298 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 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 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 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 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 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 provides a self-adaptive preset time control method and system for a flexible mechanical arm, and relates to the field of control science and engineering.The self-adaptive preset time control method comprises the steps that a dynamic model of the flexible mechanical arm is built, and a nonlinear system state equation is deduced; constructing a time transformation function and a coordinate transformation function based on the system state equation; a time transformation function and a coordinate transformation function are utilized to construct an adaptive preset time controller, a dead zone compensator is connected to the output end of the controller in series, the ideal input control signal is compensated, and actual control input is obtained and acts on the flexible mechanical arm; a time transformation function is introduced into flexible mechanical arm control, a Lyapunov function and a self-adaptive estimation method are combined, an asymptotic tracking given tracking curve is finally output, global specified transient performance is guaranteed, performance indexes in the aspects of stable time and tracking precision can be pre-distributed offline according to task requirements, and the stability of the flexible mechanical arm is improved. And the method does not depend on an initial state and any design parameters.

The invention provides a quadrotor unmanned aerial vehicle fault-tolerant control method and system considering random disturbance, and belongs to the field of unmanned aerial vehicle flight control. The method comprises the steps that a random control system model fusing Wiener process random disturbance, actuator faults and input saturation is established, a six-degree-of-freedom control problem is decoupled into a position and attitude tracking sub-problem, and an expected attitude angle is inversely solved; applying constraints to the state variables by adopting a mixed constraint strategy fusing a delay state conversion function and nonlinear state dependence mapping; based on a self-adaptive backstepping method control framework, a controller is designed for six-degree-of-freedom sub-channels of the four-rotor unmanned aerial vehicle, and final input control signals of the degree-of-freedom sub-channels are obtained; and constructing a comprehensive Lyapunov function covering six degrees of freedom of the unmanned aerial vehicle, and proving that all signals of a closed-loop system are finally consistent and bounded. According to the invention, multiple complex factors can be coped with synchronously, the flight stability and reliability of the unmanned aerial vehicle in a complex environment are improved, and the method is suitable for multi-scene quadrotor unmanned aerial vehicle control.

The invention discloses a mechanical arm self-adaptive sliding mode control method based on a disturbance observer, and relates to the technical field of mechanical arm control, and the mechanical arm self-adaptive sliding mode control method comprises the following steps: constructing an initial dynamic model based on a Lagrange equation; constructing a simplified two-degree-of-freedom rigid mechanical arm dynamic model based on the initial dynamic model; designing a novel reaching law fusing the advantages of an exponential reaching law and a power reaching law, and combining a hyperbolic tangent function to replace a sign function to construct a sliding mode surface; designing a nonlinear disturbance observer, estimating the total disturbance of the system in real time, carrying out feed-forward compensation, and bringing the composite disturbance influence into a control law; designing a control law and an adaptive law of the mechanical arm through a Lyapun function; the stability of the system is analyzed through a Lyapunov function, tracking errors are proved to be asymptotically converged, and the method is used for ensuring the reliability and stability of the control system. According to the method, the disturbance observer and the self-adaptive sliding mode control strategy are combined, and the control precision and robustness of the mechanical arm when external disturbance and model uncertainty exist are effectively improved.

The invention discloses a wind power generation system model prediction control method and device based on event triggering. The method comprises the following steps: establishing a wind power generation system model; based on the wind power generation system model, whether an event triggering mechanism is activated or not is judged according to the deviation between the current state and the target state of the system and the maximum sampling interval time; designing an LMPC algorithm based on event triggering: if an event triggering mechanism is activated, solving an optimization problem in a finite time domain to obtain an optimal control sequence of the system, and fusing Lyapunov function constraint to ensure the closed-loop stability of the system; wherein the optimization problem takes a minimum weighted sum of a system output tracking error and a control input variable quantity as a target; and deploying the LMPC algorithm based on event triggering into a control device of the wind power generation system so as to realize optimal control of the wind power generation system. The stability and robustness of the wind power generation control system can be effectively improved.

The invention provides an optimal primary frequency control method based on reinforcement learning. The method comprises the following steps: establishing a dynamic model of a power system; designing a frequency control strategy based on a deep reinforcement learning algorithm; the system frequency deviation is used as a reward signal, and a control strategy is trained through interaction with a power system; lyapunov function stability constraints are introduced into the reinforcement learning algorithm; a neural network is adopted to carry out parameterization design on the controller; discretizing the frequency dynamic equation, and proposing a multi-node collaborative optimization target; and deploying the trained control strategy to an actual power system, and adjusting control parameters in real time through an online optimization technology to adapt to the dynamic change of the system operation state. By means of the method, the frequency control accuracy and stability of the power system are improved, and the global optimal performance of frequency control of the whole power grid is achieved in actual deployment.

The invention provides a model-free current prediction control method for a permanent magnet synchronous motor based on a Lyapunov function method, and the method comprises the steps: building a q-axis control channel, constructing a discrete prediction current model without motor parameters, and introducing a parameter alternative item vector to represent the influence of the precise parameter change of the motor on a prediction model; performing first-step prediction on the current state by using the discrete prediction current model; constructing a candidate voltage set, calculating a measurable output quantity regression error by utilizing a regression error model according to the current operation state data, estimating a current parameter alternative item vector by utilizing the parameter alternative item vector self-adaptive law based on the regression error, and for each candidate voltage, calculating the current parameter alternative item vector according to the parameter alternative item vector self-adaptive law; performing second-step prediction on the current state by using the discrete prediction current model; calculating a cost function corresponding to each candidate voltage, and selecting a cost candidate voltage as an optimal control voltage; according to the method, the robustness of the system is improved, and the calculation complexity is reduced.

The invention discloses a fast output feedback control method for a networked nonlinear system triggered by a dynamic event. By designing the dynamic gain, the dynamic gain is bounded, tracking error coupling is avoided, and meanwhile, the influence of a nonlinear term containing an unknown quantity and external disturbance on the system is compensated. In addition, a dynamic event triggering mechanism is provided based on the dynamic gain, and dynamic compensation of a triggering threshold value is achieved. And constructing a composite Lyapunov function by recursively constructing a virtual control quantity, and enabling the Lyapunov function to meet a fast finite time stability criterion in combination with parameter design of dynamic gain and nonlinear term power. Therefore, tracking errors are converged to a tight set, and the states of all closed-loop systems and observers are globally bounded. According to the method, rapid finite-time dynamic event triggering output feedback actual tracking control of a non-strict feedback system can be realized, and the method has urgent engineering application requirements and is of great significance in promoting the development of a networked nonlinear system control theory and an engineering technology.

The invention provides a speed-measurement-free unmanned ship tracking control method and system based on absolute position sensing. Aiming at the problem that a following boat cannot acquire speed information due to failure of a pilot boat speed sensor or communication failure, a distributed finite time state observer is designed, and the speed of a pilot boat is estimated in real time by combining an absolute position and positioning information. And designing an auxiliary controller by adopting a backstepping method based on the observation speed, and constructing a stability constraint. And a model prediction control framework based on a distributed Lyapunov function is further proposed, so that the multi-unmanned ship system can realize expected formation trajectory tracking and attitude consistency only depending on self-sensing and positioning information, and meanwhile, the formation stability is ensured. According to the method, under the condition that the speed of the pilot ship cannot be obtained, the speed can still be accurately estimated, and the control strategy can be optimized online, so that the intelligent sensing, positioning precision, control performance and robustness of the unmanned ship formation system are remarkably improved.

The invention provides a time mark event triggering reinforcement learning multi-agent consistency control method and system, and relates to the technical field of multi-agent cooperative control. Establishing a multi-agent system dynamics model comprising a leader and a plurality of followers on the time mark; based on the state information of the leader and the follower, constructing a tracking error and consistency error dynamic equation between the intelligent agents; a reinforcement learning controller based on a neural network is constructed, and an optimal control strategy and a performance index function are approached; an event triggering mechanism is designed, and the controller is triggered to update only when the local error of the intelligent agent exceeds a dynamic threshold value; by constructing a Lyapunov function on a time scale and solving a linear matrix inequality condition, bounded consistency of the system is ensured, and a Zeno phenomenon is eliminated. Unified modeling and distributed safety control of the time scale multi-agent system in the non-ideal environment are achieved, communication resources are saved, the convergence speed is increased, and the robustness and practicability of the system are enhanced.

The invention discloses a transient performance enhancement-oriented multi-spacecraft attitude cooperative tracking control method, which provides a fixed time controller through an inversion control strategy, realizes quick response of a multi-spacecraft attitude system, and ensures that a consistency error is converged to a sufficiently small domain within fixed time. A fuzzy weight updating law is designed, dependence on system model information is reduced, and adaptability and high robustness are achieved. According to the asymmetric fractional obstacle Lyapunov function, adherence of multiple spacecrafts to time-varying constraints is guaranteed, meanwhile, the problem that the control amplitude related to a large constraint boundary in a traditional logarithmic obstacle Lyapunov function is too large is solved, and therefore the proposed method can process constrained and unconstrained scenes. A saturation threshold event triggering strategy is designed, a multi-spacecraft system is allowed to select three different triggering strategies by adjusting parameters, the transmission frequency of control signals is flexibly changed, and control bus resources are reasonably distributed.

The invention discloses a zero-sum game-based two-degree-of-freedom helicopter safety controller design method, which relates to the technical field of two-degree-of-freedom helicopter control, and comprises the steps of constructing a system equation, introducing an attack signal and a preset performance function, decomposing into two stages of subsystems, and designing a virtual controller, an adaptive law and a self-triggering mechanism. And solving a Hamiltonian-Jacobian-Exophone equation by using a zero-sum game theory, and finally verifying the stability of the system through a Lyapunov function. According to the invention, it can be ensured that the tracking error converges to the steady-state error interval within the predetermined time, and the transient and steady-state performance of the system is significantly improved; and meanwhile, a Nussbaum function and a zero-sum game strategy are introduced to improve the attack resistance, the communication burden and actuator abrasion are reduced through a self-triggering mechanism, and the sesame effect is avoided.

The invention discloses a fixed time ship trajectory tracking control method considering state limitation. The method comprises the following steps: S1, establishing an under-actuated ship mathematical model considering input saturation; s2, limiting the actual position of the ship based on the set first obstacle Lyapunov function; limiting the expected position of the ship based on a set second obstacle Lyapunov function; s3, defining a ship position tracking error, and designing a longitudinal expected speed and a transverse expected speed which can enable the ship position tracking error to converge; s4, defining a ship speed tracking error, and designing a first fixed time sliding mode surface and a second fixed time sliding mode surface according to the ship speed tracking error; and S5, designing a longitudinal control force and a yawing control moment which can enable the first fixed time sliding mode surface and the second fixed time sliding mode surface to converge so as to enable the ship speed tracking error to realize fixed time convergence and solve the problem that the convergence rate of the speed tracking error is low.

The invention discloses a fault-tolerant control method for a discrete-time Markov jump power system, which belongs to the technical field of power system control and comprises the following steps of: introducing a hidden Markov process with unknown partial information to solve the problems of transient faults in a power line, subsequent switching of related circuit breakers and limited mode information of the power system; the influence of the fault-tolerant control of the power system on the stability is considered; establishing a controller model to obtain a closed-loop system model of the system; on the basis of the Lyapunov stability theory, sufficient conditions for ensuring the random stability of the closed-loop system and meeting expected performance are obtained; proving that the inequality condition is effective by utilizing a Lyapunov function and a performance index function; calculating a gain matrix of the controller by using simulation software; and realizing fault-tolerant control of the system according to the gain matrix of the controller and given system parameters. According to the invention, fault-tolerant control of the system can be realized based on a mode of combining performance index control and controller control.

The invention discloses a method and a device for designing a multi-anti-interference incremental backstepping observer based on specified time, and belongs to the technical field of signal tracking and processing. The method comprises the following steps: establishing a system dynamics model containing time-varying disturbance and model uncertainty items; scaling the tracking error and the estimation error by using a scaling function so as to realize the accurate convergence of the system state and the disturbance estimation within the specified time T; combining the scaled error, introducing an incremental backstepping control thought, and designing an incremental backstepping observer capable of processing model uncertainty and multiple disturbances at the same time; under the condition that the total disturbance and the derivative thereof are assumed to be bounded, stability analysis is carried out through a Lyapunov function, and constraint conditions of system stability convergence are deduced; and finally, verifying the effectiveness and superiority of the observer under various input and disturbance working conditions through simulation. The method is suitable for a high-dynamic and time-sensitive precision control system.

The invention discloses a safety control method of a high-order chain type multi-agent system under spoofing attack, and belongs to the technical field of safety control of multi-agent systems. The method comprises the following steps: establishing a dynamic model of a high-order chain type multi-agent system; constructing an extreme attack scene in which all communication links are subjected to bounded spoofing attacks; based on a backstepping control method, a distributed fixed time security control protocol is designed for a system, and the protocol counteracts attack influence by designing a virtual control strategy and a Lyapunov function; a Lyapunov stability theory is utilized to prove that under the action of the protocol, a system tracking error can be converged to zero within fixed time, so that safe and consistent tracking is realized. The defect that the prior art mainly aims at a linear system or only can realize asymptotic convergence is overcome, the method is particularly suitable for scenes such as non-complete constraint wheeled mobile robots, and the robustness and the rapid recovery capability of the system under extreme spoofing attacks are effectively improved.

The invention discloses a cloud control method of an information physical switching system under TOD protocol and DoS attack, which belongs to the technical field of network security, and comprises the following steps: collecting the state of the information physical switching system through multiple sensor nodes, and scheduling data transmission of the sensor nodes through a TOD protocol secondary selection rule to form a scheduling signal; identifying a switching signal arriving at the cloud switching controller through a switching identifier; according to whether the DoS attack exists in the communication network, the local switching controller or the cloud switching controller generates control input to control the information physical switching system; constructing a segmented multi-convex Lyapunov function according to the switching signal and the scheduling signal, and obtaining average residence time and controller gain; the control input is processed by the actuator, and the processed control input is fed back to the information physical switching system. According to the method, engineering feasible balance among the anti-attack capability, the real-time response and the network efficiency is realized through simulation verification.

The invention discloses a grinding robot rigidity self-adaption constant force impedance control method and system and a storage medium, and relates to the technical field of robot control, and the method comprises the following steps: firstly, obtaining original point cloud data of a to-be-ground workpiece through a camera, carrying out filtering noise reduction and down-sampling processing on the original point cloud data, and fitting a workpiece curved surface; secondly, establishing an impedance control model between the tail end of the robot and the workpiece, and constructing a complete control law; secondly, a three-dimensional vision system is introduced to obtain workpiece surface information, and a rigidity and position self-adaptive estimation mechanism integrating vision and a force sensor is constructed; and finally, designing an adaptive law based on a Lyapunov function to guarantee global asymptotic stability of the system. The robot can stably apply constant grinding force under the conditions of a complex curved surface and rigidity change, and the grinding precision, the force control stability and the system self-adaptive capacity are effectively improved; the method is suitable for free-form surface high-precision grinding scenes in the fields of aviation, military industry and the like.

The invention discloses a consistency control method and system for second-order multi-agent iterative learning and a medium. The method comprises the steps that a second-order nonlinear CMANSs kinetic equation which is composed of N follower bodies and a leader body and has uncertainty disturbance is constructed; constructing a double-layer adaptive iterative learning control input model; controlling an input model based on the double-layer adaptive iterative learning so as to enable the tracking consistency error of the iterative learning of the second-order nonlinear CMANSs kinetic equation within a preset time range to be within a preset range; and on the basis of a Lyapunov function, the tracking consistency error of iterative learning in a preset time range is demonstrated in a preset range. Therefore, the method can control the input model through double-layer adaptive iterative learning, and ensures that the second-order nonlinear CMANSs can still realize robust tracking in an uncertain environment, thereby realizing tracking consistency of the second-order nonlinear CMANSs with uncertain disturbance in finite time.

The invention belongs to the technical field of control theories, and provides a ZNN model design method for solving a time-varying Sylvester equation based on a filter, and the method comprises the steps: building an error function based on the basic knowledge of the time-varying Sylvester equation; designing a predefined time function based on a high-gain method, introducing the predefined time function into a ZNN model, and considering unknown noise interference in a ZNN model solving process; for unknown noise interference, a filter containing a predefined time function is introduced, and a subsystem of a neural network model is constructed; a Lyapunov function is constructed, a ZNN is obtained through stability analysis, noise can be effectively suppressed, and a time-varying Sylvester equation is solved at predefined time; and solving a time-varying Sylvester equation by using the predefined time ZNN model based on the filter, and verifying the performance of the predefined time ZNN model based on the filter. The method is used for solving a time-varying Sylvester equation, and the influence of noise can be effectively suppressed.

The present application relates to tower crane anti-swing motion control technical field, especially a kind of six-degree-of-freedom tower crane anti-swing control method and system based on sliding mode control.The dynamic model of tower crane is constructed based on Lagrange method;The dynamic model of tower crane is linearized in equilibrium position using the constructed tower crane;According to the state information of tower crane, the error signal is selected to design the sub-sliding surface, and the initial control law is designed using the principle of negative rate of Lyapunov function change;Unknown disturbance and model uncertainty are estimated using adaptive algorithm to obtain updated control law;Design total sliding surface and introduce auxiliary function to select appropriate approach law to obtain auxiliary control law;The final control law is obtained by combining the updated control law with the auxiliary control law.The problem that tower crane is easily disturbed in complex environment is effectively solved by constructing hierarchical sliding surface and introducing adaptive algorithm, and the working efficiency and performance of tower crane are significantly improved.