction kettle system can be estimated under the condition that the system state is unknown, the cost of the control system is reduced, and the efficiency is improved.

The invention discloses a whole-body collaborative voice control system and method for a humanoid robot, relates to the technical field of robots, and aims to solve the problems of semantic understanding superficial layer, action planning localization and safety control lagging in existing humanoid robot voice control. Through closed-loop cooperation of the multi-mode intention understanding module, the whole-body action planning module, the dynamic balance security arbiter and the microkernel security scheduler, a unified control architecture is constructed. According to the system, natural language instructions are deeply analyzed into whole-body motion constraints through a semantic-action mapping knowledge base, anthropomorphic collaborative motion trails are generated, pre-security verification is conducted through a boundary model based on Lyapunov stability, and finally hard real-time execution and full-cycle security guarding are achieved through a microkernel security scheduler. According to the method, direct conversion from natural language to safe, stable and anthropomorphic whole-body motion is realized, and the task execution capability and the interaction naturalness of the humanoid robot in a complex scene are remarkably improved.

The invention provides a magnetic suspension underwater robot path following control method, a magnetic suspension underwater robot path following control device, magnetic suspension underwater robot path following control equipment and a medium, and relates to the field of underwater robot path following control. The motion control bottleneck of a traditional underwater robot in a complex environment is broken through. In the aspect of dynamic modeling, interaction between magnetic force and fluid resistance is considered, and underwater motion characteristics of the robot are accurately described. A nonlinear control strategy combining the Lyapunov stability theory and sliding mode control is adopted, and a trajectory tracking controller with high robustness is designed to ensure that the robot can stably and efficiently operate along a predetermined trajectory in a complex underwater environment. The maneuverability and the operation precision of the underwater robot are remarkably improved, a more reliable solution is provided for the fields of ocean exploration, resource development, environment monitoring and the like, and huge application potential and market value are shown.

The invention provides a pinned pulse safety synchronization control method and system of a multi-coupling time-delay network, and the method comprises the technical schemes: building a coupling time-delay dynamic network model, constructing a mathematical description of a random spoofing attack, designing a pinned pulse safety controller, and deducing a Lyapunov stability discriminant. Through defining an error system of a following network and a target network, under a pinned pulse safety control framework, based on a pulse system theory, system stability is analyzed, an attack probability is described in combination with a Bernoulli random variable, a pinned pulse control node selection strategy is finally designed, and safety synchronous control is successfully realized. The mechanism can effectively extract time delay information with a stabilization effect in a continuous system, and fuses the time delay information into the design of an anti-attack containment pulse safety controller, so that the synchronization safety of the system is remarkably improved, the resource allocation efficiency is optimized, and the attack influence is weakened by utilizing inherent time delay.

The invention relates to a clamping force constraint backstepping sliding mode control method for an electronic mechanical braking system, and belongs to the related technical field of new energy automobile control. The method comprises the following steps of: establishing a state space model of the system by considering the nonlinear characteristic of the electronic mechanical braking system, and reasonably simplifying the state space model into a strict feedback form; a time-varying tangent barrier Lyapunov function (BLF) containing an error integral term is constructed, an exponentially decayed time-varying function is used as a constraint boundary of a clamping force tracking error, the integral term is introduced to eliminate a steady-state error caused by unmodeled dynamics, and the output constraint performance and tracking precision of a system are ensured; on the basis, a control law is designed by combining a backstepping method and non-singular fast terminal sliding mode control, and fast and accurate tracking of the braking clamping force is achieved; and proving the system stability based on the Lyapunov stability theorem. According to the invention, the braking response time and the clamping force control precision of the electronic mechanical braking system can be effectively improved.

The invention relates to the technical field of power electronics and direct current transmission, in particular to a two-level VSC-HVDC minimum direct current capacitance calculation method based on LMI. The method comprises the following steps: establishing a state equation comprising a main circuit and a controller dynamic model, converting a three-phase alternating current quantity into a direct current quantity under a synchronous rotating coordinate system by utilizing Park transformation, and deducing a system state equation in combination with a Kirchhoff's law; performing Taylor expansion and linearization on the nonlinear equation, and constructing a small signal model based on a Lyapunov stability theory; by separating a diagonal matrix and defining a stability criterion in an LMI form, minimum direct-current capacitance solving is converted into a convex optimization problem, a critical value is iteratively searched by adopting a dichotomy method to determine minimum capacitance, and meanwhile, a tolerance mechanism is introduced to improve the robustness of an algorithm. Through combination of mathematical modeling and an optimization algorithm, the calculation efficiency and accuracy are effectively balanced, and a theoretical basis is provided for reducing the cost and the size of the converter.

The invention discloses a multi-agent sliding mode safety control method based on dynamic event triggering under hybrid network attack, and the control method comprises the following steps: building a multi-agent system model comprising spoofing attack and DoS attack; in order to cope with hybrid network attacks, an integral sliding mode surface function is constructed, an online estimation method is provided, and an unknown network attack mode can be estimated in real time in the absence of prior information; a novel self-adaptive sliding mode controller is developed, and the accessibility of a preset sliding mode surface is ensured; by introducing an auxiliary variable, a dynamic and static event triggering mechanism in the framework is considered, so that the use of communication resources is minimized; a Lyapunov stability theory and a finite time method are utilized to analyze the convergence of the multi-agent system under the control strategy. According to the control method provided by the invention, the influence of spoofing attack and DoS attack on the system can be eliminated, so that the state of the multi-agent system is consistent within finite time.