38 results about "Internal model" patented technology

The invention relates to the technical field of industrial process automation control, and discloses an intelligent adjusting method for an industrial waste liquid low-temperature evaporation process, which comprises the following steps of: superposing pressure step test excitation to a vacuum adjusting valve, synchronously acquiring temperature response data flow, calculating absolute error integral and SIAE and reply time characteristics of a response curve, and calculating the temperature response data flow according to the absolute error integral and SIAE and reply time characteristics of the response curve; substituting into an inverse model to solve an equivalent process gain and a time constant of the controlled object; based on the internal model control principle, PID controller parameters are dynamically calculated and updated through the calculated gain and the time constant, a control law is reconstructed to generate an execution instruction, and the problem of control divergence of a non-linear time-varying object under model mismatch is solved through active energy feature identification and a pole assignment mechanism; decoupling control and self-adaptive adjustment of fluid viscosity sudden change and equipment scaling are achieved, and the stability and control precision of the system are improved.

The application discloses a rocket attitude self-adaptive control method and system based on reinforcement learning, and belongs to the technical field of rocket attitude control. The method constructs an adaptive controller for controlling the rocket attitude through a backstepping method, and the adaptive controller is used for realizing the self-adaptation of the rocket to unknown disturbances based on an Actor-Critic (AC) of reinforcement learning, so that the system can rapidly converge to a desired state, unknown nonlinear observation is realized without depending on an internal model, and the tracking control of the rocket attitude is realized.

The application provides a data-driven non-zero-sum game multi-energy system scheduling method and device, and belongs to the technical field of game control and decision-making. The method designs a scheduling strategy expression form based on an internal model system, and the control strategy gain in the strategy and the internal model parameter of the internal model system are unknown but can be solved. The internal model parameter is solved based on external system state data, and the control strategy gain estimation value is determined based on two-stage learning, so that the balanced scheduling strategy of each energy supplier in the multi-energy system is solved. By using the application, Nash equilibrium between each energy system can be realized only through measurable system data under the condition that the parameters and disturbances of the multi-energy system are unknown, and the limitation of traditional accurate model technology is made up.

The invention discloses a permanent magnet synchronous motor rotating speed control method based on efficient calculation reinforcement learning, and belongs to the technical field of permanent magnet synchronous motor control. Designing a dynamic control algorithm based on an internal model theory, and converting a rotating speed tracking control problem into a stabilization control problem in combination with an output regulation theory; acquiring input-state data by applying a piecewise constant continuous excitation input to the electric machine; carrying out iterative reinforcement learning based on an efficient offline strategy, and carrying out iterative control on the gain until the gain is converged to be optimal; and substituting the optimal gain into a dynamic control algorithm to realize high-precision tracking of the reference signal by the motor. Accurate system parameters of the motor are not needed, and the calculation complexity of solving the optimal control gain is reduced.

The invention relates to the field of traffic control, in particular to a regional traffic situation perception system based on fog computing, which comprises a perception acquisition module, an edge analysis module, a regional cognition module and a situation service module. The system acquires data through the sensing acquisition module; the edge analysis module constructs a digital twinborn model, and outputs a structured scene risk signal by using a multi-modal dynamic attention generative adversarial network and an intention reasoning model; the regional cognition module performs parallel simulation in the digital twin sand table and preferentially generates a collaborative strategy; and the system finally realizes closed-loop optimization of the internal model through a complete scene information packet formed by feedback. According to the invention, by constructing a cloud-edge collaborative digital twinning and multi-agent decision framework, intelligent optimization transformation of traffic management from passive response and local control to active pre-judgment and regional collaboration is realized.

This invention discloses a closed-loop data quality management and intelligent evaluation system and method for AI model iteration, relating to the interdisciplinary fields of artificial intelligence and data governance. The method uses causal probes embedded in the model to detect data anomalies in real time and constructs a dynamic causal probability graph to characterize the causal relationship between data quality, internal model perturbations, and performance. It achieves accurate attribution through counterfactual reasoning, utilizes model-based reinforcement learning for simulation and deduction to determine the optimal repair solution, and simultaneously optimizes and adjusts the decision-making strategy based on feedback from the repair effect. This invention realizes a fully intelligent closed loop from data quality perception and attribution diagnosis to accurate repair and system self-learning and self-optimization, providing an automated data quality assurance mechanism for the continuous and stable iteration of AI models.

The invention discloses a dynamic regulation and control method for hydration heat of a variable-cross-section stacked box rigid frame aqueduct. The dynamic regulation and control method comprises the steps that S1, temperature field data and environment parameters of a pouring structure are obtained in real time; s2, constructing a double-digital twinborn model, wherein the double-digital twinborn model comprises a first digital twinborn model and a second digital twinborn model; s3, synchronously inputting the temperature field data and the environmental parameters into the first digital twinborn model and the second digital twinborn model, and obtaining temperature prediction results of the two; s4, performing interactive verification on the temperature prediction results of the first digital twinborn model and the second digital twinborn model, and triggering a parameter correction rule when the difference of the prediction results exceeds a tolerance interval so as to dynamically correct the input parameter or the internal model parameter of at least one model; and S5, based on the temperature prediction result after interactive verification and dynamic correction, generating a water cooling control instruction to cope with the change of temperature fluctuation and environmental disturbance in the construction process.

The invention belongs to the technical field of robot control, and particularly relates to an anti-disturbance multi-finger cooperative grabbing control method with global preset performance, which comprises the following steps: establishing a kinetic model of a dexterous hand system according to an Euler-Lagrange equation; describing a communication topological structure of the dexterous hand system through a virtual coupling mechanism, and constructing a collaborative grabbing error of the dexterous hand system; a preset time function and a global preset performance function are introduced to convert the collaborative grabbing error, and the overall potential energy of the dexterous hand system is calculated; with the purpose of minimizing the overall potential energy of the dexterous hand system, joint control input torque is designed in combination with a distributed control architecture; designing a self-adaptive updating law for unknown disturbance according to an internal model principle, and obtaining a distributed global preset performance control law in combination with a joint control input torque; according to the method, the robustness of the system under disturbance of joints and external force is remarkably improved.

The invention provides a motor control method and device, equipment and a storage medium, and relates to the field of motor control, and the method comprises the steps: obtaining an expected control current and an actual control current of a motor; using a current tracking module to generate a control voltage according to a current difference between the expected control current and the actual control current; a disturbance suppression module is used for generating disturbance compensation voltage according to the actual control current and the internal model principle; generating a target voltage according to the control voltage and the disturbance compensation voltage by using a harmonic suppression module; and controlling the motor to operate based on the target voltage. The method is suitable for a motor control process and is used for solving the problem of torque fluctuation caused by interference of current harmonics.

The invention discloses an urban rail multi-permanent magnet synchronous motor speed control method based on a distributed internal model, and the method comprises the following steps: S1, selecting a permanent magnet synchronous motor as a traction motor of an urban rail train, and building a mathematical model of a multi-permanent magnet synchronous motor system; s2, describing a multi-permanent magnet synchronous motor speed synchronization and interference suppression problem under the condition that parameters are unknown as a collaborative robust output adjustment problem of a multi-agent system; s3, constructing directed communication topology to describe information transmission between the motors; and S4, a speed-current loop cascade structure is adopted, a speed loop adopts a distributed internal model control method, a current loop adopts a PI control method, a PI controller is constructed, and a final controller is given. The speed synchronization controller based on the distributed internal model is designed for the conditions of communication constraint and parameter uncertainty in actual control, speed synchronization and interference suppression of the multi-permanent magnet synchronous motor system for the urban rail are achieved, and all motor parameters are allowed to be unknown.

The invention discloses a two-step reconstruction anti-interference PID control method and a control method of an SCR flue gas denitration system, and the method comprises the steps: carrying out cascade active-disturbance-rejection control reconstruction, decomposing a controlled object of a cascade PID control system into a determined part model and an uncertainty part model containing system disturbance through virtual decomposition, and carrying out the reconstruction of the cascade active-disturbance-rejection control reconstruction on the basis of the determined part model, constructing a cascade active-disturbance-rejection control system of which an inner ring adopts internal model control and an outer ring adopts active-disturbance-rejection control; carrying out anti-interference PID control reconstruction, equivalently reconstructing the cascade active-disturbance-rejection control system to form an anti-interference PID control system by adopting an improved disturbance observer, and decomposing system disturbance into negligible internal disturbance and external disturbance which generates influence on a controlled variable and meets a preset requirement by the improved disturbance observer; and performing two-step reconstructed anti-interference PID control based on the anti-interference PID control system.

The invention discloses a powder grinding multi-target cooperative control method and system, and belongs to the field of industrial process automation control, and the method comprises the steps: extracting powder concentrator load and mill main motor power time sequence data, separating internal model components based on variational mode decomposition, and combining with residence time distribution to generate a working condition quality mapping matrix; performing least square polynomial regression and numerical control comparison on the working condition quality mapping matrix to construct candidate control parameters; calculating an internal model component instantaneous frequency variance to construct a dynamic weight, selecting a candidate vector with the minimum weighted sum, and executing amplitude truncation to generate a control instruction; collecting new data to calculate a prediction residual vector, and executing least square iteration update to generate a correction regression equation; and eliminating abnormal lines of the working condition quality mapping matrix by utilizing a quantile boundary on the prediction residual error sequence, adding new data and executing secondary calibration. According to the method, data-driven closed-loop control is adopted, and the adaptive capacity and control precision of the model to nonlinear working conditions can be improved.

The invention relates to an anti-interference performance analysis and optimization method for a two-dimensional photoelectric turntable based on LDO-fuzzy PID, and the method comprises the steps: building an electromechanical coupling virtual prototype of the two-dimensional photoelectric turntable, and constructing a position-speed-current three-ring series closed-loop control system; a linear disturbance observer LDO is designed, input / output signals of a three-ring series closed-loop control system are monitored in real time, external disturbance and the uncertainty disturbance quantity of a system internal model are reconstructed, and the disturbance quantity is injected into a control loop after being processed by a low-pass filter to achieve disturbance compensation. Dynamically adjusting a control parameter by taking the difference signal and the difference differential signal as input and taking PID parameter increment as output through a fuzzy PID (Proportion Integration Differentiation) controller; and constructing an LDO-fuzzy PID composite control strategy, cooperatively integrating the disturbance compensation output of the linear disturbance observer and the parameter adaptive adjustment output of the fuzzy PID controller, and accessing the three-ring series closed-loop control system. The problems of overshoot and insufficient anti-interference of the traditional PID in a disturbance environment are solved.

The invention relates to the technical field of data center and server heat dissipation management, and discloses a server fan rotating speed self-adaptive control method and device based on an internal model control algorithm, and the method achieves the dynamic adjustment of the fan rotating speed through the real-time monitoring of the server load and the environment temperature, thereby improving the heat dissipation efficiency of a server, and reducing the energy consumption. In order to enable a server fan to consider conditions such as energy consumption, noise and heat dissipation at the same time, the invention provides a self-adaptive control algorithm, the fan rotating speed based on temperature errors is adjusted through a PID control algorithm, meanwhile, a fuzzy control rule is used, and the fan rotating speed is dynamically adjusted according to the current temperature and load state. The internal temperature of the server is kept in an ideal range, and overheating is avoided. The fan rotating speed is reasonably adjusted according to load and temperature changes, and power consumption is reduced. And real-time monitoring and dynamic adjustment are combined, so that the system is kept stable and reliable under various working loads.

This application discloses a time-varying formation tracking method and system for multi-agent systems based on the internal model principle, relating to the field of disturbance suppression. The method includes: initializing all relevant parameters; designing two observers based on the relevant parameters to estimate the state and input of the unknown leader, ensuring observation is completed within a preset convergence time; designing an internal model dynamic compensator; determining the time-varying formation tracking error based on the state estimation result of the unknown leader; determining the formation tracking compensation term based on the multi-agent system parameters; determining the adaptive estimation update law based on the time-varying formation tracking error; and determining the final control law based on the input estimation result of the unknown leader, the internal model dynamic compensator, the time-varying formation tracking error, the formation tracking compensation term, and the adaptive estimation update law. This application, by designing a distributed preset-time observer and an internal model-based controller, can realize time-varying formation tracking in multi-agent systems with unknown disturbances and non-autonomous leaders.

The invention relates to the technical field of data processing of a control system, in particular to an internal model control system based on real-time self-adaptive online identification of an industrial system, which comprises a real-time data acquisition unit for acquiring an actuator opening instruction value, an actual controlled system output value and a model controlled system output value; the deviation calculation unit outputs a deviation value between the actual output value of the controlled system and the output value of the model controlled system; the dynamic parameter identification unit generates a weight factor in a linear proportional relationship with the square of the deviation value, outputs an adjusted gain parameter when the weight factor is greater than a preset threshold value, and outputs an adjusted time constant parameter when the weight factor is smaller than the preset threshold value; the internal model control model updating unit reconstructs a transfer function model; the controller parameter optimization unit outputs an optimized actuator opening instruction value; and the control execution unit drives the execution mechanism to enable the actual controlled system output value to respond to the instruction change. According to the method, the problems of dynamic response degradation and steady-state error increase caused by fixed parameter model mismatch are solved.

The application discloses a harmonic suppression method based on frequency adaptive resonance control and belongs to the field of power system harmonic suppression. In view of the problem that SVG under a traditional control strategy is difficult to effectively suppress harmonics, a resonance control strategy is designed based on an internal model theorem and is applied to a voltage control link of SVG. Meanwhile, in order to adapt to the uncertainty of harmonic frequency, a recursive least square method is introduced to identify the harmonic frequency, the resonance control parameter is dynamically adjusted, and the harmonic compensation bandwidth of the SVG device is widened. The simulation result shows that the suppression method provided by the application is simple in operation, can effectively eliminate the (intermediate) harmonic of the offshore wind power system without additionally adding electrical primary equipment, and has strong robustness and adaptability.

The invention belongs to the technical field of robots, and particularly relates to a robot joint motor dual-port two-degree-of-freedom fuzzy adaptive internal model control method based on inertia identification. Comprising the following steps: step 1, designing a robot joint motor rotating speed ring dual-port two-degree-of-freedom internal model controller; 2, designing an improved recursive least square rotational inertia identification method with a forgetting factor; and 3, designing a fuzzy adaptive law to realize dual-port two-degree-of-freedom fuzzy adaptive internal model control. Double-port two-degree-of-freedom internal model control is adopted for the rotating speed ring, the rotating speed falling amplitude after sudden load adding is reduced, and the return time after load disturbance is shortened. The invention provides an identification method capable of accurately identifying the rotational inertia under the condition that the rotating speed of the motor is steady, and automatic setting of controller parameters is realized by combining a fuzzy adaptive law.

The invention discloses a permanent magnet synchronous motor adaptive position control method based on an internal model, and belongs to the field of motor control. The method comprises the following steps: firstly, establishing a mathematical model of the permanent magnet synchronous motor under a coordinate system, and describing a position tracking control problem under the condition that the interference frequency is unknown as a self-adaptive position tracking control problem; then, an internal model is constructed to convert the problem into a self-adaptive adjustment problem, and a dynamic compensator and coordinate transformation are introduced to convert the self-adaptive adjustment problem into a self-adaptive stabilization problem; a cascade control structure is adopted in the design of the controller, a self-adaptive controller based on an internal model is designed for a position-speed ring, a PI controller is constructed for a current ring, and a final controller is obtained. According to the method, the problem of high-precision position tracking of the permanent magnet synchronous motor under the conditions that the load torque interference amplitude, phase and frequency are unknown and the motor parameters are unknown can be solved, and the method has excellent position tracking performance and practicability.

The application provides a ship-shore power grid connection control method, comprising the following steps: sampling the output voltage of a shore power inverter device and the output voltage of a ship generator under load, and performing grid connection when the grid connection condition is met; after the grid connection, the output of the shore power inverter device is adjusted by using fuzzy internal model control based on droop control, and flexible grid connection between the ship and the shore is completed; the output of the shore power inverter device is adjusted by using fuzzy internal model control based on droop control, the controller parameter design is simplified, the control precision, the stability of the grid connection voltage and the frequency are improved, and the system robustness is improved.

The embodiment of the application discloses a gain self-adjusting discrete current regulation method of a permanent magnet synchronous motor, relates to the technical field of motor control, and can reduce the calculation amount of high-performance vector control of the permanent magnet synchronous motor while guaranteeing robustness. In the application, a discrete time domain permanent magnet synchronous motor complex vector mathematical model is established, a complex vector current regulator is designed through zero-pole configuration and internal model principle, and a multi-gain parameter online identification and self-adjusting method is provided. The current control method can realize high-performance vector control of the permanent magnet synchronous motor, and has the advantages of small calculation amount, strong robustness and the like.

The application relates to the technical field of industrial process automation control, and discloses an intelligent adjustment method for an industrial waste liquid low-temperature evaporation process, which comprises the following steps: superimposing a pressure step test excitation on a vacuum adjustment valve and synchronously collecting a temperature response data stream, calculating an absolute error integral of a response curve, SIAE and a recovery time characteristic, and substituting the absolute error integral, the SIAE and the recovery time characteristic into an inverse model to solve an equivalent process gain and a time constant of a controlled object; based on an internal model control principle, PID controller parameters are dynamically calculated and updated by using the solved gain and time constant, a control law is reconstructed to generate an execution instruction, through active energy characteristic identification and a pole configuration mechanism, the application solves a control divergence problem of a nonlinear time-varying object under model mismatch, realizes decoupling control and self-adaptive adjustment of fluid viscosity mutation and equipment fouling, and improves the stability and control precision of a system.

The application discloses a kind of based on neural network and approximation type internal model control infusion heating control method, comprising the following steps: step S1: with the form of full power heating, the system response curve of infusion heating system is obtained, and infusion heating parameter is obtained based on system response curve;Step S2: based on infusion heating parameter, the equivalent controller model of infusion heating system is established;Step S3: construct the approximation type internal model control system based on neural network and equivalent controller model, realize the control of infusion heating.The application can be automatically adjusted according to temperature deviation, real-time correction, online optimization, and quickly and stably reach target heating temperature.

The invention discloses an airborne photoelectric tracking system turntable broadband disturbance suppression method based on internal model control and an extended state observer. Belongs to the technical field of servo control systems. The technical problem that in the prior art, an airborne laser communication platform cannot effectively suppress low-frequency attitude disturbance and high-frequency vibration at the same time, and consequently tracking precision is reduced is solved. Comprising the following steps: S1, establishing a controlled object mathematical model of an airborne photoelectric tracking system turntable; s2, designing a two-degree-of-freedom internal model controller based on the mathematical model in the step S1; s3, based on the mathematical model in the step S1, constructing an extended state observer for estimating and compensating lumped disturbance of the system in real time; s4, forming a composite control law, and realizing dynamic fusion control of internal model control and the expansion state observer; and S5, performing equivalent transformation on the fusion control system formed in the step S4 to obtain a whole control link which takes the airborne photoelectric tracking system turntable as a controlled object and takes the two-degree-of-freedom internal model controller and the extended state observer as controllers.

The application belongs to the technical field of artificial intelligence and machine learning, and discloses an intelligent collaboration method and system based on large model integrated reasoning, and proposes a general reasoning aggregation mechanism without accessing the internal model, which realizes the collaborative decision of multiple models by structuring the combination and dynamically weighting the prediction results of multiple independent models. The mechanism can realize information fusion under the condition that the models are completely independent, so that the prediction results of each model are complementary, and the reasoning consistency and comprehensive judgment ability are improved. By constructing a hybrid mechanism of "unsupervised basic aggregation + a small amount of label guided calibration", the model prediction deviation can be quickly corrected and the effective feature alignment can be strengthened without reconstructing the integrated architecture, so that the prediction accuracy and stability of the integrated system on the target task are significantly improved. The StackingNet framework proposed by the application can automatically estimate the reliability weight of the output correlation between models under the condition of limited or zero labeling, and realize sorting and pruning by using the weight distribution.

The invention relates to the technical field of thermal generator set control, in particular to an SCR denitration control method and system based on load prediction feedforward and prediction compensation feedback, a medium and a program product. The method comprises the steps that the flue gas temperature and flow are predicted through boiler load signals, a theoretical ammonia demand calculation model is established, and the theoretical ammonia demand calculation model is calculated; calculating a theoretical ammonia injection feedforward demand quantity; identifying and establishing a dynamic characteristic model of the SCR denitration system based on historical data of the opening degree of an ammonia spraying valve, the ammonia spraying amount and the NOx concentration of an outlet; constructing a pre-estimation compensation control framework, forming a pre-estimation error by utilizing the deviation between the internal model output and the actual output, and calculating the ammonia spraying correction amount; an ammonia spraying valve control instruction is generated according to the ammonia spraying feedforward demand quantity and the feedback correction quantity, opening and closing of an ammonia spraying valve are controlled in advance, and the NOx concentration of an outlet is rapidly adjusted. According to the method, the problems of large lag and the like existing in denitration control can be solved, the change of the opening degree of the valve is predicted, advanced adjustment is achieved, and optimal control over the SCR denitration system is achieved.

The invention discloses a current tracking control method of a cascaded H-bridge energy storage converter system based on a high-order internal model, and belongs to the field of new energy power generation. The current tracking control method specifically comprises the following steps: 1, constructing an equivalent circuit model, and obtaining a mathematical model of a cascaded H-bridge energy storage converter system in combination with dynamic characteristics of inductance and capacitance; 2, selecting a state variable, and constructing a state space expression; 3, introducing a high-order internal model of an iteration domain to obtain an expected trajectory with the high-order internal model; 4, proposing a P-type iterative learning control law based on a high-order internal model according to the state space expression; and 5, inputting the deviation between the current reference value and the feedback value into an iterative learning control algorithm, and carrying out iterative optimization to realize current tracking. The method can effectively deal with the iteration variable expected trajectory problem, and maintain the stable operation of the cascaded H-bridge energy storage converter system, thereby enhancing the adaptive capacity of the cascaded H-bridge energy storage converter system to a complex power grid environment.

This invention discloses an adaptive internal model PID control method for desulfurization systems based on particle swarm optimization and multi-model weighting, belonging to the field of industrial process automatic control technology. Addressing the control challenges of nonlinearity, large inertia, large delay, and variable operating conditions in limestone-gypsum wet flue gas desulfurization systems, this invention first establishes multiple first-order inertial plus lag process models under different typical loads to form a multi-model set, and designs corresponding internal model PID controllers. Second, a composite weighting mechanism integrating load priors and real-time errors is designed to weight and fuse the outputs of each sub-controller to generate the control quantity. Finally, the particle swarm optimization algorithm is used to globally and collaboratively optimize the key parameters of the sub-controllers and the weighting mechanism parameters to optimize the overall control effect of the system under large load variations. This invention significantly improves the dynamic response, steady-state accuracy, and robustness of the desulfurization system under large load variations through multi-model coverage of nonlinearity, smooth weight switching, and PSO global optimization, demonstrating strong engineering applicability.

The application discloses an intelligent industrial control system for a via filling electroplating process of an HDI board, belongs to the field of intelligent manufacturing of circuit boards, and is applied to an electroplating device comprising a PCB board as a main cathode, a conductive hanger and a contact thereof, and an auxiliary shielding electrode arranged around the contact. The system comprises an initial electric field non-uniformity modeling module, a dynamic electric field compensation control module, a post-plating quality evaluation module and a model parameter self-adaptive correction module. The application introduces the post-plating quality evaluation and model parameter self-adaptive correction module, and constructs a complete prediction-control-evaluation-correction closed loop system. This enables the system to learn from the production result each time, automatically correct and optimize the internal model, and adapt to the slow drift of the process conditions.

This invention provides a method for tracking the trajectory of a fully driven ship based on predictive optimization control, belonging to the fields of marine technology and intelligent navigation technology. The invention involves the following steps: constructing a basic dynamic nonlinear model based on the ship's three degrees of freedom; transforming the basic dynamic nonlinear model into a linear steady-state system using a parametric design method; constructing an enhanced error system and an internal model compensator using the linear steady-state system; determining the controller form by combining the enhanced error system and the internal model compensator; and introducing a predictive term to obtain the optimal controller. This invention optimizes the calculation process of control inputs, reduces the computational burden of real-time control, and achieves an optimized control strategy; furthermore, the predictive controller can respond more quickly to dynamic trajectory changes, shorten the system's adjustment time, improve adaptability to complex trajectories, and achieve rapid response.