189 results about "Learning controller" patented technology

The invention provides a configuration method and system for an overall electrical scheme of a machine room, and relates to the technical field of electrical planning, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out feature extraction and semantic mapping, constructing a high-dimensional semantic association network, carrying out semantic enhancement, semantic modeling and logic association, extracting an abnormal mode, and obtaining a machine room operation state portrait; determining a machine room energy consumption rule and high-energy-consumption equipment, carrying out composite characteristic analysis to obtain a machine room electrical load curve, carrying out hierarchical progressive prediction in combination with exogenous variables, constructing a target function, solving to obtain optimal configuration parameters, and generating a power distribution system design scheme; a self-learning controller is added in a power distribution unit, the electrical load of a machine room is optimized, a power distribution equipment degradation mechanism model is constructed, service life prediction is performed, a power distribution equipment service life management strategy is output, equipment running abnormally is identified, abnormal reasons are judged by referring to historical abnormal data, and an abnormal report is generated and sent to equipment maintenance personnel.

The invention discloses a multi-modal data real-time identification and cooperative processing system based on edge computing and federated learning. The multi-modal data real-time identification and cooperative processing system comprises a cloud center coordination node, a plurality of edge computing nodes, a cross-modal encryption engine, a federated learning controller and a model updating verification module. The cloud center coordination node executes federated learning model aggregation and dynamic task allocation, and generates a cross-modal encryption strategy; and the edge computing node is configured with a multi-modal data acquisition module, a local model training unit and a co-processing gateway to realize multi-modal data acquisition and local processing. The system encrypts vision, acoustics and text data by using differentiated algorithms such as spatial confusion, frequency domain permutation and homomorphic encryption; the federated learning controller carries out multi-modal feature fusion, hierarchical encryption and dynamic networking at the edge node; and the model updating verification module performs aggregation updating after ensuring parameter consistency by using secure multi-party calculation. According to the method, real-time processing and privacy protection of multi-modal data are realized, and the data co-processing efficiency is improved.

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

The invention relates to the technical field of autonomous navigation and robot environment perception, and discloses a multi-source sensor fused adaptive navigation system, which comprises a multi-source sensor space-time synchronization module, a quantum particle filtering positioning estimation module, a space-time element learning controller module, a cross-modal quantum fusion module and an adaptive navigation control module. Multi-source data space-time alignment is realized through Lie group SE (3) calibration and dynamic time warping; the positioning robustness of particle filtering is improved based on quantum state coding and annealing optimization; dynamically distributing a fusion weight and injecting a physical constraint by utilizing a meta-learning network; feature level fusion of laser radar, vision and inertial data is realized by means of a quantum entanglement mechanism; and constructing closed-loop adaptive navigation by combining model predictive control and quantum purity trigger feedback. According to the method, the navigation reliability problem caused by misalignment of multi-modal sensor data fusion, divergence of state estimation and insufficient cross-modal relevance in a dynamic environment is solved.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention relates to the technical field of intelligent control of the welding process, and discloses an online monitoring system and control method for the friction stir welding process, and the system comprises a multi-mode sensor array module which is used for collecting temperature, pressure and displacement data in the welding process; the edge computing node module preprocesses the collected data and extracts spatial-temporal characteristics; the space-time diagram convolutional network module is used for mining a space-time association relationship between the sensors; the hierarchical reinforcement learning controller generates a control instruction according to the associated features; the execution mechanism module receives the control instruction and outputs a welding signal; the digital twinborn verification module performs simulation feedback on the control effect; and the cloud model optimization module fuses the multi-source feedback information to continuously optimize the control strategy. The welding process self-adaptive control technology based on reinforcement learning is adopted, the technical effect of adjusting the welding parameters in real time to optimize the welding quality is achieved, and the problem that in a traditional method, the welding quality is unstable due to working condition changes is solved.

The invention discloses an electrical control circuit fault adaptive diagnosis system and method based on deep learning. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a diagnosis model module, an adaptive optimization module and a visual interaction module. Based on an improved Transform architecture, a time convolution operator is embedded to enhance the local feature perception capability of a self-attention mechanism, high-dimensional time sequence features are extracted, a dual-channel deep residual network is constructed, electrical features and thermal-mechanical features are analyzed respectively, a fault classification result is output after fusion, and model parameters are updated through online incremental learning. And on the basis of a transfer learning controller, new fault sample distribution characteristic transfer pre-training parameters are adapted, and a genetic algorithm is utilized to dynamically optimize the network depth and the convolution kernel size so as to improve the generalization ability. The method has the advantages that high-precision and multi-dimensional information fusion is realized through an improved Transform architecture and a self-adaptive optimization mechanism in combination with time convolution and transfer learning, and the method can quickly adapt to new fault types.

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.

The invention discloses an expressway hard shoulder dynamic opening method and system based on an NFGD model and a simulation platform. Comprising the following steps: collecting and adopting a dynamic confidence mechanism to carry out weighted fusion on multi-source real-time sensing data to obtain fused traffic state characteristics; a hard road shoulder dynamic control decision is made based on an NFGD model comprising a fuzzy-neural hybrid controller, a multi-target genetic algorithm controller and a reinforcement learning controller; and real-time interaction with a simulation platform is realized, and control instruction issuing, feedback acquisition and online strategy evaluation are realized. Compared with a traditional method based on a fixed empirical threshold value, the fuzzy-neural hybrid controller, the multi-target genetic algorithm controller and the reinforcement learning controller are fused, self-adaptive modeling and open discrimination are achieved, and decision-making precision and scene adaptability are improved. And meanwhile, by combining with a prediction adjustment feedback type step length control structure, simulation time drift is effectively inhibited, the timeliness of a control strategy is enhanced, and the method has relatively high practical application value.

The invention discloses a finite iteration error tracking learning control method for robot accurate trajectory tracking. The method comprises the steps of establishing a kinetic equation of a repeated operation robot system, determining a discrete dynamic model of the robot system, designing a finite iteration error tracking learning controller and carrying out simulation verification. According to the method, under the conditions that the initial position of the robot system is arbitrary and the expected trajectory changes, the number of iterations required for achieving arbitrary precision tracking of the given expected error trajectory by the output error of the robot system can be calculated by designing an error tracking learning controller and only utilizing error information obtained through first iteration. According to the method, the limitation that zero error convergence needs to depend on infinite iterations in a traditional iterative learning control method is effectively overcome; meanwhile, it is ensured that the robot system can still achieve the preset tracking precision with the rapid convergence capacity under the disturbance conditions that the initial position is arbitrary and the expected trajectory changes, and the practicability and the popularization value of iterative learning control in actual engineering are improved.

The invention provides a multi-agent reinforcement learning task planning and control method under space-time task driving. The method comprises the following steps: S1, establishing a reinforcement learning controller training architecture of a signal sequential logic task under a multi-agent system; s2, establishing a self-attention action compensation mechanism among the multi-agent systems; s3, constructing a robust value filtering baseline corresponding to the signal sequential logic task according to the task framework; s4, according to the environment information, soft constraints are generated for intelligent agent actions; s5, constructing a conflict detection and task replanning mechanism of the multi-target task according to the compensation action degree; and S6, based on the constraint conditions, iteratively training controller parameters so as to provide a control law online to meet the STL task requirements. According to the method, the controller parameters are learned in a reinforcement learning mode, the method adapts to the online solution control law in a complex environment, the given control law better conforms to the actual conditions of the external environment and the internal dynamic system, and the burden of the online calculation control law is greatly reduced.

The invention discloses an agricultural irrigation control method and system, and relates to the technical field of irrigation control, and the method comprises the following steps: constructing a crop water demand model based on the fusion of crop physiological sensing data and a data driving model, and outputting a target irrigation flow; calculating a control error between the target irrigation flow and the actually acquired flow, and generating a control signal through a model prediction control strategy; inputting a static mapping model fitted by a Sigmoid function, and superposing the output of a dynamic correction model driven by state sensing data to generate a final driving signal; feedback data is collected, a driving signal and the feedback data are subjected to closed-loop comparison, and irrigation control strategy parameters are adaptively optimized through a reinforcement learning controller; according to the method, the crop physiological sensing data and the data driving model are fused, the space and time sequence feature fused water demand model is dynamically constructed, and the problems that agricultural irrigation cannot be accurately controlled and the irrigation strategy is rigid are solved in combination with a static and dynamic composite control mapping mechanism.

The invention relates to the technical field of data center thermal management, and discloses a data center liquid cooling precise temperature control method and system, and the method comprises the following steps: pre-analyzing a calculation task to generate a power consumption prediction sequence; combining the power consumption prediction sequence with the real-time state information of the liquid cooling system, calculating the heat cost of the task at each candidate calculation node through a digital twin model, and selecting a target execution node according to the heat cost; inputting the power consumption prediction sequence and real-time state information into a reinforcement learning controller, and generating a composite coordination action containing a flow regulation instruction and a dynamic power consumption upper limit instruction; and cooperatively controlling the micro-fluidic execution array and the chip power consumption management unit on the target execution node according to the composite coordination action. Through prospective power consumption prediction and two-way cooperative control from the heating end and the heat dissipation end, intelligent distribution and accurate temperature control of thermal loads are achieved, temperature overshoot is effectively restrained, and the overall operation energy consumption of the data center is reduced while the system reliability is guaranteed.

The invention relates to the technical field of diamond epitaxial growth, and discloses an intelligent bias voltage regulation and control method and system for diamond single crystal heteroepitaxial growth, and the system comprises a data acquisition module, a prediction module, an intelligent optimization module, a gradient field compensation module and a feedback and regulation and control module. The future trend is pre-judged by adopting the prediction model, and potential growth abnormality such as nucleation probability reduction in a certain area can be perceived in advance, so that the control system has perspectiveness, and lagging of post passive compensation is avoided; the reinforcement learning controller comprehensively considers an accumulative effect and a long-term target in a strategy optimization process, so that bias voltage regulation and control are more intelligent; the intelligent decision-making capability is far better than that of an adjustment mode of artificial experience or a preset program, so that the growth process has stronger robustness on external disturbance such as airflow fluctuation and discharge state change; in the whole growth process, the consistency of crystal nucleuses and growth rates in all regions is kept, and the accumulation of thermal stress and defects in the large-size thin film can be inhibited.

The invention relates to the technical field of steel plate hot-dip production, and discloses an intelligent scheduling system for a steel plate hot-dip production process. The system comprises a working condition sensing module which is used for continuously collecting steel belt inlet temperature, zinc liquid component concentration fluctuation data, air knife pressure time sequence parameters and roller system operation state signals of a galvanizing pot area; the feature extraction module receives the working condition data flow, analyzes the steel strip surface temperature field distribution features and the zinc liquid viscosity change rule, and generates a process feature spectrum; the parameter optimization module dynamically adjusts membership function parameters and rule base weight coefficients of the fuzzy reinforcement learning controller according to the process characteristic spectrum, and outputs an optimized plating layer control parameter set; the self-adaptive plating layer control module generates a plating layer thickness control instruction based on the real-time zinc liquid temperature parameter and the optimized plating layer control rule set; and the dynamic roller system scheduling module realizes global cooperative control of the roller system speed based on the instruction. The system can realize intelligent scheduling of the steel plate hot-dip production process.

A periodic attitude tracking task-oriented unmanned aerial vehicle predetermined time anti-interference learning control method comprises the following steps: establishing a kinetic model of a four-rotor aircraft system, dividing the uncertainty of the system into a periodic uncertainty part and a non-periodic uncertainty part, and initializing the state and control parameters of the system; designing a predetermined time disturbance observer, and quickly estimating and compensating a non-periodic uncertain part; designing a predetermined time sliding mode variable; designing a full-saturation repetitive learning law, and accurately estimating and compensating the uncertain part of the period; and designing a repetitive learning controller based on the predetermined time disturbance observer, the predetermined time sliding mode variable and the full-saturation repetitive learning law. According to the method, the influence of the system uncertainty on the control precision of the unmanned aerial vehicle can be effectively suppressed, and it is ensured that the upper bound of the minimum convergence time of the observation error can be preset by adjusting an adjustable parameter, so that the tracking error is converged to the neighborhood near the original point within the preset time.

The invention discloses a reinforcement learning-based spring steel wire drawing control method, which comprises the following steps of: acquiring real-time process parameters to form a process state data sequence; inputting the process state data sequence into the state space model, and constructing a virtual working condition sample; based on the virtual working condition sample, pre-training a reinforcement learning controller and outputting an initial control strategy; inputting the initial control strategy into a lower-layer strategy network, and outputting a wire drawing speed adjusting instruction; the current process state and the wire drawing speed adjusting instruction serve as synchronous input, and an implicit context vector is generated; extracting control strategy characteristics in the edge controller, performing compressed encoding and forming control strategy representation; uploading the control strategy representation to a cloud server, and outputting a unified global control strategy model; and carrying out anomaly detection on the current process state, and if a detected state reconstruction error exceeds an anomaly judgment threshold, triggering safety control logic. The spring steel wire drawing control device realizes spring steel wire drawing control.

The invention belongs to the technical field of quad-rotor unmanned aerial vehicle cluster control, and particularly relates to a man-machine cooperation unmanned aerial vehicle optimal encircling control method based on a neural network. The method comprises the following steps: establishing a leader dynamic model with commander control input, a position dynamic model of a four-rotor unmanned aerial vehicle follower, an attitude dynamic model and an unmanned cluster directed graph model; and a distributed follower observer, a position loop intelligent learning controller based on an evaluation neural network and a posture loop intelligent learning controller based on the evaluation neural network are designed for each model, so that the optimal encircling control problem of a plurality of leaders under the command of a commander is solved, and the robustness and adaptability are good. In cooperation with a weight parameter updating algorithm based on a dynamic regression extension and hybrid technology, the transient performance of the attitude ring evaluation neural network parameter learning process is improved, and a task is completed under a weak continuous excitation condition.

The invention discloses a robot motion control optimization method, device, equipment and medium, and the method comprises the steps: inputting task description in a natural language form, robot state information, a historical execution track and environment feedback information into a preset large language model; through the large language model, selecting a target motion strategy of the robot according to the task description; when it is detected that the task of the robot fails or the action of the robot is abnormal, a reward function used for controlling a deep reinforcement learning controller of the robot is reconstructed; and when the state of the robot is abnormal, parameters of the deep reinforcement learning controller are adjusted and optimized, the motion stability, the task completion efficiency and the strategy adaptability of the robot in a high-risk, high-temperature and high-complexity scene are remarkably improved, and the self-adaptive adjusting and optimizing capability and the abnormity recovery capability of the robot in a dynamic environment are enhanced.

The invention relates to the technical field of boiler control, in particular to a multivariable coupling thermal process regulation and control system and method for carbon pollution governing, and the method comprises the steps: collecting multi-source data such as acoustic emission, temperature, humidity and spectrum, and constructing a feature sequence through time mark alignment and wavelet packet enhancement; a heat value characterization quantity is predicted by using a Shenchang differential equation model fused with dynamic gating, a partition equivalent thermal network model is driven on this basis, and accurate prediction of a future time domain temperature field is realized by dynamically correcting thermal resistance and thermal capacity; based on the prediction result, a control instruction is solved through multi-objective optimization under the condition that the active temperature constraint is met; and in combination with heat flow density feedback, a layered reinforcement learning controller is adopted for online compensation of a pre-feedback instruction, and stable and efficient regulation and control of the boiler are achieved.

The invention discloses an interplanetary orbit transfer method based on a hidden state and reinforcement learning, and relates to the technical field of spacecraft control. According to the method, by establishing the sequence hidden variable model, representation learning can be explicitly carried out on the uncertain environment, and hidden information hidden under observation of the uncertain environment is extracted. And a reinforcement learning controller is formed through the actor network and the commentator network. A sequence hidden variable model and a reinforcement learning controller are adopted to construct a reinforcement learning algorithm framework, so that the training of an intelligent agent in an uncertain environment is accelerated, and the processing capability of the intelligent agent on uncertainty is improved. Besides, according to the embodiment of the invention, the next state is predicted based on the current observation and expected operation, and the quality of the predicted next state is brought into the reward structure, so that the real-time reward can capture the validity of the current and subsequent strategies at the same time, and the learning efficiency of the algorithm is further improved.

The invention discloses a water treatment method and equipment based on analogue simulation and deep learning and a medium, and relates to the technical field of urban water supply systems and water treatment. The method comprises the steps of obtaining design data related to water treatment plant facilities, and based on the design data, performing simulation modeling on the water treatment whole-process facilities to obtain a facility simulation model; obtaining process data related to processes of the water treatment plant, and performing reaction simulation on the facility simulation model based on the process data to obtain a water treatment simulation model; the method comprises the following steps: acquiring raw water quality data and equipment state data of a water treatment plant in real time, and inputting the raw water quality data and the equipment state data into a pre-trained deep reinforcement learning controller to generate a facility or process control instruction; and inputting the facility or process control instruction into the water treatment simulation model for execution, and monitoring effluent quality data in real time so as to dynamically adjust a control strategy according to the effluent quality data.

The invention discloses an intelligent acquisition risk identification system based on deep learning, and the system comprises a time sequence sample construction module which is used for building a time sequence sample sequence; the hierarchical attention structural feature coding module is used for carrying out structural feature coding on the sequential sample sequence by utilizing a hierarchical attention network; the time sequence feature extraction module is used for inputting the structural feature vector into an ETSform model to perform time sequence feature extraction; the hierarchical time sequence collaborative attention adaptive fusion module is used for performing bidirectional attention interaction and dynamically generating a hierarchical weight and a time sequence weight through a meta-learning controller; the improved CatBoost risk identification module is used for outputting a risk score and a risk type label; and the system fusion module is used for summarizing and merging the risk scores and the risk type labels. The method and the device are suitable for merchant transaction risk identification in an acquiring business scene.

The invention belongs to the technical field of lower limb rehabilitation, and discloses a man-machine coupling system intelligent cooperative control method and device based on deterministic learning. Establishing a state-space equation of the lower limb exoskeleton robot, defining a tracking error, and designing a self-adaptive neural network controller of the lower limb exoskeleton robot; defining an expected position state vector and a real position state vector of the walking robot, solving a position state vector error, and designing a self-adaptive neural network controller of the walking robot; the method comprises the following steps: constructing a time-varying linear matrix, verifying the exponential stable convergence characteristic of the time-varying linear matrix under a continuous excitation condition on the basis of an adaptive neural network controller, storing nonlinear dynamic knowledge of a man-machine coupling system, and constructing a learning controller by using the learned knowledge. According to the method, while the unknown dynamic state of the man-machine coupling system is accurately modeled, the continuous excitation condition is ensured, so that accurate and stable trajectory tracking control is realized.

The invention provides a neural network-based self-learning controller and a method thereof. The neural network-based self-learning controller comprises a neural network self-learning module, a state prediction and evaluation module, an input leveling module, a dynamic expansion and adjustment module and a deployment and debugging module which are in communication connection in sequence. The beneficial effects of the invention are that the intelligent level of the system is improved: through the self-learning characteristic of the neural network, the controller can automatically adapt to a complex industrial environment, manual intervention is reduced, and the intelligent and automatic levels of the system are improved; manual intervention and debugging are reduced: the system can automatically learn and adjust according to the operation condition of the equipment, so that the complexity of system deployment and maintenance is greatly reduced, and the labor cost is reduced; dynamic expansibility: the system supports dynamic expansion and can adapt to different equipment and load changes, and control logic does not need to be redesigned or the system does not need to be greatly modified; and rapid fault early warning: through prediction and analysis of the equipment state, the system can give out early warning in advance, equipment operation interruption or faults are avoided, and the reliability and safety of the whole system are improved.

The invention discloses a direct current motor iterative learning control optimization method for executing a changing task, and relates to the field of direct current motor control. According to the method, an iterative learning controller is added in parallel on the basis of a direct-current motor closed-loop feedback control system, and the direct-current motor control system is converted into an input and output matrix model of a time sequence based on a lifting technology. An optimal iterative learning control algorithm is designed under a norm optimization framework, and zero-error tracking of expected output is basically realized by the system through combination of inter-batch repeated learning and in-batch real-time feedback. Based on an optimal input sequence and an error sequence which are obtained by repeatedly executing a certain task, a feedback controller and a feedforward controller are integrated into a new feedback controller based on learning by utilizing a least square fitting method, and finally the new feedback controller is applied to a system for executing a change track task. According to the method, historical learning experience is transferred to a brand new task without limiting the time length, and trajectory tracking of the direct current motor change task can be realized without re-learning.

A high-speed matching-engine architecture is disclosed that sustains deterministic sub-microsecond latency while processing more than 10 million order messages per second per core on commodity multi-core processors. Orders reside in cache-aligned Data Holder Nodes whose occupancy and price-level boundaries are tracked with constant-time bitmask operations, eliminating pointer-chasing penalties. Per-core huge-page pools, SIMD copy kernels, and lock-free, cache-line-aligned queues further minimize TLB misses and coherence overheads. Overflow is handled by Push Back / Push Forward cascades that relocate the least- or most-prioritized orders between adjoining nodes without violating price-time priority. Node capacities vary monotonically with book depth and are re-tuned online by a lightweight machine-learning controller that maximizes cache-hit probability under changing market micro-structure. The design tightens spreads, raises match-rate revenue, and complies with stringent regulatory latency caps using standard x86-64, Arm, or other architectures.

Systems, methods, software, and devices are disclosed herein related to trajectory computation by way of a memory-based learning (MBL) controller. An MBL controller in various embodiments stores a set of trajectories in memory. The trajectories connect various initial states of a dynamical system with a target state. In addition to the memory, the controller further includes a processor that collects a current state of the dynamical system and determines, using memory-based learning (MBL) on training instances derived from the set of trajectories, a control policy that defines a trajectory connecting the current state of the dynamical system with the target state. The processor controls the dynamical system according to the control policy.

The invention belongs to the field of energy storage system frequency control, and discloses a safety reinforcement learning load frequency control method, system and device and a medium, and the safety reinforcement learning load frequency control method comprises the steps: constructing a frequency control model; acquiring historical data of the power system to construct a labeled data set; constructing a cost prediction network according to the label data set, taking the state and action of the frequency control model as the input of the cost prediction network, and predicting the control cost at the current moment; constructing a deep reinforcement learning controller, and converting an inequality constraint of a target function in the deep reinforcement learning controller based on a Lagrange multiplier; and according to a conversion result, optimizing an actor network of the deep reinforcement learning controller by predicting the control cost at the current moment, and applying the optimized actor network to real-time load frequency control to realize control of the load frequency. Operation cost and frequency modulation performance of the power system are considered, and comprehensive benefits of the power system are guaranteed.

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.