260 results about "Predictive controller" patented technology

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention belongs to the technical field of temperature control, and discloses a temperature control method and device of a hot runner system for an inverted mold. The method comprises the following steps: defining a critical melt area in a physical sprue which directly influences the product quality in a sprue of a hot runner system as a virtual sprue; dynamically generating a melt viscosity target track of the virtual gate region according to the type of an injection molding material and an injection molding process stage; based on measurable injection molding process parameters and sprue area temperature, estimating a current melt viscosity value of a virtual sprue area in real time through a state observer; calculating to obtain an optimal power control sequence by adopting a multivariable model prediction controller; and driving a sprue area heater according to the optimal power control sequence, so that the actual change of the melt viscosity of the virtual sprue area tracks a melt viscosity target track. According to the invention, the defects such as salivation and wire drawing can be effectively eliminated, so that the sprue quality stability and the production efficiency are improved.

The invention discloses an operation control method for a wind power generation system, and relates to the technical field of operation control, and the method comprises the steps: obtaining the historical operation data of a wind power generation system in a target sea area, taking an offshore environment factor as an interference factor, and coupling the time-space parameters of ocean turbulence intensity, wave load and salt spray corrosion rate; establishing a fan-marine environment digital twinborn body; operation data is acquired in real time, synchronous mapping and dynamic updating of the digital twin are realized, and a state prediction model is established in combination with historical operation characteristics; taking the maximum power generation efficiency and the minimum mechanical load as a multi-objective optimization function, and introducing a model prediction controller to obtain an optimal control sequence of the wind power generation system in a future time domain of each control period; and selecting a first control instruction in the optimal control sequence, issuing the first control instruction to a physical fan torque controller and a variable pitch system in real time, realizing rolling optimization of the wind power generation system, and obtaining a self-adaptive operation control scheme of the wind power generation system. The power generation benefit of the whole life cycle is improved.

The invention discloses a welding wire production control system and method based on visual feedback, and belongs to the technical field of precision machining. The system comprises a multi-mode intelligent sensing subsystem, a self-adaptive model prediction controller and a safety and exception handling subsystem, and a two-stage control architecture of a quality closed loop and an execution closed loop is constructed. According to the method, the microcosmic roughness, macroscopic geometry, defects and other multi-dimensional quality characteristics of the surface of the welding wire are obtained in real time through multi-modal sensing and fused into a comprehensive quality index; carrying out rolling optimization by adopting a model prediction control algorithm for updating parameters on line, and generating a control instruction; meanwhile, trend early warning based on statistical process control and sudden fault diagnosis based on model residual analysis are operated in parallel, and active safety protection is achieved. According to the method, the problems of unstable quality, control lag and passive safety caused by supplied material fluctuation and tool time variation in the welding wire flaking machining process are solved, and self-adaptive accurate control and intelligent safety protection in the machining process are achieved.

According to the clearance robot indoor and outdoor fusion navigation method and system, closed-loop detection and error state Kalman filtering are introduced, a feedback mechanism is established, accumulative errors of long-term operation are corrected in a bidirectional mode, and the global navigation precision is ensured. By constructing a multi-level semantic map and fusing laser, visual and inertial data in a tight coupling mode, high-precision and robust positioning and navigation of the cleaning robot in a complex community environment are realized. And on the basis of the closed-loop constraint in the step A5, the system can automatically adjust the prediction time domain and the control time domain of the model prediction controller, so that a smoother, more energy-saving, more stable and safer walking path can be planned. And after real-time pose correction of the filter, a robot pose sequence with higher global consistency is obtained and serves as input of the closed-loop detection module, so that subsequent global pose optimization is more effective and accurate.

The invention discloses a grid-connected inverter control method based on an MAGRNN neural network observer, and aims to solve the problems that a model prediction control method is insufficient in state estimation precision and worsened in control performance under the conditions of model mismatch, parameter offset and power grid voltage distortion. For an LCL type grid-connected inverter, firstly, a prediction model is established based on a state-space equation; secondly, real-time estimation parameters of an inductance parameter state observer are constructed; the method comprises the following steps: firstly, estimating an interference compensation item through a neuron self-adaptive adjustment mechanism, then introducing an MAGRNN neural network as a dynamic error compensator, using input and output of the observer as MAGRNN input, estimating the interference compensation item through the neuron self-adaptive adjustment mechanism, and finally, feeding back the interference item predicted by the MAGRNN to a model prediction controller for interference compensation. By utilizing the strong nonlinear fitting capability of the MAGRNN, the state estimation precision under complex working conditions such as parameter change and power grid disturbance is remarkably improved, and the control performance of the system is effectively improved.

The application provides a kind of intelligent building environment adaptive regulation and control method based on digital twinning and AI, it is related to artificial intelligence technical field, including the following steps: based on time alignment after multi-source system data, extract dynamic characteristics to construct dynamic feature set;Multi-source system data is input to digital twinning, with the current building equipment state as boundary condition, iteratively simulates the key environmental parameters of multiple time steps in the future, and outputs multi-step prediction sequence;Again input model predictive controller, with the minimum overall energy consumption in the prediction period as the optimization goal, with the key environmental parameters not exceeding the comfort interval as the constraint condition, the equipment control instruction sequence in the first preset time in the future is solved;The first equipment control instruction in the equipment control instruction sequence is issued to the on-site actuator to realize the adaptive regulation and control of building environment, solve the disadvantage that the environment parameter is over-standard and then responds passively, significantly improve the rapid response capability of the system to sudden environmental changes.

The invention relates to the technical field of power control, and discloses an energy storage frequency modulation control method and device, electronic equipment and a storage medium. Comprising the following steps: predicting system state variables based on an MPC mechanism model and an LSTM-based load prediction model; based on the system state variable and the load disturbance state variable, outputting a basic power instruction at the current moment through an MPC-based model prediction controller; based on the system state variable and the load disturbance state variable, outputting a power compensation instruction through a reinforcement learning agent based on DDPG; superposing the basic power instruction and the power compensation instruction to generate a target control instruction and acting the target control instruction on a control object; the output state of the control object base is acquired, and the output state is used as a feedback signal. According to the invention, the response speed, the control precision and the operation robustness of the energy storage frequency modulation system to complex power grid working conditions are obviously improved.

The invention discloses an energy storage power station multi-time-scale optimization scheduling method and system fusing adaptive distribution robustness and model prediction control, and the method comprises the following steps: obtaining the data of an energy storage power station, and solving a hierarchical planning model containing weekly-ahead distribution robustness optimization and day-ahead adaptive distribution robustness optimization; obtaining a robust day-ahead energy storage power scheduling plan and a charge state reference trajectory; respectively taking the day-ahead energy storage power scheduling plan and the charge state reference trajectory as a center reference and a terminal constraint of a model prediction controller, and performing rolling optimization solution in each control period by using the model prediction controller to obtain a corresponding power set value; and superposing the power set value with a fine tuning power amount to generate a final energy storage system output power instruction. On the premise of ensuring the robustness of the system, the decision conservative property is obviously reduced, and the economic benefit and the execution precision of the scheduling plan are improved.

The invention discloses an intelligent coating control method and device capable of sensing the state of a coating surface in real time, and relates to the technical field related to intelligent coating control. The method comprises the steps that a multi-source sensor is started to execute data collection, and a coating state multi-source data set is established; after timestamp alignment and drift compensation are carried out, a coating state time sequence data set is established; inputting to a Koopman prediction model under physical constraint, and establishing a coating state prediction sequence; carrying out residual analysis and generating a coating state uncertainty set containing a confidence interval by utilizing Gaussian process regression; inputting into a dual model prediction controller, and executing dual-target optimization analysis; intelligent coating control is performed using the control input trajectory. The technical problems that in the prior art, due to the fact that dynamic changes in the coating process are difficult to sense and predict in real time, the coating control precision is low, and the stability and the coating quality are poor are solved, and the technical effects that high-precision coating control is achieved, and the coating control stability and the coating quality are improved are achieved.

The invention relates to an expressway intelligent driving hierarchical decision-making and control method and system based on a large language model and multiple safety mechanisms, and belongs to the technical field of automatic driving automobile decision-making control. The method comprises the following steps: S1, analyzing multi-source scene information in real time, and extracting state features of surrounding traffic participants; s2, based on the historical driving experience, constructing a semantic-space-time perception retrieval mechanism, and recalling the historical driving experience most related to the current scene from the vehicle-mounted memory; s3, standardizing the reasoning process by adopting a three-step normal form structured thinking chain technology; s4, a cue word technology and small sample learning combination are utilized to guide the large language model to carry out analysis and decision making, and interpretable driving suggestions are generated; and S5, constructing a model prediction controller of a multi-layer security mechanism, and realizing real-time rolling optimization and security redundancy of a control instruction. Compared with a traditional method, real-time decision making, whole-process traceable decision interpretation and high-safety-margin control output can be achieved in the highway scene.

The invention relates to an aiming point dynamic stabilization and automatic calibration method fusing inertial measurement unit data, and belongs to the technical field of aiming equipment dynamic control. The method comprises the following steps: acquiring attitude data of an inertial measurement unit, and constructing a multi-source data matrix in combination with real-time position data of an aiming point and environmental interference data; after time sequence synchronization and drift suppression are carried out on multi-source data, associated feature vectors are extracted through multi-modal feature fusion; inputting a self-adaptive extended state observer to estimate total disturbance and generate an anti-interference compensation amount, constructing a kinetic model in combination with a nonlinear model prediction controller, solving a multi-objective optimization problem, and generating an attitude compensation control sequence to realize dynamic stability of an aiming point; and finally, aiming deviation is calculated in real time to trigger double-closed-loop calibration, an inner ring corrects the drift error of the inertial measurement unit, and an outer ring optimizes parameters of the observer and the controller. According to the invention, environmental interference and inertial drift are effectively suppressed, the dynamic aiming stability is improved, and the long-term aiming reliability is guaranteed through closed-loop calibration.

The invention provides a trajectory tracking control method and system for a high-load omni-directional AGV, the omni-directional AGV is driven by a plurality of steering wheel mechanisms which are independently steered and driven, and the trajectory tracking control method is characterized by comprising the following steps: acquiring vehicle state information and expected trajectory information of the omni-directional AGV in real time; on the basis of the vehicle state information and the expected track information, core parameters of a model prediction controller are optimized online through a chaos simulated annealing algorithm, and the core parameters comprise a state weight matrix, a control increment weight matrix, a prediction time domain and a control time domain; wherein the model prediction controller performs state prediction based on a longitudinal-transverse decoupling dynamic model adaptive to a high-load working condition; performing rolling horizon optimization through the model prediction controller by utilizing the optimized core parameters, and solving to obtain an optimal control increment; and converting the optimal control increment into a driving instruction for a multi-steering-wheel mechanism, and controlling the omnidirectional AGV to track an expected trajectory.

The invention provides a vehicle steering control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a measurement state parameter of a vehicle at a current moment, and obtaining a prediction error vector according to the measurement state parameter and a prediction state parameter at a previous moment; adjusting the weight configuration of a model prediction controller according to the prediction error vector, and inputting a preset vehicle dynamics model, the measurement state parameters and a reference state determined according to the steering angle of the steering wheel of the driver into the adjusted model prediction controller to obtain a target steering control quantity; and generating a target power-assisted torque according to the target steering control quantity so as to control the steering of the vehicle. According to the method and the device, the optimal balance of the safety, the stability and the control performance of the vehicle can be realized under various road and driving conditions.

The invention relates to an integrated state estimation and motion control method for an automatic driving vehicle, and the method comprises the steps: building an implicit mapping function between a vehicle-mounted sensor measurement signal and a longitudinal lateral vehicle speed, determining the input and output of a state estimator based on a long short-term memory network, and constructing a state estimation loss; establishing a nonlinear prediction model for trajectory tracking control, and depicting a trajectory tracking control optimization problem under a rolling time domain framework; the control quantity output by the model prediction controller based on the long-short-term memory network is projected to a constraint space, the control quantity meeting constraints acts on the nonlinear prediction model oriented to trajectory tracking control, a state quantity in a prediction time domain is obtained, and optimization target loss is constructed; and constructing a joint loss function to carry out integrated training, deploying on a real vehicle platform after training is finished, and carrying out state estimation and motion control on the automatic driving vehicle. Compared with the prior art, the method has the advantages that estimation precision and trajectory tracking performance are both considered, and safety and reliability are high.

The invention discloses a thermal power generating unit deep peak regulation and combustion stabilization system and method coupled with fixed bed oxygen-enriched gasification, and relates to the technical field of thermal power generating unit intelligent control. The method comprises the following steps: S1, pre-constructing an AI model library containing a flame state recognition model and a unit load-gasification intensity linkage control model; s2, collecting an automatic power generation control load instruction, a multispectral flame image, unit operation parameters and gasification furnace operation parameter data in real time, and inputting a flame state recognition model to output a flame state and a stability index; s3, calling the linkage control model, and inputting real-time working condition data to calculate the calorific value compensation amount of the synthesis gas; s4, taking the flame stability index as feedback, correcting the feedforward control quantity through the model prediction controller, and dynamically adjusting the operation parameters of the gasification furnace; and S5, when an exit condition is met, an exit track is generated. Intelligent control over the thermal power generating unit is achieved, the operation cost is reduced, the flame monitoring precision and the control response speed are improved, and safe and economical operation of the unit is guaranteed.

This invention discloses a machine learning-based multi-objective collaborative control system for photovoltaic buildings. The invention relates to the field of building energy conservation and renewable energy utilization technology, and includes: a data acquisition module for real-time acquisition of outdoor environmental data, indoor environmental data, photovoltaic power generation data, and building energy consumption data; and a multi-objective predictive controller, whose input is connected to the data acquisition module, for processing the data input by the data acquisition module based on a machine learning model. This machine learning-based multi-objective collaborative control system for photovoltaic buildings can systematically balance multiple objectives such as power generation, building energy consumption, indoor light environment, and thermal comfort, thereby automatically generating and executing more reasonable and safer control strategies under dynamically changing environments and user needs. This helps to improve the building's energy utilization efficiency and renewable energy production capacity while ensuring the comfort of indoor occupants, achieving synergistic optimization of multi-dimensional performance.

The present application relates to high-altitude hoisting technical field, disclose a kind of hoisting operation control method, comprising the following steps: one, real-time acquisition the state information of tower material to be hoisted;Two, construct multi-source data fusion model to process the state information of tower material to be hoisted, and obtain the high-precision state information estimate of tower material to be hoisted;Three, based on the layout information of pedestal, hoist arm and sling, establish fixed coordinate system;Four, based on the elastic deformation of tower material to be hoisted and crane's sling, gravity and visual feedback information, establish the differential equation of sling shape;Five, predict the state trajectory of tower material to be hoisted in future time window, design model predictive controller, with the error between tower material desired pose and actual pose minimization as goal, solve the optimal control parameter of tractor;The present application is aimed at hoisting tower material state feedback demand, installs multiple sensors in hoist arm end, hook and other positions, constructs tower material closed-loop attitude control algorithm, realizes the accurate control of tower material attitude.

This application belongs to the field of precision machining technology, specifically disclosing a feed speed control method based on real-time parameter identification and model predictive control. The method includes: establishing a dynamic model of robot feed speed; updating the dynamic model of robot feed speed in real time based on the real-time feed speed during robot operation through an event-triggered mechanism to correct model parameters, resulting in a corrected parameterized model; constructing a model predictive controller, using the actual feed speed to track and schedule the feed speed as the objective, and constructing a multi-objective optimization function by combining the corrected parameterized model and preset feed speed constraints; solving the multi-objective optimization function to obtain the optimal control input sequence, and outputting a control signal based on the optimal control input sequence to achieve robot feed speed control. This application can reduce feed speed tracking errors, ensure speed stability during flat-tail skin machining, and improve machining efficiency while ensuring machining quality.

Disclosed are systems and methods for vehicle control. In one example, the system includes a memory with an instruction module that, when executed by a processor, directs the processor to manage vehicle operation using a control action sequence from a model predictive controller. This controller utilizes an enhanced predicted vehicle state derived from a predicted vehicle state and a residual generated by a last-layer Bayesian meta-learning vehicle model. The system enhances vehicle control by integrating advanced predictive modeling and adaptive learning techniques.

This document discloses a method and apparatus for product recommendation using an external computing device. A data capture apparatus may include an imaging component and a prediction controller connected to a host device. The data capture apparatus may be configured to: capture images of objects in one or more fields of view; provide the generated image data associated with the images to an object prediction application deployed on the prediction controller; identify one or more aspects of the objects via the object prediction application; generate object candidate data corresponding to the objects from the identification via the object prediction application; generate object identifier data for each object candidate in the object candidate data; and / or transmit the object candidate data and / or object identifier data to the host device via a scanner terminal of the host device.

The invention discloses a DC-NGPC reactor load tracking control method and system fused with disturbance compensation, and the system comprises a nonlinear disturbance observer which is used for calculating a nonlinear disturbance estimation value of a reactor system according to the speed of a control rod and state feedback information outputted by the reactor system; the nonlinear generalized predictive controller based on disturbance compensation is used for providing a predictive control law of reactor load tracking control, and the predictive control law is used for calculating the speed of a control rod according to a power track provided by the outside, state feedback information output by a reactor system and a nonlinear disturbance estimated value calculated by the nonlinear disturbance observer; wherein the control rod speed is provided for the reactor system after being subjected to rod speed amplitude limiting, so as to track and control the load of the reactor system. The disturbance observed quantity obtained through estimation is introduced into the nonlinear generalized predictive controller, dynamic compensation of disturbance is effectively achieved, and the robustness of the whole system is improved.

The invention relates to the technical field of intelligent control, in particular to a cut-tobacco dryer steady-state model predictive control method based on Kalman filtering time delay correction, which comprises the following steps of: utilizing second-level historical production data and a system identification technology to process a cut-tobacco drying steady-state production process; according to the method, a dynamic transfer function prediction model and a state-space equation model from the moisture removal air door opening degree and the cylinder wall temperature to the outlet moisture are established, an appropriate Kalman filter (KF) is selected to carry out online correction on a cut-tobacco dryer prediction model with time delay, and the control of the process parameters in the cut-tobacco drying steady-state production stage is realized by using a model predictive controller (MPC) algorithm. Therefore, ideal outlet material moisture is achieved, and the problems that in the cut-tobacco drying process of an existing cut-tobacco dryer, dynamic characteristics of a system are ignored in a control method applied to the cut-tobacco drying process of the cut-tobacco dryer, and the control effect of the cut-tobacco drying process of the cut-tobacco dryer is poor due to the fact that the system with large inertia for controlling the cut-tobacco drying process of the cut-tobacco dryer is poor in prediction effect are solved.

The invention provides a state space model prediction optimization control system and method in a water-light complementary system, and relates to the field of power system automatic power generation control and renewable energy source grid connection. For uncertainty caused by photovoltaic output and load fluctuation in the water-light complementary system, a prediction controller based on a state space model is constructed. The control method comprises the following steps: establishing a nonlinear state space model of the water-light complementary system, and performing linearization processing at a steady-state point to obtain a discrete state space prediction model; on the basis of future dynamics of the model prediction system, designing a cost function including prediction error compensation, and adopting a quadratic programming method to solve an optimal control sequence in an online rolling manner; finally, the first component of the control sequence acts on the hydroelectric generating set speed regulation system, and frequency stability and power balance are achieved. Through model prediction, rolling optimization and feedback correction mechanisms, the adaptive capacity of the system to photovoltaic fluctuation and load disturbance is remarkably improved, and the robustness and dynamic response performance of the system are enhanced.

This system provides an autonomous mobile vehicle movement prediction system that allows users to intuitively check the predicted movement range of an autonomous mobile vehicle. [Solution] The movement prediction system 10 comprises a guide robot 20 and a prediction controller 30 that predicts the movement range of the guide robot 20. The prediction controller 30 has a digital map M that represents the current position R of the guide robot 20 and the predicted movement range P of the guide robot 20. The predicted movement range P includes the error range P1 of the position information of the guide robot 20 and the possible range P2 of the guide robot 20 after a predetermined time has elapsed.

The application discloses a construction method of a flat wire permanent magnet wheel hub motor backstepping model predictive controller, first derives an angular velocity deviation, constructs a corresponding Lyapunov function one, calculates q-axis current, obtains a q-axis current estimation value according to a calculation formula of the q-axis current and a torque estimation error, and constructs a backstepping controller with the angular velocity deviation input and the q-axis current estimation value output; then constructs a corresponding Lyapunov function two according to the angular velocity deviation and a current tracking error, solves acceleration, constructs an acceleration control module with the angular velocity deviation and the current tracking error input and the acceleration output, and finally evaluates each d-axis current prediction value, q-axis current prediction value and acceleration prediction value through a value function formula, selects a voltage vector corresponding to a prediction value that makes the value function minimum; the method realizes rapid and accurate acquisition of motor given current under complex working conditions, improves the response capability of a hub driving system, and is favorable to improvement of collaborative control performance of a distributed driving system.

The application discloses a pre-cooling concrete production Internet of Things temperature control system and method, which comprises a region demarcation module, a temperature control module, an environment data acquisition module, a data analysis and reasoning module, a device control module and a visual display module, and the modules cooperate with each other, realizes accurate temperature control of different pre-cooling concrete production regions, and the system demarcates different temperature control regions by using laser radar point cloud and hyperspectral image imaging technology, and fiber grating temperature sensing network array, model prediction MPC controller and magnetic suspension variable frequency refrigeration components ensure accurate temperature control, and meanwhile, the data analysis and reasoning module combines a deep learning time sequence prediction model and a reasoning rule mining algorithm to provide support for temperature control of production equipment; the overall temperature control scheme improves the intelligentization and accuracy of the pre-cooling concrete production Internet of Things temperature control, and is suitable for various pre-cooling concrete production scenes.

A control system including a power grid configured to supply a grid voltage, a filter capacitor and a filter inductor each connected in parallel to the power grid, a filter current meter connected in series to the filter inductor, an active power filter connected to the filter current meter, a nonlinear load connected in series to the power grid, and a modulated model predictive controller (MMPC) configured to generate a control signal for operating the active power filter.

The application discloses a stable domain constraint trajectory planning and control method for a modular distributed electric drive heavy load vehicle, comprising the following steps: constructing a nonlinear dynamics model of the modular distributed electric drive heavy load vehicle, fitting a tire lateral force model through a rational polynomial, and establishing a vehicle system state space expression based on the rational polynomial form; adopting a sum of squares planning method to estimate the stable domain of the vehicle under different vehicle speeds, road adhesion coefficients and front, middle and rear wheel turning angles, and analyzing the influence of each state quantity on the shape and boundary of the stable domain; embedding the stable domain constraint into a trajectory planning layer based on a quintic polynomial to generate an obstacle avoidance trajectory meeting the dynamics stability; designing a multi-axle proportional steering strategy based on a steady state assumption, and combining a nonlinear model predictive controller to realize trajectory tracking control, so that the stability and control feasibility of the vehicle in a high-speed dynamic obstacle avoidance process are ensured. Through construction of a high-speed dynamic obstacle avoidance simulation scene, the effectiveness, stability and control feasibility of the stable domain constraint trajectory planning and control framework are verified.