129 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 invention provides an intelligent network connection vehicle distributed optimization control method based on a reinforcement learning strategy, which has a layered structure, and comprises the following steps: establishing a vehicle longitudinal dynamics model, and setting control targets of an upper layer and a lower layer; designing an upper layer, namely a trajectory optimization control layer; a trajectory planning controller based on a distributed convex optimization algorithm in combination with an internal model principle, wherein the controller designed in combination with the internal model principle removes external interference in an upper-layer trajectory planning process; designing a lower layer, namely a tracking control layer; the optimal trajectory tracking controller is based on a reinforcement learning Actor-Critic framework; the stability and convergence of a tracking control system are analyzed through a Lyapunov stability criterion, and accurate tracking of a reference trajectory is guaranteed; the feasibility of the algorithm is verified through a simulation experiment. According to the method, the problems of trajectory planning and trajectory tracking control in networked vehicle queue control are researched, and the dependence on an accurate vehicle kinetic equation is eliminated.

The invention proposes an ultra-wideband power grid simulation method based on improved repetitive control, and the method comprises the steps: carrying out the reconstruction of a repetitive controller from an internal model and a compensator, and obtaining a self-adaptive control inner loop system matched with the compensator through deduction; secondly, a plurality of internal model branches considering the normal deviation of the power grid frequency are connected in parallel, delay parameters of each branch are accurately designed by using a fractional order delay filter, and improvement is performed by using a compensator, so that the requirement of ultra-wideband high-performance output of the power grid simulator is met.

The invention relates to the technical field of motor control, and particularly provides a permanent magnet synchronous motor predictive current control method, which comprises the following steps of: establishing a basic mechanism equation of an inner ring and an outer ring in permanent magnet synchronous motor MPCC control, and introducing a lumped disturbance term and a current ripple term into a cost function of the inner ring, the robustness of the MPCC to mismatch of inductance, permanent magnet flux linkage and resistance parameters is improved; and a strong tracking dimension expansion Kalman filtering torque observer is designed on an outer ring, so that the influence of load torque disturbance and sensor measurement noise on the control performance of the motor is reduced. According to the method, the robustness of the MPCC on disturbance such as model internal parameter mismatch and model external uncertain load torque can be enhanced at the same time.

The embodiment of the invention discloses a voice coil motor fast reflector generalized internal model control method and device and a medium, belongs to the technical field of voice coil motor fast reflectors, and solves the problem that the performance of a controller is reduced due to perturbation of system parameters. Comprising the following steps: analyzing input and output data of the voice coil motor fast reflector system to obtain a corresponding nominal model; and performing loop shaping on the nominal model through a pre-compensator, and performing loop shaping controller calculation based on a standard H-infinity control algorithm to obtain a performance controller. The additive uncertainty of the nominal model and the model under the real working condition is determined, and an upper bound function corresponding to the additive uncertainty is obtained through curve fitting. And performing iterative processing on the preset controller to obtain a robust controller. And based on the performance controller and the robust controller, solving to obtain an inner ring controller so as to realize generalized internal model control of the voice coil motor fast reflector.

The invention discloses a periodic disturbance compensation method and device for an input time delay system, equipment and a medium, and relates to the field of disturbance control, and the method comprises the steps: obtaining a state-space equation of the input time delay system; according to the state-space equation of the input time-delay system, converting the input time-delay system into a non-input time-delay system by adopting a time-delay compensation method; based on the error signal of the no-input time delay system, estimating the value of the to-be-determined parameter by adopting an internal model principle and a linear regression method to obtain an estimated value of the to-be-determined parameter; and determining a control input signal of the zero-input time-delay system by adopting the feedback controller according to an output signal of the zero-input time-delay system and the to-be-determined parameter estimation value so as to perform periodic disturbance compensation on the zero-input time-delay system. The feedback controller is adopted to perform feedback control on the control input signal according to the output signal of the system, and compensation of unknown time-varying periodic disturbance of the input time delay system can be realized.

The invention discloses an ultra-supercritical unit rapid dynamic response method based on multiple models, and relates to the technical field of thermal power generation. According to the method, a nonlinear control method which unifies internal model control and generalized predictive control is provided, and nonlinear control over an ultra-supercritical unit (USC) system is effectively achieved: firstly, through internal model control based on LSTM-Transform, the USC system achieves rapid convergence to be close to an expected value, and the response speed of a load is remarkably increased; secondly, the model is further optimized by enhancing a composite weighted human learning optimization network (CWHLO), and the accuracy of the model is improved; and finally, a generalized predictive control (CWHLO-GPC) algorithm based on an enhanced composite weighted human learning optimization network is adopted to control the USC system, so that high-precision load tracking performance is realized, and the problem of slow dynamic response of the ultra-supercritical unit system is solved.

The invention discloses an industrial process control system performance evaluation method, which is based on a system model, combines an internal model principle to design an equivalent PID (Proportion Integration Differentiation) controller, then calculates a control performance index of a closed-loop system, carries out contrastive analysis by combining a performance index of a current actual system, quantifies the deviation between ideal performance and actual performance, and finally evaluates the performance of an industrial process control system. And finally, giving out an evaluation result of the system performance. According to the method, the performance of the control system can be accurately evaluated, a quantitative basis is provided for controller optimization design and system performance improvement, and the evaluation accuracy is improved.

The invention belongs to the technical field of anti-interference, and discloses a high dynamic response anti-interference and noise suppression method and device for a robot, and the method comprises the steps: building an equivalent input interference state space model of a robot control system; designing a state observer, and obtaining a state observation value according to the system output of the robot control system and the state observer gain of the state observer; designing an internal model system, and establishing a state feedback controller of the robot control system according to the state observer and the internal model system; according to the method, a parallel-cascade interference estimator is designed, the parallel-cascade interference estimator comprises a plurality of equivalent input interference estimators and filters, the equivalent input interference estimators are connected in a mode of first parallel connection and then cascade connection, and a corresponding disturbance suppression feedback control law is designed. Through the combination of parallel connection and cascade connection, the disturbance estimation error is reduced, the disturbance suppression capability is enhanced, and the influence of measurement noise is remarkably reduced, so that the comprehensive performance of a robot control system is improved.

The invention discloses an underwater vehicle fixed time sliding mode tracking control method based on an all-wheel-drive system theory. Aiming at the problem that an underwater vehicle executes an accurate trajectory tracking task under unknown external interference and internal model uncertainty, a second-order all-wheel-drive system model of the underwater vehicle is constructed based on a six-degree-of-freedom tracking error, so that excessive simplification of coupling characteristics by a traditional decoupling method is avoided; meanwhile, a fixed time disturbance observer based on an all-drive system model is designed, external disturbance and internal parameter perturbation are estimated, the system is compensated in real time, and a fixed time sliding mode controller is provided by further combining a fixed time stability theory. The provided sliding mode control method based on the all-wheel-drive system theory can effectively cope with disturbance in the underwater environment, enables tracking errors to converge to zero within fixed time, and achieves accurate trajectory tracking control over the position and attitude of an underwater vehicle system.

The invention discloses a permanent magnet synchronous motor adaptive speed control method based on a time-varying internal model. The method comprises the following steps: S1, establishing a permanent magnet synchronous motor mathematical model; s2, describing a speed tracking and interference suppression problem of the permanent magnet synchronous motor under the condition that the external interference frequency is unknown and time-varying as a self-adaptive servo control problem; s3, designing a time-varying internal model, and converting an adaptive servo control problem of the motor system into a robust stabilization problem of an augmented system composed of the motor system and the time-varying internal model; and S4, a speed-current loop cascade structure is adopted, a self-adaptive controller based on a time-varying internal model is designed, a PI controller is designed for a current loop, and a final controller is given. According to the method, all motor parameters are allowed to be unknown, through the adaptive controller based on the time-varying internal model, the problem of high-precision speed tracking control of the permanent magnet synchronous motor under the conditions that the external interference frequency is unknown and the time is varying is solved, and good speed tracking performance is achieved.

The invention relates to a plug-in type second-order feed-forward dual-mode repetitive control method. The method comprises the following steps: establishing a single-phase LCL type grid-connected inversion model based on a PP-SFDMRC controller; according to the single-phase LCL type grid-connected inversion model, a transfer function of a controlled object is obtained, a PP-SFDMRC controller structure is discretized, and a discrete transfer function of an internal model of the SFDMRC is obtained; the method comprises the following steps: acquiring an error transfer function of a PP-SFDMRC controller based on an SFDMRC internal model discrete transfer function by taking SFDMRC internal model stability as a target, converting the error transfer function into a characteristic polynomial, and determining a stability condition of the PP-SFDMRC controller; through stable conditions, corresponding parameters of the PP-SFDMRC controller are set, and odd and even harmonics are controlled to different degrees. The PP-SFDMRC strategy provided by the invention has excellent dynamic performance and super-strong adaptability to power grid frequency change.

The invention discloses a multi-agent variable expected trajectory iteration consistency tracking method and system. The method comprises the following steps: constructing a dynamic model and a network communication topological graph of a multi-agent system; determining a multi-agent tracking error, constructing a controller through a P-type open-loop learning algorithm of a high-order inner membrane, performing iterative control on a dynamic model of the multi-agent system, and outputting the multi-agent system with consistent tracking; verifying the multi-agent system with consistent tracking according to a compression mapping principle and norm properties; and based on the verified multi-agent system, tracking the multi-agent through a P-type iterative learning control algorithm based on a second-order internal model. According to the method, the corresponding iterative learning controller based on the high-order internal model can be designed for the intelligent agent on the basis of the observation error, and the consistency tracking precision of the variable expected trajectory is improved. The multi-agent variable expected trajectory iteration consistency tracking method and system can be widely applied to the technical field of intelligent control.

The invention discloses a dry quenching circulating air volume online prediction method and system based on an AGRU-PLS model, and belongs to the technical field of dry quenching production process intelligent prediction. According to the method, the production process data of the dry quenching full-process technology is collected, and a corresponding information database is constructed and stored; preprocessing the data, including cleaning and filling missing data, and finally performing standardization processing on the data; performing feature processing on the data in a correlation analysis mode, and finding out variable features with high correlation with the circulating air volume in the whole process; establishing an AGRU-PLS model, establishing a characteristic and circulating air volume covariance maximum model through an outer model, and solving a data collinearity problem in a mode of solving latent variables by iteration; the method comprises the following steps: establishing an Attention-GRU internal model, and mining nonlinear and dynamic relationships among data; the model is updated, and online prediction of the circulating air volume is achieved; and the prediction result of the circulating air volume is converted into a given value of the circulating fan, and the given value is written into UaExpert software to achieve control over the circulating fan.

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 invention discloses a high-damping internal model control method based on a precise discrete model, and belongs to the technical field of high-speed motor disturbance suppression, and the method comprises the steps: firstly, carrying out the precise reconstruction of a complex vector model of a surface-mounted permanent magnet synchronous motor in a discrete domain according to the complex vector model; through systematic exploration and research of a traditional internal model control strategy and mathematical model deduction analysis, a pole distribution rule of an internal model control system is found, damping analysis of the control system is combined, a damping modulation loop is designed according to an expected high-damping dominant pole, and a discrete domain high-damping current controller is designed according to an internal model control theory. By adopting the high-damping internal model control method based on the precise discrete model, the problem that a traditional controller is poor in disturbance suppression effect under the low-carrier-frequency-ratio working condition of the high-speed motor is solved, the running stability of the high-speed motor in a wide speed range is enhanced, and the stability of a control system under the low-carrier-frequency-ratio condition is improved.

The present invention discloses a method for robust complex coefficient repetitive control of a permanent magnet synchronous motor based on a position domain internal model, which relates to the technical field of permanent magnet synchronous motor control. First, the time domain periodic characteristics of the spindle speed are used to construct an improved time domain complex coefficient repetitive controller. Second, based on the position domain periodic characteristics of the cutting force, a position domain repetitive extended state observer is constructed to online estimate non-periodic disturbances such as parameter perturbations and position domain periodic disturbances caused by the cutting force. Finally, the separation design principle and the small gain theorem are applied to derive the system stability conditions, and an optimization algorithm is used to synchronously optimize the controller parameters. The system design method based on time domain complex coefficient repetitive control and position domain repetitive extended state observer of the present invention designs an improved complex coefficient repetitive control system based on the position domain repetitive extended state observer to achieve feedforward compensation for position domain periodic disturbances and non-periodic disturbances and fast and high-precision tracking of time domain periodic reference speed signals.

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.

Methods and systems are provided for improved prediction and generation of structure interiors. In one aspect, a method is provided that includes receiving an exterior image of a structure and using a machine learning model to determine an exterior surface of the structure. The exterior surface may surround an exterior portion of the structure. The machine learning model may further determine exterior features of the structure and may determine an interior model of the structure based on the exterior surface of the exterior features. Based on the exterior surface and the interior model, a three-dimensional representation of the interior and exterior portions of the structure may be generated. TIFF2025503485000002.tif111159

The invention provides a distributed power supply aggregation additional control method and a related device. The distributed power supply aggregation additional control method comprises the following steps: acquiring a coupling variable in a system; determining an independent variable by using a relative gain matrix based on the coupling variable; constructing a control model based on internal model control and pipeline model prediction control; adapting the uncertainty of the voltage deviation based on the independent variables and control model predictive control; according to the distributed power supply aggregation additional control method provided by the invention, a controller with separated performance is designed, dynamic response and error compensation processes of aggregation additional control and aggregation robust control are separated, control decoupling is realized, and the problem that parameters of the additional controller are difficult to independently optimize is solved; meanwhile, the influence of an additional controller on the stability and dynamic performance of an original control system is restrained, and the uncertainty possibly causing more serious voltage deviation can be adapted through accurate prediction.

The invention relates to the technical field of semiconductor test equipment, and discloses a multivariable decoupling method for a temperature control system of test equipment, comprising the following steps: S1, establishing a generalized internal model control framework containing a main response and a coupling term; s2, multi-frequency orthogonal disturbance signals which do not interfere with master control are injected into control input, and coupling characteristics are detected; s3, collecting controlled output; s4, extracting an output response of the disturbance frequency component by using multi-channel synchronous demodulation; s5, calculating a frequency response data point of the coupling path according to the response and disturbance amplitude; s6, fitting the dynamic model to obtain coupling path parameters; s7, using the fitting model to update coupling terms of the GIMC internal model matrix; and S8, decoupling compensation is adjusted in real time. According to the invention, on-line adaptive identification and compensation of coupling characteristics are realized, the precision, stability and response speed of temperature control of semiconductor test equipment are improved, and coupling characteristic time variation of the test equipment caused by working point change or equipment aging can be automatically adapted.

The present application relates to an engine line spectrum noise estimation method and a vehicle active noise reduction control method, which includes: calculating the main order frequency of the engine line spectrum noise according to the real-time speed of the engine; obtaining the primary noise through the internal model control framework, and performing spectrum estimation on the primary noise through the frequency estimation algorithm to obtain the main order frequency and harmonic frequency of the engine line spectrum noise; using a pre-trained neural network model as an engine frequency prediction model, combined with engine operation data as input, and outputting the predicted main order frequency and harmonic frequency of the engine line spectrum noise in real time; performing a comprehensive analysis on the above frequency prediction data to generate the final order frequency corresponding to the engine line spectrum noise. The present application combines the comprehensive output results of different estimation schemes to generate an order frequency that is closer to the actual noise, reduce the impact of frequency imbalance on engine noise control, thereby achieving a better order noise control effect and providing users with a better noise reduction experience.

The invention belongs to the field of lead-bismuth fast reactor advanced control system design, and discloses a lead-bismuth fast reactor advanced control system and a design method.The method comprises the steps that a lead-bismuth fast reactor model is built, and the lead-bismuth fast reactor model comprises two controlled object models of a reactor core and a once-through steam generator; on the basis of the built lead-bismuth fast reactor model, characteristic analysis of controller output anti-disturbance and anti-noise capability is carried out for various advanced control algorithms, and characteristic analysis results corresponding to the advanced control algorithms are obtained; based on a characteristic analysis result, screening to obtain an advantage advanced control algorithm; based on an advanced advantage control algorithm, a reactor core power self-disturbance controller, a steam pressure internal model controller and a main steam flow internal model controller are designed, so that the advanced control system of the lead-bismuth fast reactor is designed. The advanced control system designed by the method has good response speed and stability; and the lead-bismuth fast reactor can meet the application requirements of complex operating environment and variable working conditions.

The present invention discloses a method for adaptive attitude control of a flexible aircraft based on an internal model, and relates to the field of flexible aircraft system control; the method comprises: step 1, establishing a kinematic and dynamic model of a flexible aircraft system; step 2, designing an internal model; step 3, designing an adaptive state feedback controller based on the internal model. The present invention designs an adaptive state feedback controller based on an internal model for a flexible aircraft system that is subjected to external interference and has an unknown interference frequency, which can not only realize interference suppression and vibration suppression of the flexible aircraft, but also ensure that the estimated frequency converges to its true value.

The invention discloses an inverter special waveform control system which comprises a repetitive control module, and the repetitive control module comprises a repetitive control internal model, an advanced link zm, a gain parameter Kr and a forward low-pass filter S (z) which are connected in sequence. The repetitive control internal model comprises a backward low-pass filter Q (z), a backward delay link z-N and a forward delay link z-N. A high-frequency special waveform is controlled by applying a repetitive control method, and a control system can have very high gains on fundamental waves and infinite-dimensional harmonic waves by introducing an internal model of a periodic signal, so that tracking of the high-frequency special waveform is realized.

The invention discloses a temperature reducing valve cascade control system and method based on an improved internal model-active disturbance rejection controller. The system comprises a first comparator, an internal model controller, the improved active disturbance rejection controller, a temperature reducing valve, a temperature sensor, a superheater, a temperature sensor and a fifth comparator which are sequentially arranged. According to the method, the main steam temperature control quality of transient working conditions such as peak regulation and frequency modulation of the thermal power generating unit can be remarkably improved; the improved ADRC is adopted in an inner ring of the cascade system to estimate and compensate disturbance in real time, and compared with PID control, the improved ADRC is more suitable for the complex multi-source characteristic of main steam temperature disturbance, and the anti-interference capability of the system can be improved; and internal model control is adopted in an outer ring of the cascade system, compared with PID control, the internal model control has a better control effect on the characteristics of large inertia and large delay of the main steam temperature, and the set value tracking performance of the system can be improved.

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 discloses a control method and system for deep peak regulation of a 1000MW secondary reheating unit, and relates to the technical field of thermal power unit peak regulation control. Aiming at the problems of thermal inertia lag, multivariable coupling and low-load stability during deep peak regulation of a unit, a technical system is constructed from three dimensions of boiler side optimization, steam turbine protection and coordinated control upgrading: a three-stage stable combustion system of plasma strengthening, powder preparation cooperation and smoke temperature regulation is used for assisting a boiler to maintain stable combustion under low load; a parameter early warning-dynamic correction-rate grading protection system is adopted to assist safe operation of the steam turbine; the coordination control system is upgraded based on'multivariable feedforward + internal model optimization 'to relieve multivariable coupling influence. Meanwhile, through the implementation path of layered modeling, logic integration, staged verification and all-working-condition optimization, safe and economical deep peak regulation of the unit within the 30%-100% load interval is facilitated. According to the method, the unit peak regulation adaptability and stability can be improved, and the energy consumption and equipment loss risk can be 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.