472 results about "Lyapunov function" patented technology

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention discloses a space robot finite time anti-interference trajectory tracking control method based on performance constraint and actuator saturation, and the method comprises the steps: firstly solving a state-space equation of a flexible joint free flight space robot, constructing a system error vector in combination with a finite time instruction filter and an anti-saturation auxiliary system, and carrying out the finite time instruction filter and anti-saturation auxiliary system; a novel asymmetric logarithmic obstacle Lyapunov function with an asymmetric fixed time constraint boundary is constructed; and finally, a novel finite time advanced activation anti-saturation auxiliary system and a finite time adaptive controller with a performance constraint effect are constructed, so that finite time anti-interference trajectory tracking control of the space robot is realized. According to the method, the influence of sudden disturbance is considered in the design of the flexible joint free flight space robot controller for the first time, and under the limitation of the saturation performance constraint of the actuator, the trajectory tracking rapidity and accuracy of the space robot are ensured; and the influence of sudden disturbance in a space robot system on the trajectory tracking transient performance can be effectively inhibited.

The invention relates to the field of fault diagnosis, and discloses a drying machine fault diagnosis method based on deep reinforcement learning, which is used for improving the accuracy and control efficiency of drying machine fault diagnosis. A physical dynamic model fusing thermodynamics and mechanical dynamics is constructed, real-time quantitative evaluation is performed on system stability in combination with a Lyapunov function, and a fault propagation path is visually displayed by using a directed topology network. Firstly, dryer multi-source parameters are obtained to establish a dynamic model; positioning a fault source component and calculating the network influence degree of the fault source component; and a self-adaptive control instruction and a topology suppression instruction are generated, control parameters are dynamically adjusted, fault propagation is suppressed, the control instruction is converted into an actuator driving signal, and accurate control over the drying machine is completed. Through deep fusion of a physical model and data driving, the fault diagnosis accuracy and the control efficiency are remarkably improved, the troubleshooting time is effectively shortened, the production safety is guaranteed, and the method is suitable for intelligent operation of the drying machine under the complex working condition.

The invention provides a distributed formation control method driven by a recursive balance network under a communication attack, and relates to the technical field of cooperative control of a multi-agent system, and the method comprises the steps: constructing a distributed multi-agent system model, and constructing an RBN controller heterogeneous communication network architecture and a DoS attack model; the RBN controller design is optimized, the distributed RBN controller architecture is realized, the stability of the RBN controller is analyzed based on the shrinkage mapping theory, and the exponential convergence of the system state difference is proved by constructing a Lyapunov function; training the optimized RBN controller by adopting a difficulty priority confrontation training strategy, designing a multi-objective loss function, and dynamically adjusting the training weight of each difficulty level; and simulating the distributed multi-agent system optimized by the above steps, testing the robustness of the distributed multi-agent system under incremental DoS attack intensity, and performing comparative analysis of heterogeneous and isomorphic communication network configuration and comprehensive performance comparison of an RBN controller and a traditional MPC method.

The invention provides a mechanical arm predefined time trajectory tracking control method and system based on time delay estimation, and relates to the technical field of multi-degree-of-freedom mechanical arm trajectory tracking control. Constructing a predefined time error conversion function to define a system error and conversion error system, designing a barrier function and a feedback control rate based on the conversion error system, and designing a Lyapunov function to prove the stability of the system so as to track an expected trajectory; according to the method, time delay information is deeply fused, dependence on a complex model approximate structure is not needed, stable convergence of the mechanical arm system state within predefined time can be achieved when a mathematical model is not accurate, the system control performance and robustness are effectively improved, and the requirement of modern industry for mechanical arm high-performance control is met.

The invention relates to the technical field of power plant intelligent adjustment, and discloses a power plant load intelligent adjustment method and system. The method comprises the following steps: distributing and deploying sensors according to field equipment of a power plant, and constructing a distributed sensor network based on the sensors; establishing a single time scale control framework according to the distributed sensor network; a heterogeneous delay compensation mechanism is constructed based on a distributed sensor network and a single time scale control framework; constructing a local linear matrix inequality constraint condition of each node, obtaining an optimal solution of the local linear matrix inequality constraint condition according to a heterogeneous delay compensation mechanism, and then generating an optimal control strategy of each node; the method comprises the following steps: constructing a global Lyapunov function based on a distributed sensor network and a single time scale control framework; and the change rate of the global Lyapunov function is monitored in real time, and corresponding equipment is intelligently and dynamically adjusted according to the optimal control strategy. The method achieves the intelligent regulation and control of the power plant load, is good in stability, is high in adaptability, and is short in regulation and control period.

The invention discloses a nonlinear multi-agent system sampling consistency control method and system and a medium, and the method comprises the following steps: S101, obtaining system model parameters, including a nonlinear function # imgabs0 #, a pulse gain # imgabs1 #, a sampling period # imgabs2 #, a communication topological graph # imgabs3 # and a leader-follower kinetic equation; s102, verifying an assumed condition; s103, constructing a Lyapunov function # imgabs4 #, and analyzing the stability of an error system in combination with pulse disturbance; s104, deriving a consistency condition through a Lyapunov method and a pulse theory, calculating key parameters, and determining a pulse gain and a sampling period constraint; s105, selecting parameters meeting conditions; s106, constructing a sampling controller containing pulse disturbance; and S107, implementing the controller and performing numerical simulation. According to the invention, the anti-pulse disturbance capability is strong: through the design of the pulse gain # imgabs5 #, the instantaneous interference of an input channel is effectively counteracted, and the robustness of the system is improved; a sampling data control strategy is adopted, a control signal is updated only at a discrete sampling moment, and the communication burden is reduced.

The invention relates to the field of nonlinear system control, and provides a preset time reinforcement learning method and system of a continuous nonlinear system, and an electronic device, and the method comprises the steps: constructing a zero-sum game framework based on a kinetic model and a performance index of the nonlinear system; determining a value function and a Hamiltonian function based on a zero-sum game framework; applying a preset neural network model to carry out approximation on the value function, and determining an approximation error; based on the Hamiltonian function and the approximation error, an approximate optimal control strategy and a worst interference strategy are determined; constructing a Lyapunov function based on the value function and the weight error of the value function; and based on a Lyapunov function, an approximate optimal control strategy and a worst interference strategy, a reinforcement learning result is verified. The method and the device are used for overcoming the defects that convergence time cannot be dynamically adjusted, parameter complexity is high and robustness is insufficient in the prior art, and the scheme of the invention can meet dual requirements of a continuous nonlinear system on dynamic convergence and anti-interference performance.

The invention relates to the technical field of information monitoring, in particular to a remote monitoring method and system for intelligently sensing situation, vibration and orientation. Identifying and acquiring a dynamic construction behavior mode, constructing a space-time dependent topological graph of the dynamic construction behavior mode by monitoring a traceability pattern and an orientation tracking community, and inferring state potential energy of the space-time dependent topological graph according to evolution of the vibration sensing signal and the horizontal sensing data so as to determine a global construction sensing situation of the target monitoring area; and performing phase space evolution trajectory evaluation on the global construction perception situation through an evaluation phase space of a spatial evolution evaluation criterion to obtain a stable node pattern, and introducing a Lyapunov function to analyze whether the divergence degree of the stable node pattern interferes with the buried facility or not so as to control remote monitoring equipment to send out a monitoring early warning signal. According to the invention, all-around real-time monitoring can be carried out on construction behaviors around the buried facility, early warning of potential risks is realized through active perception, and the safety and operation efficiency of the buried facility are improved.

The invention discloses a reinforcement learning automatic driving decision control method based on a Lyapunov function, and relates to the technical field of automatic driving automobile safety control. The method comprises the steps that driving state information of a self-vehicle and driving state information of surrounding environment vehicles are received, the driving state information of the self-vehicle comprises the position, the speed, the acceleration and the course angle, and the driving state information of the surrounding environment vehicles comprises the position, the speed, the course angle and lane line information of the environment vehicles relative to the self-vehicle; and a decision control model of the automatic driving task based on reinforcement learning is constructed, and the decision control model takes the driving state information of the vehicle and the surrounding environment vehicles as output and takes the optimal automatic driving strategy as output. According to the method, the reinforcement learning strategy updating process is constrained through the stability condition proved by theories, the unification of safety and decision-making efficiency is realized in dynamic scenes such as highway lane changing and emergency obstacle avoidance, and the method has a good application prospect for training the safety and strategy stability of automatic driving by adopting reinforcement learning.

The invention discloses a VSG-DFIG subsynchronous oscillation adaptive suppression method based on a Hamiltonian model, and belongs to the technical field of subsynchronous oscillation suppression of a power system. The method aims at solving the problem that an existing method for suppressing subsynchronous oscillation existing in grid-connected operation of the doubly-fed induction generator is poor in suppression effect. Comprising the following steps: constructing a port controlled Hamiltonian dissipation model; constructing a closed-loop system dynamic equation based on interconnection and damping distribution under strict passivity; an expected energy function in a unit closed-loop form is constructed, a DFIG unit feedback control law based on a Hamiltonian model is obtained, and further deformation is carried out to obtain a deformed DFIG unit feedback control law; the port controlled Hamiltonian dissipation model is corrected, and a corrected port controlled Hamiltonian dissipation model is obtained; constructing a dynamic equation of the anti-interference adaptive observer, and establishing an error dynamic equation; and constructing a Lyapunov function, and designing an estimation error feedback control law according to the stability condition of the Lyapunov function. The subsynchronous oscillation suppression circuit is used for suppressing subsynchronous oscillation.

The invention belongs to the field of flight control, and particularly relates to a tilt-rotor aircraft transition state undisturbed switching control method based on linear active disturbance rejection. Longitudinal dynamics of the tilt rotorcraft is modeled into a switching system, a nacelle inclination angle is combined to serve as a switching signal, a switching active-disturbance-rejection controller is designed, internal / external disturbance of the system is estimated in real time and compensated, and multi-channel decoupling is achieved; and a variable gain continuous switching controller is further introduced, and a time interval is set and adjusted after a switching moment, so that jolt at the switching moment is inhibited, and the influence of controller switching on the working process of the conversion process of the flight mode of the tilt-rotor aircraft is effectively reduced. Based on a dynamic error system and a multi-Lyapunov function, in combination with an average residence time constraint, a linear matrix inequality condition for ensuring the asymptotic stability of the system is deduced, and a simulation experiment of the transition state of the tilt-rotor aircraft is carried out.

The invention relates to an aluminum pipe local conduction regulation and control method, system and device based on dynamic electromagnetic field modulation and a medium, and the method comprises the steps: driving an electromagnetic coupling array to scan a target conduction region of an aluminum pipe, so as to construct a three-dimensional dynamic electromagnetic model; analyzing and generating a multi-band excitation parameter combination in a bandwidth constraint range according to the target conductivity gradient parameter; the method comprises the following steps: driving an electromagnetic coupling array by a multi-band excitation parameter combination, collecting an electromagnetic response spectrum of a target conductive area in real time, extracting an eddy current characteristic component set, calculating an obtained phase compensation amount, and dynamically adjusting an excitation phase difference and a power ratio of the electromagnetic coupling array to form a controllable standing wave interference field; and solving the Lyapunov function gradient difference between the node distribution characteristics of the standing wave interference field and the predicted value of the three-dimensional dynamic electromagnetic model so as to iteratively update the multi-band excitation parameter combination. According to the invention, high-precision dynamic regulation and control of the conductivity gradient of the aluminum tube are realized, the energy consumption is effectively reduced, and the stability of a standing wave interference field is enhanced.

The invention relates to the technical field of unmanned aerial vehicle group intelligent control, in particular to a multi-agent unmanned aerial vehicle cooperative control method and system, and the method comprises the steps: obtaining multi-sensor data to estimate a relative pose, generating an environment occupation model and a path homotopy identifier, and building an adjacent set; based on a control barrier function and a control Lyapunov function, writing security and convergence constraints into a quadratic programming solution control instruction, and exporting a constraint dual multiplier, a barrier redundancy and a conditional risk value; constructing a task graph, carrying out bidding distribution according to energy consumption, time, safety cost and risk cost, generating a task intention identifier and taking the task intention identifier as a nominal reference trajectory; and triggering minimum change control correction and local redistribution through a sequential logic monitoring completion condition, a barrier redundancy and a synchronous residual error based on adjacency set evaluation. According to the invention, stable, safe and risk-controllable group collaboration is realized in a dense environment.

The invention provides a distributed driving vehicle preset performance fault-tolerant control method and system based on an adaptive neural network observer. The method comprises the following steps: constructing a vehicle dynamics model containing motor faults and unknown disturbance; in order to realize real-time estimation of faults and disturbance and break through the limitation on disturbance derivative boundaries, a self-adaptive neural network observer is designed; aiming at the problem that the control direction is uncertain, an upper-layer controller is designed based on a preset performance control theory, a Nussbaum function and an obstacle Lyapunov function are introduced, it is ensured that system dynamic errors meet preset performance constraints all the time, and accurate tracking control over the transverse force, the longitudinal force and the yawing moment of a vehicle is achieved; in order to improve the system coordination capability under the multi-motor fault working condition, an optimal torque distribution model fusing the motor torque constraint and the tire force limit is constructed, collaborative optimization control of the driving and steering system is realized through a quadratic programming algorithm, and the stability and safety of the whole vehicle in the fault state are improved.

The invention discloses a quadrotor unmanned aerial vehicle trajectory tracking control method, a quadrotor unmanned aerial vehicle and a storage medium, and relates to the technical field of unmanned aerial vehicle control. The method is technically characterized by comprising the following steps: firstly, providing a fast terminal sliding mode disturbance observer for unknown disturbance of a system so as to realize accurate estimation of the disturbance within preset time; then designing a preset time fast terminal sliding mode controller based on disturbance estimation information, and further weakening a buffeting phenomenon of the system by using a saturation function; finally, based on a Lyapunov function method, it is proved that the system can achieve global predetermined time stability; according to the method, predefined time convergence of the trajectory tracking error of the quad-rotor unmanned aerial vehicle under unknown disturbance can be realized, the upper bound of the stabilization time can be randomly preset in a physical limitation range, the rapidity of the system is ensured, buffeting is eliminated, and the stability of the system is improved.

The invention belongs to the field of automatic control, and particularly relates to a nonlinear system dynamic gain global trajectory tracking control method based on a self-adaptive neural network observer, which comprises the following steps: converting an uncertain nonlinear system model with unknown interference according to the requirement of output feedback control to obtain a first nonlinear system model; enabling an unknown nonlinear continuous function in the first nonlinear system model to be expressed as a function only containing an output signal and other system state estimation values; performing approximate approximation on an unknown nonlinear continuous function in the first nonlinear system model by using the first radial basis function neural network vector to obtain a second nonlinear system model; designing a second radial basis function neural network vector to construct a dynamic gain state observer of the second nonlinear system model, and obtaining an estimated value of a system state and an estimated value of an unknown nonlinear continuous function in the model; according to an inversion control method, defining a dynamic system tracking error index containing an intermediate virtual control signal, designing a Lyapunov function, and obtaining a change rate of a dynamic gain, a weight adaptive update rate of a second radial basis function neural network vector, and a control rate of the intermediate virtual control signal and a system input signal; different from most existing nonlinear system control methods, the method combines a novel dynamic gain neural network observer with an inversion controller with dynamic gain, and provides a robust global trajectory tracking control solution for a dynamic system operating under uncertain conditions.

The invention discloses a distributed anti-interference fault-tolerant control method and a distributed anti-interference fault-tolerant control system for a multi-six-rotor unmanned aerial vehicle attitude system. The control method comprises the following steps: constructing a multi-six-rotor unmanned aerial vehicle attitude model based on a multi-six-rotor unmanned aerial vehicle attitude system model and interference uncertainty; designing a disturbance observer which is used for estimating the degree of external disturbance and actuator faults; and based on the output of the multi-six-rotor unmanned aerial vehicle attitude model and the degree of external interference and actuator fault, constructing a Lyapunov function by using a backstepping recursion technology, and designing a distributed anti-interference fault-tolerant controller for generating a control output signal to the multi-six-rotor unmanned aerial vehicle attitude model. According to the method, the Lyapunov function is constructed, so that output signals of all followers and leaders are synchronous, and errors are converged. Meanwhile, the general approximation characteristic of the neural network is utilized, the nonlinear term and the unmodeled dynamic are approximated online, and the model complexity is reduced.

The invention discloses an exoskeleton robot fixed time control method based on output constraint and disturbance observation, relates to the field of robot control, constructs a fixed time neural controller (FTNC), and ensures that a system is stable in fixed time by combining a universal obstacle Lyapunov function (UBLF) and a preset performance function (PPF), so as to improve the system stability. The convergence time is irrelevant to the initial state; meanwhile, a radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO) are adopted to jointly compensate the uncertainty of the model and the man-machine interaction disturbance; by dynamically adjusting UBLF boundary conditions, joint angle errors are limited, and training safety is guaranteed. According to the method, a fixed time control theory is adopted, fixed time is combined with a preset performance function (PPF) and a universal barrier Lyapunov function (UBLF), and the output of the exoskeleton robot is strictly limited, so that the rapid convergence and transient performance requirements of an exoskeleton system are met at the same time.

A method includes utilizing a learning model, which has been trained to mimic the nominal control outputs of a model predictive control, to generate a nominal control output for a system that is an aircraft system having one or more system constraints. The method also includes, based on the nominal control output, satisfying each of one or more system constraints, implementing the nominal control output; and, based on the nominal control output not satisfying one of the one or more system constraints, utilizing a safety stability filter that includes a control barrier function (CBF) portion and a control Lyapunov function (CLF) portion to modify the nominal control output and obtain a modified control output, and implementing the modified control output in the aircraft system. A system is also disclosed.

The invention discloses a distributed consistency control method suitable for a multi-unmanned trolley system, and designs a control strategy based on a preset time disturbance observer for the problem that communication topology dynamic change and external disturbance interference are difficult to consider at the same time. In system modeling, each trolley is modeled as a second-order nonlinear system, and external disturbance is generated by a heterogeneous external source system. In the design of the controller, a disturbance observer with a preset time convergence characteristic is introduced, and strict convergence of disturbance estimation errors can be realized in finite time. Control input is fused with three parts of neighbor state difference feedback, leader tracking and disturbance compensation, and it is ensured that the system can still achieve a consistent target in a complex dynamic environment. By constructing a combined Lyapunov function and combining a Markov jump system theory, it is proved that a system state error and a disturbance estimation error converge to zero after a preset time. A simulation result verifies the effectiveness and stability of the method under communication switching and non-ideal disturbance interference.

The invention discloses a self-adaptive finite time fuzzy safety control method for a flexible joint mechanical arm, which comprises the following steps that a flexible joint mechanical arm state space model influenced by spoofing attack is established, and a control signal comprises an unknown time-varying parameter and an unknown bounded attack function; introducing a Nussbaum function meeting a preset limit integral property; designing a control method based on a finite time stability principle, and ensuring that a system state converges to a bounded residual set within finite time; a fuzzy logic system is adopted to approach an unknown nonlinear function in the mechanical arm model; defining an error variable through coordinate transformation, and constructing a parameter which exponentially decays along with time; constructing a Lyapunov function containing the exponential decay parameter, and designing a virtual control function, a controller and a parameter adaptive law based on the function; and the Nussbaum function is constructed to be used for processing the unknown time-varying parameters. According to the invention, the influence of spoofing attacks on state signals can be reduced.

The invention discloses a multi-missile self-learning differential game cooperative guidance method based on disturbance feed-forward compensation, and the method comprises the steps: providing a finite-time convergence sliding-mode nonlinear expansion state observer for solving the problems of unknown partial parameters and external disturbance in a multi-missile cooperative guidance system; a sliding mode control method is combined with an extended state observer, a novel fixed-time convergence sliding mode surface and a sliding mode reaching law are constructed, and real-time estimation and efficient compensation of unknown disturbance in a system are achieved. Strictly proving the fixed convergence time of the observer by using a Lyapunov function, and analyzing a disturbance estimation error; compared with a traditional ESO, the method has the advantages that the speed and precision of state tracking are remarkably improved, and the anti-interference capability of the multi-missile cluster in a complex interference environment is effectively improved.

The invention discloses a vehicle queue preset time output feedback safety control method and system, and belongs to the field of intelligent network connection multi-vehicle queue control, the control method comprises the following steps: designing a non-linear vehicle queue model, designing a fuzzy observer, combining with a fuzzy logic system to approximate an unknown non-linear function in the vehicle queue model, and calculating the preset time output feedback safety of the vehicle queue. Outputting an estimated displacement, an estimated speed, and an estimated acceleration of the following vehicle; mapping the error transformation of the finite time domain to an infinite time domain to obtain infinite time domain error transformation; designing a Lyapunov function, and performing derivation on the Lyapunov function to finally obtain a following vehicle brake input signal; designing a false data injection FDI attack model for injecting an attack signal into the following vehicle brake input signal to obtain an input with an attack; the invention provides a cooperative control architecture based on a time domain mapping mechanism, and tracking errors under any initial condition can be asymptotically converged to zero within preset time.

The invention discloses a design method of a barrier function rehabilitation robot safety tracking control system, and relates to the technical field of medical robots. The method comprises the steps that on the basis of an existing double-joint single-arm robot mathematical model, a mathematical model with uncertain model parameters, uncertain external disturbance and uncertain external force borne by the tail end is established, and the established model is closer to a real model of a rehabilitation robot; the uncertainty in the model is classified into one class, and a disturbance observer is used for estimating and compensating the uncertainty; a logarithmic obstacle Lyapunov function is adopted to constrain the joint angle; and designing an adaptive trajectory tracking controller based on the disturbance observer and the obstacle Lyapunov function method, and tracking a set trajectory. The high-precision tracking of the target trajectory is ensured, the actual motion trajectory always moves within a certain range on the two sides of the target trajectory, and the secondary injury of the patient in the rehabilitation process is avoided.

The invention relates to the technical field of control and information, and particularly discloses a distributed optimization control method for a security constraint uncertain multi-agent system, which comprises the following steps of: constructing a topological graph according to a network structure of the multi-agent system, and determining an adjacent matrix of the topological graph; determining a state equation; determining a target function needing to be optimized and inequality constraints needing to be met; according to the state equation, the target function needing to be optimized and the inequality constraint needing to be met, based on a Lyapunov function control method, an optimal solution search condition is established, and based on a barrier function control method, a system safety maintenance condition is established; determining an input constraint condition according to the multi-agent system; calculating by using a quadratic programming method to obtain optimal control input; and controlling the multi-agent system according to the optimal control input. According to the method, the long-term reliability of the system can be remarkably improved, the communication and calculation overhead is remarkably reduced, and the calculation efficiency is remarkably improved.

The invention relates to an optimal power capturing method for a wave energy power generation device of a mixed-flow type air turbine, and belongs to the field of wave energy power generation. The optimal power capturing method comprises the steps that the change condition of the average output power of the turbine under different sea conditions along with the rotating speed is tested, and the maximum average output power point of the turbine under each sea condition is obtained; fitting the maximum average output power point under each sea condition to obtain a control law of the electromagnetic power of the motor; the quadrature-axis current reference value of the stator is made to be equal to zero, and the direct-axis current reference value of the stator is calculated; calculating a stator quadrature-direct axis current error term, and establishing a positive definite Lyapunov function; deriving the Lyapunov function, substituting the Lyapunov function into a stator quadrature-direct axis current state equation, and then performing transformation according to a first stability theorem to obtain a stator quadrature-direct axis voltage control law; and generating a corresponding SVPWM (Space Vector Pulse Width Modulation) switching signal according to a stator quadrature-axis voltage control law so as to control a motor-side converter and realize control on a turbine. The wave energy capturing efficiency can be improved.

The invention discloses a power control method, system, equipment and medium for a modular multilevel converter, and belongs to the technical field of power electronics and power system application, and the method comprises the steps: collecting the bridge arm current, the sub-module capacitor voltage, the DC bus voltage and the AC side power of the modular multilevel converter; calculating a power error according to the AC side power and a power reference value, and calculating a sub-module capacitor voltage error according to the sub-module capacitor voltage and a voltage reference value; constructing a Lyapunov function comprising a plurality of control targets, wherein the Lyapunov function comprises a power error term, a sub-module capacitor voltage error term, a circulating current suppression term and a total energy balance term; and determining a control law on the basis that the Lyapunov function meets a stability condition, and controlling the modular multilevel converter according to the control law. According to the method, the coupling control problem of multi-terminal direct current system power distribution and sub-module energy balance is considered and improved.

The invention discloses a performance control method of a limited nonlinear information physical system, which belongs to the technical field of intrusion tolerance control of information physical systems, and comprises the following steps of: establishing a control-oriented dynamic model of the limited nonlinear information physical system with external disturbance under spoofing attack; mathematical characterization is carried out on bounded multiplicative and additive spoofing attacks; a Lyapunov function based on a preset performance control method is constructed, and a system stability determination method based on a Nusb inequality is given; and coordinate change is introduced to meet full-state constraint, and a fixed time controller is designed by using a backstepping method, so that preset performance control of the limited nonlinear information physical system under spoofing attack and external disturbance is realized. The invention develops a fixed time intrusion tolerance controller based on a preset performance method, which can ensure the stable operation of a limited nonlinear information physical system under a spoofing attack with a small tracking error.