477 results about "Performance function" patented technology

The disclosure provides a method and system for trajectory tracking control of a vehicle-manipulator coupling system with finite time prescribed performance. Specifically, a coupling weaken trajectory planning method is designed to reduce the system's coupling effects. A finite time performance function is designed to constrain the trajectory tracking error. In a case the constraint conditions corresponding to the finite time performance function are satisfied, the trajectory tracking error is converted to obtain a transformed error. The sliding mode surface is designed based on the transformed error to control the transformed error to converge in a finite time, and the external disturbance of the vehicle-manipulator coupling system is observed based on non-linear disturbance observer. The control input of the vehicle-manipulator coupling system is designed based on the sliding mode surface and the non-linear disturbance observer output. This ensures that the vehicle-manipulator coupling system can operate precisely along the desired trajectory.

The invention relates to the technical field of charging pile control, in particular to a charging pile dynamic energy-saving method based on a cooling algorithm, and the method comprises the following steps: carrying out the real-time data collection of a charging pile cooling device, and collecting the temperature and flow velocity of a cooling medium, and the temperature and voltage data of a battery in a charging state; according to the method, the energy consumption evaluation performance function is constructed based on the data, and the analysis of cooling energy consumption optimization is achieved in combination with the charging efficiency. The calculation of the gradient data provides an optimization direction in flow and temperature parameter adjustment, and the unreasonable phenomenon of cooling resource allocation is avoided. Iterative optimization of a parameter adjustment result is combined with continuous monitoring of energy consumption and equipment efficiency, and it is ensured that the cooling system can still operate in a dynamic optimization mode under complex conditions. The prediction analysis of the temperature change range in the peak period is combined with the real-time data to adjust the cooling resource allocation rule, and the adaptability of the cooling system in a complex scene is improved.

The invention relates to a control method and system for a flexible joint robot under input delay, and the method comprises the steps: carrying out the modeling according to the motion state of the flexible joint robot, and introducing the input delay into a constructed model; according to the physical characteristics of the flexible joint robot, the constructed model is converted into a state equation; introducing a specified performance function with preset time into the state equation obtained by conversion; constructing an error conversion function, and converting a tracking error constraint problem in the control period of the flexible joint robot into a bounded problem by utilizing the error conversion function and a specified performance function; through an auxiliary system and coordinate transformation, the influence of input delay is eliminated, and a compensated tracking error signal is obtained; and defining a performance index function including an error index and a control input index, designing a virtual controller by taking the minimization of the performance index function as a target, and obtaining unknown parameters in the virtual controller by using a neural network.

The invention relates to the technical field of injection molding, and discloses a plastic part injection mold regulation and control system and method based on a complex curved surface and a thin-wall structure, and the plastic part injection mold regulation and control system based on the complex curved surface and the thin-wall structure comprises a mold modeling module, a temperature modeling module, a control modeling module, a control optimization module and a control execution module; the plastic part injection mold regulation and control method based on the complex curved surface and the thin-wall structure comprises the steps that a mold three-dimensional model and thermal control area mapping are built, a heat conduction model is built, modal dimensionality reduction is conducted, a target temperature field and a performance function are combined, optimal temperature regulation and control are achieved through a predictive control algorithm, real-time feedback is matched, and a heating and cooling unit is driven. And dynamic closed-loop control of the mold thermal field is realized. According to the method, thermal control precision and energy consumption efficiency are optimized through Galerkin modal dimension reduction, model predictive control and a weighted performance function, global closed-loop temperature control is achieved by combining mapping of a three-dimensional area and a heating and cooling unit of the mold, and system safety and stability are enhanced.

The invention provides a turbine case connection part reliability analysis and verification method considering uncertainty, and the method comprises the steps: building a finite element analysis model of a local connection structure of a turbine case, and achieving the representation of the uncertainty of connection rigidity; establishing a turbine casing structure uncertainty analysis model considering the connection rigidity uncertainty; determining a failure mode of the turbine case connecting part, defining a failure performance function, and performing reliability analysis on the turbine case structure; according to the finite element analysis result of the turbine case, designing a feature simulation piece of the connection part; processing a characteristic simulation part of the turbine case connecting part and carrying out a fatigue test; and obtaining a finite element analysis result of the turbine case of the processed characteristic simulation piece, and verifying the processed characteristic simulation piece in combination with the dispersibility of the fatigue life data. According to the method, the precision and engineering of the reliability analysis of the connection part of the turbine case are realized, and the precision of reliability evaluation is improved.

The invention discloses a mechanical structure reliability analysis method and system based on an active learning agent model, and belongs to the technical field of mechanical structure reliability analysis, and the method comprises the steps: analyzing the working condition and load condition of a mechanical structure to be subjected to reliability analysis, and determining a performance function of the mechanical structure in combination with the safety working requirements of the structure; based on the principle of equal probability conversion, the input variables are converted into a standard normal space, and Monte Carlo reliability analysis samples are generated; in a standard normal space, using a center cross method to generate a 4n initial fitting sample, calculating a function value of the sample, obtaining an initial response matrix, and fitting an initial proxy model; and establishing a distance constraint learning function based on the existing agent model and the fitting sample, and iteratively updating the agent model, the fitting sample, the response value matrix and the failure probability in combination with a prior reliable circular ring sample reduction method until a reliability analysis result with enough precision is obtained, and ending the reliability analysis system.

The invention relates to a multi-robot system global flexible preset performance control method under an unknown state and a DoS attack, and belongs to the technical field of multi-robot system control. Aiming at the problems in the prior art that the communication topology is unstable due to attack, the initial error constraint is strict, and the input saturation conflicts with the performance, the following scheme is provided: dynamically reconstructing the attacked communication topology based on a bipartite consensus layering algorithm; designing a global error constraint mechanism fusing the scale function and the tunnel type performance function; constructing a balance control input and performance boundary of the input saturation self-adaptive auxiliary system; and estimating an unknown state and compensating an observation error by adopting a state observer. The method has the technical effects that the DoS attack resistance of the system is enhanced, the global preset performance control of any initial error is realized, the contradiction between input saturation and tracking performance is dynamically coordinated, and the multi-robot collaborative operation under the unbalanced topology is supported.

The invention relates to a trajectory tracking optimization control method for a two-connecting-rod mechanical arm with an incomplete measurable state, and belongs to the technical field of control engineering. The method utilizes a preset performance function and an event triggering mechanism to implement event triggering model prediction optimization control: establishing a mathematical model of a two-connecting-rod mechanical arm system, and obtaining a state-space equation of the system according to the mathematical model; designing an extended state observer, setting a preset performance function, adjusting parameters of the preset performance function to meet transient performance requirements, and performing error conversion on an output tracking error according to the transient performance requirements; designing an event triggering model predictive controller based on a preset performance function; solving an optimization problem to obtain an optimization control input variable and a state variable; and designing a self-adaptive time-varying trigger mechanism to reduce the optimization times of predictive control. The method integrates the advantages of a preset performance control method, an event triggering mechanism and a prediction control method, has the advantages of high response speed, high positioning precision, small calculation amount and the like, and has relatively good tracking performance and control flexibility.

The invention discloses a preset performance asymptotic control method for a hydraulic servo system with control signal unknown dead zone compensation. The preset performance asymptotic control method comprises modeling of the hydraulic servo system with the control signal unknown dead zone and the preset performance asymptotic control method with the dead zone compensation. Firstly, in order to weaken the influence of an unknown dead zone, a smooth inverse dead zone model is introduced, and parameterization processing is carried out to facilitate control design; secondly, a decreasing constraint is applied to the tracking error by defining a preset performance function; a parameter adaptive law is further designed, and in combination with a robust technology based on interference upper bound estimation, it is ensured that the system has presettable performance and asymptotic stability under bounded time-varying disturbance. According to the control method, the preset performance and the asymptotic stability of the hydraulic servo system containing the control signal unknown dead zone under the time-varying interference are achieved for the first time.

The invention provides an optimal control method for a human-in-loop multi-agent system under FDI attack, and belongs to the technical field of multi-agent system control. According to the control method, a non-completely autonomous leader system is constructed, and FDI attacks from a follower controller to an actuator end are considered. In view of a contrary relationship between an attacker and a defender, under a unified design framework of a zero-sum game and an adaptive dynamic programming theory, constructing a performance function and obtaining a Nash equilibrium point, namely an optimal strategy of the attacker and the defender; meanwhile, in order to reduce the consumption of network resources, a dynamic event triggering mechanism is designed; and a Nash optimal solution is obtained by combining a single evaluation network structure based on a neural network. According to the control method disclosed by the invention, dynamic event triggering game optimization control under the influence of FDI attacks is realized, a method for simultaneously designing optimal strategies of attackers and defenders under a unified framework is constructed, and communication resources are saved while game performance optimization is achieved.

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 relates to the technical field of structural reliability analysis, in particular to an adaptive structural reliability analysis method based on a Co-kriging proxy model, which comprises the following steps: determining a structural failure mode, acquiring corresponding high-fidelity and low-fidelity performance functions and inputting distribution information of variables; a high-fidelity sample set and a low-fidelity sample set are obtained through sampling; obtaining responses corresponding to the two fidelity samples, and constructing a Co-kriging agent model; carrying out structural reliability analysis by utilizing a Monte Carlo simulation method, and calculating a failure probability of each iteration; judging whether a convergence condition is met; adding new sample points into the training set by using a learning function, constructing a Co-kriging proxy model, and obtaining a final failure probability; according to the method, the Co-kriging proxy model is combined with the learning function MPO (x, m), multi-precision sample data can be fused, and on the premise that it is ensured that the failure probability estimation result has high precision, the calculation process is optimized, unnecessary calculation resource consumption is effectively reduced, and the overall calculation cost is remarkably reduced.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

The invention relates to the technical field of formation control of an air-sea heterogeneous cluster system, and discloses a predefined time formation control method based on a fuzzy neural network, and the method comprises the steps: building kinematics and dynamics models of a six-degree-of-freedom four-rotor unmanned plane and a three-degree-of-freedom unmanned ship; establishing a formation tracking error and a speed tracking error of the air-sea heterogeneous cluster system; constructing a preset performance function, and determining an error conversion function; determining a predefined time virtual control law; establishing a predefined time fuzzy neural network, and determining a distributed predefined time formation controller of the air-sea heterogeneous cluster system in combination with a predefined time virtual control law; and performing distributed predefined time error constraint formation control on the air-sea heterogeneous cluster system according to a distributed predefined time formation controller of the air-sea heterogeneous cluster system. According to the method, the robustness of the air-sea heterogeneous cluster is improved, so that the system can converge within the settable predefined time and does not depend on the initial value of the system.

The invention relates to a rigid spacecraft attitude control method and system based on finite time constraint, and belongs to the technical field of spacecraft attitude control. Comprising the following steps: respectively constructing a spacecraft attitude control system model and a finite time convergence performance function containing a preset time constant; according to the finite time convergence performance function, dynamic attitude constraint is applied to the system state of the spacecraft attitude control system model; converting the attitude constraint into differential homeomorphic mapping of an unconstrained space; obtaining an error conversion variable according to differential homeomorphic mapping; and designing a virtual control law and constructing an error compensation module according to the error conversion variable, and designing an actual control input law according to the virtual control law and the error compensation module. According to the method, rapid convergence of the spacecraft attitude within the preset time is ensured, the dynamic response precision of the spacecraft in the rapid attitude maneuver process is remarkably improved, the control period delay is effectively avoided, and the power consumption is reduced.

The invention provides a non-probabilistic time-varying reliability analysis method based on ellipsoid correlation modeling. The method comprises the following steps: carrying out interval modeling on uncertainty input parameters; constructing a multi-dimensional ellipsoid uncertain domain based on correlation information among parameters; a system time-varying performance function is established, and an interval process of output response changing along with time is obtained through uncertainty propagation solution; identifying the maximum value point of the upper boundary and the minimum value point of the lower boundary in the interval process as reliability key points; respectively calculating non-probability reliability indexes based on the ellipsoid model at the moments of the two key points; and taking the smaller value of the two indexes as the non-probability time-varying reliability index of the system in the target time interval. According to the method, the problem that the evaluation result is conservative due to the fact that variable correlation is ignored in a traditional non-probability method is effectively solved, time-varying evolution of system performance can be accurately quantified, and an accurate and practical dynamic safety evaluation tool is provided for a complex engineering system with scarce data.

The invention relates to a preset performance specified time tracking control method for a mechanical arm, belongs to the technical field of intelligent robot control, and solves the technical problem of low control precision of the mechanical arm. Comprising the steps of establishing a dynamical model and a fault model, designing a preset performance specified time function, constructing a preset performance specified time tracking control law, and verifying the bounded stability of the preset performance specified time tracking control law. According to the control method, the specified time preset performance function is introduced into the control law design in an error transformation mode, synchronous adjustment of the convergence precision and the convergence speed is guaranteed, and the control method is used for the mechanical arm device and can also be used for locally spraying an end effector on the surface of an aircraft.

The invention discloses a general algorithm reflection evolution (MHRE) method based on a large language model. The MHRE method comprises the steps of individual coding, population construction and population iterative optimization. In the individual coding stage, the large language model generates code snippets containing performance functions and architecture functions, and the snippets define basic structures and behaviors of population individuals. In the population construction stage, a prompt word of a specific task is used for further refining code snippets, and initial population individuals are formed. In the population iterative optimization stage, through a cross evolution module, a cooperative evolution module, a framework upgrading module and other modules, iterative optimization of population individuals is achieved in combination with a generation-standardization-evaluation-selection loop module (GSES), and algorithm individuals with the optimal performance are generated. The MHRE method can automatically generate, optimize and evaluate a plurality of algorithms, improves the adaptability, universality and optimization effect of the algorithms, and is especially suitable for complex optimization tasks such as industrial production, resource allocation and logistics path planning.

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 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.

The invention relates to an optimal cooperative control method for preset performance of multiple intelligent vehicles under a non-convex input constraint, and belongs to the technical field of multi-agent cooperative control. The problems that non-convex input constraint processing is conservative, preset performance and optimal control are designed separately, and the timeliness of a traditional method is insufficient are solved. Designing a non-convex constraint operator to directly map, control and input to a feasible direction extreme value; constructing a distributed preset time observer to realize accurate estimation of the state of the leader; preset performance control is converted into an optimal control problem in combination with a time-varying performance function and error conversion; and solving an HJB equation based on a reinforcement learning iterative algorithm to approach an optimal control law. The conservative property of a traditional convex approximation method is broken through, and it is ensured that control input strictly meets the non-convex constraint; eliminating time uncertainty through preset time observation; fusing preset performance and optimal control to realize multi-target collaborative optimization; the reinforcement learning algorithm has global convergence and real-time performance, and is suitable for multi-vehicle cooperative control in a complex dynamic environment.

The invention belongs to the technical field of unmanned ship control, and particularly discloses a control method of a fixed time event triggering terminal sliding mode with preset performance constraints. The control method comprises the following steps: S1, establishing an all-drive model of a multi-under-actuated unmanned ship system; s2, designing a fixed time disturbance observer; s3, constructing a cooperative tracking error system according to the cooperative control target and the interactive topological structure; s4, restraining and converting the cooperative tracking error by using a preset performance function, and updating a cooperative tracking error system; and S5, constructing a relative threshold event triggering mechanism, designing a distributed event triggering-fixed time terminal sliding mode controller, generating an actuator control instruction, and realizing cooperative tracking control of the system through an output instruction of an actuator. According to the method, the problem of fixed-time cooperative tracking control under the preset performance constraint is solved, two indexes of'rapid cooperative control 'and'communication resource saving' are realized at the same time, and the method is applied to a cluster scene of multiple unmanned ships.

The invention provides a multi-objective optimization method fusing a classifier and an active learning strategy, and belongs to the technical field of artificial intelligence and optimization design, and the method specifically comprises the steps: obtaining a sample data set containing a plurality of target performance function values; performing non-dominated sorting on the sample data set to extract a Pareto frontier point set, and extracting geometric features from the point set as a geometric feature vector sequence; using the geometric features and the corresponding convex marks to train a classifier model of a Transform architecture, wherein the classifier model supports a multi-head attention mechanism and a position coding structure; judging the convex confidence coefficient of the current Pareto front by using a classifier model, and obtaining a candidate scheme; and inputting the candidate scheme into a double-attention mechanism prediction model based on context reasoning, and predicting the multi-target performance of the candidate scheme. According to the method, the experiment frequency can be reduced, the target performance can be accelerated to be achieved, and the intelligence and efficiency of the multi-target optimization process are improved.

The invention belongs to the technical field of spacecraft attitude control, and discloses a spacecraft attitude preset performance control method and equipment, and a computer storage medium. The method comprises the following steps: S1, establishing a spacecraft error attitude dynamics and kinematics model considering unknown bounded input time delay; s2, constructing a preset performance function, and performing preset performance constraint on the attitude error of the spacecraft through the preset performance function; introducing an error conversion function, and converting a constrained attitude error into an unconstrained error; performing state conversion on the spacecraft error attitude dynamics and kinematics model to obtain a spacecraft error attitude dynamics and kinematics model related to state variables; and S3, designing a preset performance control law based on the tracking error variable, so that the attitude error moves in a preset performance function envelope. According to the method, errors caused by unknown bounded input time delay can be compensated, and meanwhile, the steady-state and transient-state performance of spacecraft attitude control can be met.

The invention relates to the technical field of fine wire production, in particular to a continuous casting process parameter optimization method and device based on deep reinforcement learning, and the method comprises the steps: obtaining an initial experiment data set of continuous casting; the initial experiment data set comprises process parameters and performance parameters of continuous casting; optimizing the initial experiment data set by using a Bayesian performance function; and inputting the optimized initial experimental data set into the deep reinforcement learning network, obtaining an optimized Pareto leading edge, and selecting an optimal strategy from the optimized Pareto leading edge so as to adopt an optimal process parameter according to a target value of the performance parameter. The continuous casting process parameters are optimized through the deep reinforcement learning network based on the Pareto frontier, it is ensured that the continuous casting process can reach the ideal level on different performance indexes, and the comprehensive quality of products is improved; and the initial experimental data set is optimized through multiple iterations in advance, so that a foundation can be laid for rapid convergence of a subsequent deep reinforcement learning network, and the performance is improved.

The invention discloses a method and a system for evaluating the toughness of an oversized-diameter shield undercrossing airport runway based on a differential settlement and dynamic settlement rule, terminal equipment and a medium, and relates to the technical field of shield engineering. The method comprises the following steps: acquiring engineering information and post-construction settlement and post-construction differential settlement control standards; constructing a three-dimensional finite element model which comprises a plurality of simulation working conditions and a plurality of key characteristic sections and reflects interaction of soil and a structure; simulating sequential tunneling of the shield, obtaining dynamic settlement data and a final settlement value, and calculating a differential settlement amount; substituting the dynamic settlement data into the toughness performance function to obtain a dynamic toughness value and an evolution curve; and calculating a final-state toughness index based on the dynamic toughness value, and judging the toughness grade in combination with a control standard and a preset grade division standard. According to the method, dynamic and final-state quantitative evaluation and grade judgment of runway toughness are realized, a structural performance attenuation rule is clarified, and a scientific basis is provided for construction control optimization and engineering decision.

The invention discloses an electro-hydraulic system adaptive neural network preset time control method, which integrates a neural network real-time learning technology, innovatively introduces a preset time performance function, and designs an adaptive neural network preset time performance controller. For the electro-hydraulic system position tracking control problem, it can be ensured that system output transient performance and steady-state performance converge to a specified performance index range within a preset time, safe and reliable operation of the system is ensured, the unknown dynamic state of the system can be learned in real time through a neural network, high-precision motion control performance is achieved, and the control precision is improved. And the problem of differential explosion in traditional backstepping control of the electro-hydraulic system can be avoided, and the influence of measurement noise on the control precision is reduced.

The invention provides a heterogeneous multi-agent system control method under a DoS attack based on an event triggering strategy. Robust cooperative control under a complex attack environment is realized through the following steps: S1, performing state description and topological structure modeling on a heterogeneous multi-agent system; s2, carrying out approximation on an unknown nonlinear function in the system; s3, restraining the error dynamic state through a performance function, and ensuring that the system response meets the preset precision; s4, designing a time-varying threshold function, and dynamically adjusting a trigger condition to reduce communication burden; s5, designing an anti-attack controller in combination with event triggering and a Lyapunov theory; s6, designing an adaptive law to carry out online estimation on disturbance; and S7, testing synchronization precision, communication efficiency and anti-attack performance based on a physical platform. According to the method, deep fusion of the event triggering mechanism and the preset performance constraint is realized for the first time, and the self-adaptive dynamic threshold strategy is introduced, so that malicious interference of DoS attacks on a communication link is effectively dealt with, and it is ensured that the heterogeneous nodes can still meet preset performance indexes in an attack scene.

The invention discloses a universal verification system, device and method suitable for the functional performance of an FPGA device, and relates to the technical field of FPGA device testing, the universal verification system comprises verification hardware, verification software, a verification auxiliary instrument and environment equipment, and the specific verification steps are as follows: step 1, on-chip logic function verification; step 2, common interface function verification; 3, software and hardware adaptability function verification; 4, comprehensive performance function verification; 5, dynamic power consumption testing; step 6, common IP function verification; 7, testing associated parameters; step 8, system adaptability; and step 9, environment adaptability. The universal verification system is suitable for verification of a serial interface function, a multi-path IO compatibility test function, development software platform adaptability, simulator adaptability, a configuration mode, configuration starting time, a signal processing function, official IP supportability and the like, and has functions of a high-speed interface protocol, an Aurora protocol, a DDR protocol, an IIC protocol, an SPI protocol and the like.

The invention discloses a reconfigurable robot control method and system for a zero-sum game optimization event triggering mechanism. The method comprises the steps that a reconfigurable robot dynamic model based on joint torque feedback is established, an error dynamic state space is established, and a preset performance function is set to restrain the transient performance of the error dynamic state space; the error dynamics state space constraining the transient performance is mapped to the unconstrained error dynamics augmented system state space, and an event triggering mechanism is introduced, so that the unconstrained error dynamics augmented system state space meets the zero-sum game of maximizing the event triggering interval and minimizing the control input; setting a performance index function based on a zero-sum game, and obtaining an optimal control law under an event triggering mechanism through the performance index function; according to the invention, the precision and stability of robot system control are improved through the predetermined performance function; the zero-sum game of an event triggering mechanism is introduced to obtain an optimal control law under a Nash equilibrium condition, so that communication resources and calculation burdens are effectively reduced; and the control torque of each subsystem is smoother under the action of the optimal control law, so that the energy consumption is reduced and the service life of the motor is prolonged.