4585 results about "Control objective" patented technology

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

Disclosed in the present invention is an unmanned aerial vehicle formation cooperative obstacle avoidance method based on distributed model predictive control. The method comprises the following steps: establishing an unmanned aerial vehicle motion model to predict a control state of a single unmanned aerial vehicle; on the basis of the unmanned aerial vehicle motion model and control objectives of reaching a target state and maintaining formation, establishing a cost function; setting a constraint, updating the control state in a prediction time domain under the condition that the cost function is minimum, and obtaining the control state of each unmanned aerial vehicle in a subsequent period of time; and each unmanned aerial vehicle flying in the subsequent period of time on the basis of the control state, so as to realize cooperative obstacle avoidance. The unmanned aerial vehicle formation cooperative obstacle avoidance method based on distributed model predictive control disclosed in the present invention has the advantages of high stability, low operation amount, low delay and the like.

The invention provides an energy-saving control method based on dynamic air conditioning operation data, and relates to the technical field of intelligent control and energy-saving optimization of an air conditioning system.The energy-saving control method comprises the steps that operation data, including temperature, humidity, air velocity and energy consumption data, of an air conditioning system are collected, and a comfort index, an air quality index and an energy efficiency index are generated; the method comprises the following steps of: generating an air conditioning index comprehensively, setting a control target parameter of a system, further generating an operation standard value, dynamically generating a control strategy which comprises the steps of adjusting the switching time of a cooling mode and a heating mode of a fan, optimizing the operation speed and time of the fan and adjusting the operation frequency of a compressor, and finally, if the generated air conditioning index exceeds the standard value, controlling the system to operate. And if not, control strategy adjustment is repeated until the air conditioning index is within the standard value. The control strategy is dynamically adjusted by collecting and analyzing the operation data of the air conditioning system, efficient operation of the system is achieved, and the comfort level of a user and the energy-saving effect are improved.

A microgrid self-triggering control method and system for resisting FDI attacks is provided. The method includes the following steps: S1: adopting primary control based on droop control for a microgrid; S2: designing a secondary control strategy of the microgrid, where the secondary control strategy includes frequency recovery and fair distribution of active power; and S3: designing a microgrid distributed self-triggering control method of a hash encryption method after considering the condition that the FDI attacks are injected into a secondary control communication link, so as to resist false malicious data in the microgrid and achieve secondary control. According to the microgrid self-triggering control method and system, a distributed secondary control target of the microgrid is achieved even under the FDI attacks, the injected malicious data can be quickly detected and eliminated, and the distributed secondary control communication network of the microgrid can be protected.

The invention discloses a multi-agent spatio-temporal dynamic system intrusion tolerance and fault tolerance cooperative security consistency control method. The method comprises the following steps: constructing mathematical models of a leader and a follower of a multi-agent spatio-temporal dynamic system based on a partial differential equation; constructing a mathematical model of actuator faults and spoofing attacks; defining a consistency condition to obtain an error system; constructing a proper Lyapunov functional, and obtaining a system stability criterion by using a Lyapunov stability theory; and the error of the multi-agent space-time dynamic system tends to zero by using the state feedback controller, so that the system realizes safety consistency control. The invention comprehensively considers that a multi-agent space-time dynamic system is easy to have actuator faults in a complex environment and is easy to suffer from network attacks in network communication, introduces an intrusion tolerance and fault tolerance cooperative security consistency control method, improves the robustness of the system, and improves the reliability of the system. And the multi-agent space-time dynamic system can achieve a safety consistency control target.

The invention discloses a motor intelligent control method and system based on a self-adaptive control algorithm, and the method comprises the steps: obtaining motor operation parameters, and determining an overall control target according to the motor operation parameters; on the basis of the overall control target and the current working condition information, initial control parameters are calculated through a self-adaptive control algorithm; monitoring interaction parameters between the motor and the load; the initial control parameters are dynamically adjusted, and the running state of the motor is optimized; in a preset motor prediction model, based on the historical operation data and the current real-time data of the motor, predicting the operation state change of the motor, obtaining a motor prediction result, formulating an adjustment control strategy, and triggering a motor control instruction; the actual operation state of the motor is collected, and a state difference value is calculated; if the state difference value exceeds the threshold range, triggering a generator exception processing instruction; adjusting control parameters are obtained; triggering an adjustment instruction based on the adjustment control parameter; the control effect and the self-adaptive capability of the motor control system are improved.

The invention discloses a hotel energy consumption monitoring management system based on big data, and relates to the technical field of energy consumption monitoring management, low-efficiency equipment is screened out according to equipment energy consumption data and operation feedback data, and operation parameters of the low-efficiency equipment are automatically controlled by using a trained fuzzy control model by taking energy consumption reduction as a control target; a pre-trained reinforcement learning algorithm is used to optimize a selected operation mode, feedback data after optimization is collected, an improvement degree is constructed according to changes of energy consumption data, and if the obtained improvement degree does not exceed a preset improvement threshold value, fault detection is carried out on equipment, and alarm information is sent out when a fault exists. And outputting a corresponding energy consumption optimization scheme by the hotel equipment energy consumption maintenance knowledge graph, and constraining a maintenance period. The constrained maintenance period constraint can prevent hotel equipment from being in a long-term sub-health state, repeated or delayed maintenance is avoided, and the availability rate of the equipment is improved while energy consumption is reduced.

The described technology is generally directed towards conflict management of applications implemented on a radio access network (RAN) network. Various embodiments are presented to enable identification of a potential conflict (e.g., where an application is being onboarded) as well as a present conflict (e.g., where two or more applications are already operational). Conflicts may directly affect a control target, e.g., two applications are trying to control the same target. Conflicts may indirectly affect a control target, two applications affect a first metric and a second metric, but a third metric is affected. Conflict resolution includes preventing an application onboarding, terminating operation of an application, identifying causes unrelated to the application, rolling back operation of the RAN to a prior condition (e.g., pre-conflict condition), and suchlike. Conflict resolution can be conducted at a RAN intelligent controller (RIC).

The invention relates to a batch rectification system based on intelligent control, and relates to the technical field of batch rectification. A data acquisition module; the data acquisition module is used for acquiring temperature, pressure, flow and material concentration data in real time; the control module is used for receiving various data of the data acquisition module; a model prediction control algorithm is built in the control module; according to the model prediction control algorithm, a rectification process dynamic model is established, a current system state and a set control target are combined, an optimal control strategy is obtained through rolling optimization calculation, and a control instruction is sent by an execution mechanism based on the optimal control strategy; an execution mechanism; the touch display screen is connected with the control module; the operation parameters of the rectification system are displayed in real time.

The invention discloses a quadruped robot control method and device.The method comprises the steps that quadruped robot joint angle information is obtained, and the quadruped robot joint angle information comprises hip joint adduction and abduction angles, hip joint flexion and extension angles and knee joint bending angles; calculating a rotation error vector according to the joint angle information of the quadruped robot; according to the rotation error vector, a model prediction control objective function is constructed, and the optimization objective of the model prediction control objective function is that the error between a prediction model and an expected state is minimum; according to the model prediction control objective function, quadratic programming solving processing is carried out, and a foot end force prediction value is obtained; generating a joint torque command by using a virtual force model control method; and robot control is conducted according to the foot end force predicted value and the joint torque command. The quadruped robot control method realizes quadruped robot control, improves environmental fitness and anti-rollover capability, and reduces calculation complexity and response delay. The method can be widely applied to the technical field of robot control.

The self-adaptive switching control method for the unmanned surface vehicle comprises the following steps: establishing a mathematical model of the unmanned surface vehicle, and determining a control target of the unmanned surface vehicle; designing a virtual control law and a first Lyapunov alternative function for controlling the unmanned surface vehicle based on a backstepping method; designing a second Lyapunov alternative function, establishing an adaptive neural network controller model and a robust controller model, and designing control input of the unmanned surface vehicle switching control system; designing updating laws of an adaptive neural network controller and a robust controller, and designing a third Lyapunov function to perform stability analysis on the system; and testing and simulating the unmanned surface vehicle system in a simulation environment by using the adjusted control strategy. According to the control method, on the premise of ensuring the stability of the system, the path tracking error of the unmanned surface vehicle is reduced, and the energy consumption of the controller is reduced.

The invention provides a vehicle speed adjusting method fusing road surface evenness and wheel data and related equipment, and relates to the field of intelligent control. The method comprises the following steps: acquiring a pavement image and point cloud data through monitoring equipment, determining pavement concave-convex height information and state parameters by combining the pavement image and the point cloud data, and inputting the pavement concave-convex height information and the state parameters into a pre-trained flatness evaluation model to obtain a pavement flatness grade. And when the road surface is in the uneven range, acquiring wheel data of the target vehicle, and inputting the road surface flatness grade and the wheel data into the jolting influence evaluation model to obtain jolting data. The vehicle bumping influence level is determined by combining vehicle-mounted sensor data, and then the standard vehicle speed value is determined, so that the current vehicle speed of the target vehicle is controlled, the vehicle speed is accurately adjusted according to the road surface and vehicle conditions, and the driving safety and comfort are improved.

The invention discloses a shield tunneling intelligent control method and system based on a ground-tunnel-machine-information-human adaptation level, and relates to the crossing field of underground engineering intelligent construction and information technology. Setting a first-level index and a second-level index; dividing the shield construction state into three levels of intelligent adaptation levels by combining the total score of the construction state with a dynamic threshold value; setting four targets of attitude intelligent control, settlement intelligent control, tunneling efficiency optimization and construction abnormity diagnosis; a machine learning prediction model is constructed, and the construction state parameters corresponding to the targets are predicted in real time; according to the shield intelligent adaptation level, the man-machine cooperation modes are divided into three classes of A level, B level and C level; and comparing actual data with a prediction result of the machine learning model, and dynamically correcting the control target parameters. Through real-time sensing of geological, mechanical and environmental multi-dimensional construction states and in combination with a dynamic weight distribution strategy, precise evaluation of the shield construction state is achieved, and meanwhile self-adaptive switching of the man-machine control right is achieved.

The invention relates to the field of automatic control, in particular to a self-adaptive storage tank concentration quantitative tracking control method and device for a continuous stirring reaction kettle system. Firstly, a mechanism model of a continuous stirring reaction kettle system is established; converting the mechanism model into a state constrained non-strict feedback nonlinear system model; establishing a neural network system to carry out approximation on a nonlinear term and a coupling term in the state-constrained non-strict feedback nonlinear system model, and designing a virtual controller in combination with an adaptive adjustment algorithm; based on the virtual controller, designing an actual neural network adaptive quantization controller; based on the actual neural network, a controller is adaptively quantized, and a control signal is obtained; the control signal acts on the continuous stirring reaction kettle system, the expected storage tank concentration is tracked through the storage tank concentration output by the continuous stirring reaction kettle system, and the control target is achieved. According to the invention, good performance and stability can be provided during the operation period of the continuous stirring reaction kettle system.

The invention relates to the technical field of sewage treatment, in particular to a coupling control system and method for deep denitrification of sewage, and the system comprises a real-time water quality monitoring module, a microorganism twinborn modeling module, a real-time prediction module, an intelligent decision module, an execution mechanism module and a prediction regulation and control module. Compared with the prior art that a passive feedback control strategy based on an effluent quality index is generally adopted, the hysteresis quality is high, and violent fluctuation of an inflow load cannot be coped with; according to the method, a digital twinborn body capable of reflecting the functional state of a microbial community in real time is constructed, and a control target is improved from a traditional process parameter set point to direct optimization of microbial ecological functions; according to the invention, the method achieves the fundamental crossing from the control of technological parameters to the regulation and control of microbial ecology, can carry out intervention from the root of the reaction process, enables the system to have the active health management capability, and remarkably improves the stability of the treatment efficiency and the intelligent level of coping with complex working conditions.

The invention discloses a coordinated control method and system for comprehensive energy multi-agent coordinated group control and autonomous decision making. The method comprises the following steps: establishing a relaxation strategy of an agent corresponding to a distributed power supply; the main coordinator iteratively solves to determine a global coordination signal according to the global operation data of the integrated energy system and the operation data of each agent by taking the lowest total cost, the highest system energy efficiency, the minimum carbon emission and the minimum global penalty coefficient meeting the global relaxation constraint as a relaxation global optimization target; the minimum operation cost of each intelligent agent, the minimum response deviation to a global coordination signal and the minimum deviation between an actual state and a reference state are taken as control targets; establishing an electricity price autonomous decision-making model of each agent and a response power boundary autonomous decision-making model for a global coordination signal; and based on the control target of each agent, the electricity price autonomous decision-making model and the response power boundary autonomous decision-making model, predicting to obtain a control target value of each agent, and realizing balance between a global optimization target and local autonomy.

The invention provides a glass thermal cycle control method and a related device. The method comprises the steps of obtaining equipment parameters of target equipment; performing real-time prediction on the equipment parameters through the thermal cycle regulation and control model to obtain glass yield prediction information of the target equipment under different parameter settings; the thermal cycle regulation and control model is a lightweight model which is carried in target equipment and is used for learning the real-time condition of a production environment; based on the glass yield prediction information, generating an optimization strategy of walking program parameters; the optimization strategy comprises equipment parameters corresponding to a glass finished product rate prediction value reaching a preset glass finished product evaluation standard; the glass finished product evaluation standard is related to equipment production demand information of the target equipment; and adjusting the walking program parameters of the target equipment by adopting the optimization strategy so as to control the thermal cycle speed of the target equipment and improve the production efficiency of the target equipment. The walking program parameters can be dynamically predicted and controlled, the thermal cycle speed of the target equipment is controlled, and the production efficiency and the glass quality are improved.

The invention relates to a control method and a control system for a multi-agent water plant process system. The method comprises the following steps: constructing a control objective function for maximizing the global revenue of the water plant process system; performing process simulation on the single agent of the water plant process system according to the constraint condition to obtain an optimal control scheme of the single agent; grouping and integrating the optimal control schemes, and performing multi-target iterative optimization on at least one formed multi-agent cooperative control scheme set according to a control target function to obtain a multi-agent cooperative control strategy meeting the control target function; and dynamically adjusting the multi-agent cooperative control strategy according to the real-time change information of the water plant process system, and deploying the adjusted optimal multi-agent cooperative control strategy to the water plant process system. According to the method, the competition and cooperation relationship between the single agents is managed through an innovative coordination mechanism, and it is ensured that the overall harmony and the water production efficiency of the water plant are not damaged while all the single agents pursue the optimization target of the single agents.

The invention discloses a multi-agent cooperative control system based on multi-mode brain-computer interface-visual tracking, and the system comprises the following modules: a signal collection and processing module which employs an 8-lead electroencephalogram cap to collect electroencephalogram original data, selects occipital region parts (PO7, PO8, P3, P4, Pz, O1, O2 and Oz) as an electroencephalogram data collection region, and carries out the EEG data collection; the feature extraction and classification module is used for extracting SSVEP signal features by using a space-time convolutional network, optimizing classification precision in combination with an integrated learning strategy, extracting EOG signals based on a Transform model, integrating an LMS adaptive filter and denoising the EEG signals in combination with wavelet transform, expanding the frequency tracking range of the adaptive filter, and reducing signal loss to the greatest extent; the cooperative control strategy design module is used for mapping the electroencephalogram signals into control instructions of different intelligent agents, and cooperative control over the unmanned aerial vehicle and the trolley is achieved; and the target detection and tracking module is used for optimizing an unmanned aerial vehicle visual tracking system based on a PP-YOLOE model and realizing accurate tracking and automatic adjustment of the trolley.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle simulation method, device and equipment, a medium and a product, and the method comprises the steps: simulating and generating at least one target unmanned aerial vehicle based on target unmanned aerial vehicle parameters; environment parameter information is received, and a task scene is simulated and generated according to the environment parameter information; controlling the target unmanned aerial vehicle to execute the target task in the task scene according to the target algorithm; the algorithm is selected after a corresponding triggering operation is received in the algorithm selection interface; the algorithm interface comprises an algorithm selection control of at least one algorithm type. According to the invention, the simulation effect of the unmanned aerial vehicle is more comprehensive and reliable.

The invention belongs to the field of active steering of electric vehicles, and relates to an active four-wheel steering path tracking control method based on Nash game, which comprises the following steps: S1, establishing a two-degree-of-freedom transverse dynamic model of a vehicle; s2, designing a tire cornering stiffness correction coefficient by adopting a magic formula to correct the cornering stiffness in the prediction model in real time; s3, the Nash game is introduced into four-wheel steering control, the control target of front wheel steering is tracking precision, and the control target of rear wheel steering is transverse stability; and solving the optimal control quantity of each gamer through a distributed model prediction architecture.

The invention relates to a multi-cell control method for hydrogen production alkaline electrolytic cells based on nonlinear model predictive control, which belongs to the technical field of hydrogen production and comprises the following steps of: performing data acquisition on key positions of a hydrogen production alkaline electrolytic cell system in real time; based on the dynamically determined priority of each electrolytic cell, dynamic load distribution of each electrolytic cell is carried out, and a multi-dimensional cooperative regulation strategy is implemented to obtain a hydrogen yield target value of each electrolytic cell; constructing a nonlinear model comprising an electrolytic reaction model, a heat transfer model and an inter-tank coupling model, and performing parameter identification and verification; and designing a nonlinear model predictive control algorithm, performing discretization processing on the nonlinear model, introducing Kalman filtering to process the uncertainty of the model, taking a corresponding hydrogen yield target value reached by each electrolytic cell on the premise of meeting the constraint as a final control target, constructing an optimized target function, and performing solving to obtain an optimal control scheme. Compared with the prior art, the control precision and the system stability can be effectively improved.

The invention relates to a fermentation equipment cooperative control method based on a distributed control architecture. Comprising the steps that each fermentation equipment control unit broadcasts node information of the fermentation equipment control unit through a distributed protocol including Zigbee or LoRaWAN, and the node information includes equipment types, control capability and state parameters; the master control node receives and integrates the broadcast information, dynamically generates a distributed control topological graph, and marks the roles of each device including a master control, an auxiliary and a terminal; the master control node formulates a global cooperative control target according to a production target comprising a metabolite concentration or fermentation time; each node monitors fermentation parameters in real time and uploads the fermentation parameters to the master control node through a distributed data transmission protocol; the master control node generates a globally optimized control parameter set by using a collaborative optimization algorithm including a Lagrangian relaxation method; each distributed node adjusts the operation state of the equipment according to the issued parameter set; each node collects fermentation environment data including temperature mutation and pH value fluctuation in real time, and rapid analysis is carried out through an edge calculation unit.

The invention relates to a four-axis unmanned aerial vehicle PID parameter setting method based on a whale optimization algorithm, and belongs to the technical field of unmanned aerial vehicle control. The method aims at solving the problems that in the traditional PID parameter setting process, experience is relied on, time is consumed, and optimal parameters are difficult to obtain. According to the method, the whale optimization algorithm is introduced, and the characteristics of high global search capability and high convergence speed are utilized, so that the parameters of the four-axis unmanned aerial vehicle PID controller are automatically optimized. The method comprises the steps that firstly, a kinetic model of the four-axis unmanned aerial vehicle is established, a control target of a PID controller is determined, PID parameters serve as optimization variables, and a fitness function is defined to evaluate control performance; then, performing global search in a parameter space by using a whale optimization algorithm, and gradually optimizing PID parameters by simulating a whale predation behavior; the optimized PID parameters are applied to a flight control system of the four-axis unmanned aerial vehicle, and stable attitude, height and position control is achieved. The method has the advantages that PID parameters are automatically set through the whale optimization algorithm, the low efficiency and subjectivity of a traditional trial and error method are avoided, and the parameter setting efficiency and precision are remarkably improved; meanwhile, for an unmanned aerial vehicle control system with high nonlinearity and complexity, the WOA can adjust parameters based on actual feedback under the condition that accurate modeling of the system is not needed, and dependence on system modeling is reduced.

The invention provides a vehicle driving control method and device for a backlight scene, equipment and a medium. The method comprises the steps that multi-source information of a target vehicle is acquired in real time; performing fusion calculation on the multi-source information to obtain a current backlight risk index of the target vehicle; determining a driving assistance strategy or an automatic emergency braking strategy based on the backlight risk index; and controlling the target vehicle according to the driving assistance strategy or the automatic emergency braking strategy. According to the vehicle driving control scheme for the backlight scene, the backlight risk index is generated by fusing the multi-source information, the backlight scene is accurately judged, the corresponding driving assistance or automatic emergency braking strategy is formulated, the driving safety and reliability of the vehicle in the complex backlight environment are improved, and the driving safety of the vehicle in the complex backlight environment is improved. The problems that in the prior art, due to dependence on a single sensor in a backlight scene, the target recognition precision is low, a dynamic adaptation mechanism is lacked, and a braking strategy is difficult to pre-judge and adjust in advance are solved.

The invention provides an unmanned aerial vehicle consistency control method based on a Stackelberg-Nash game, and relates to the technical field of multi-agent consistency and game optimization control. The method comprises the following steps: establishing a motion attitude nonlinear dynamic model of each unmanned aerial vehicle in a multi-unmanned aerial vehicle system, and defining a communication network topological relation; constructing a consistency error and a performance index function; the method comprises the following steps: establishing a layered Stackelberg-Nash game mechanism with a plurality of participants; utilizing a Bellman optimality principle to construct a coupling HJB equation to solve an optimization control strategy of the leader and the follower; and constructing a single evaluation network to estimate the optimization control strategy of the leader and the follower in each execution so as to realize an optimization control target. A favorable tool is provided for analyzing a series of control problems of master-slave consistency of multiple unmanned aerial vehicles in the control field, and the reliability of a control system can be enhanced to a certain extent.

The invention provides an execution method and device for a test case in a verification platform, equipment and a storage medium, and the method comprises the steps: obtaining a target test case constructed by an interpreted language, and starting the execution of the target test case on the verification platform; the target test case is a program for providing an excitation component for verification of the to-be-tested design file; in response to execution of the target test case, generating a test trigger instruction through the flow controller, and transmitting the test trigger instruction to a sequence control tool in the verification platform through the socket interface; a sequence control tool is used for analyzing the test trigger instruction, the starting and excitation sending sequence of an excitation assembly in the execution process of the target test case is controlled through an analysis result, an excitation execution result is obtained, and the sequence control tool is compiled through a compilation type language; and the excitation execution result is transmitted to the flow controller through the socket interface to be output. According to the application, multi-language compatibility is realized by adopting the socket interface.

According to the multi-unmanned-system fixed-time fuzzy self-adaptive event triggering formation control method, an unmanned system mathematical model considering environment disturbance factors is rewritten into a state equation, a fuzzy state observer based on the unmanned system mathematical model is established, and speed vector estimation of the unmanned systems is achieved; the problem that the speed information of the unmanned system cannot be measured is solved, a fixed time fuzzy adaptive event trigger formation controller based on an event trigger mechanism is established through a virtual controller in combination with a fuzzy state observer and a filtering error, and fixed time fuzzy adaptive event trigger formation control of multiple unmanned systems is realized. Based on the established event triggering mechanism, the problems of communication redundancy and communication limitation caused by uninterrupted information transmission of the formation control target can be solved, the number of times of updating the actuator is reduced, and the waste of network communication resources is reduced. And the fixed time formation control of the multi-unmanned system with unknown dynamics and unmeasurable speed can be realized.

The invention discloses an automatic driving vehicle path planning method and system combined with high-precision perception, and relates to the technical field of automatic driving vehicles, and the method comprises the steps: obtaining the multi-mode real-time data of the surrounding environment of a target vehicle through a sensor module; performing fusion processing to generate a high-precision environment perception map; generating a global path and an initial local path by combining the current position and the target position of the target vehicle; dynamically optimizing the initial local path based on model predictive control to obtain a final local path; and controlling the target vehicle according to the global path and the final local path. The technical problem that in the prior art, due to the fact that environment sensing precision is insufficient, path planning is not flexible enough and dynamic obstacle effective prediction is lacked, automatic driving safety and path planning real-time performance are poor is solved, and the technical effects of improving automatic driving safety and enhancing path planning real-time performance and adaptability are achieved.

The invention discloses a data-driven floating type wind turbine generator model-free control method and system, and the method comprises the steps: obtaining the operation state and external environment parameters of a wind turbine generator, and constructing sample data; inputting the sample data into a pre-trained controller, and outputting a control instruction to control the wind turbine generator; the specific process of obtaining the controller comprises the steps that the simulation operation state / historical operation state and external environment parameters of the wind turbine generator are obtained, and an initial controller is constructed based on the simulation operation state / historical operation state and the external environment parameters; determining a control target, a wind turbine generator operating environment, an intelligent agent, a state space, an action space, a constraint condition and a reward function in the initial controller; and performing continuous interactive learning training on the intelligent agent and the operation environment of the wind turbine generator through a deep reinforcement learning algorithm to obtain a trained controller. The method has the advantages of reducing the modeling difficulty, dynamically correcting the control strategy, improving the power generation efficiency and the like.