11 results about "Multivariable predictive control" patented technology

The invention discloses a multivariable predictive control energy consumption adjustment method and system, and belongs to the technical field of automatic control, and the method comprises the steps: collecting a parameter adjustment log in real time through an edge node, carrying out the intervention behavior recognition, and verifying the validity, so as to detect a manual parameter adjustment event; once an artificial parameter adjustment event is detected, multivariable data before and after intervention are extracted, and parameters of the local prediction model are dynamically corrected; performing rehearsal intervention based on the manual intervention parameters, generating space-time coupling constraints, and constructing a hybrid neural network to generate an energy consumption prediction trajectory; dividing types according to operator behavior modes, fusing prediction data to generate a comprehensive state vector, adjusting a reward function, focusing a sensitive variable, and generating a control instruction; and acquiring an actual energy consumption value in real time, comparing the actual energy consumption value with an energy consumption prediction track, calculating an energy consumption deviation, performing secondary optimization, positioning an error root cause through multi-scale decomposition in combination with a semantic tag and a knowledge graph, and performing layered compensation.

The invention relates to the technical field of vehicles, and discloses an engine intelligent layered thermal management system and method based on multivariable predictive control, and the system comprises a thermal management execution module, an information sensing module and an intelligent control module; the information sensing module is used for collecting real-time information and sending the real-time information to the intelligent control module; the information sensing module comprises a plurality of sensors and a communication interface, the plurality of sensors at least cover a temperature sensor, a pressure sensor and a flow sensor, and the communication interface is used for obtaining engine torque, rotating speed, fuel injection amount, boost pressure and vehicle speed information; the intelligent control module receives the real-time information sent by the information sensing module, performs thermal load prediction and multi-target optimization calculation to obtain an optimal control sequence, and sends the optimal control sequence to the thermal management execution module; and the thermal management execution module receives the optimal control sequence and calls an execution element to execute a thermal management action.

The present application relates to a kind of integrated energy system double-layer optimization operation method based on multivariable predictive control, comprising: the latest operation data of sampling system in dynamic scheduling layer, based on the dynamic characteristic model of equipment that can be used to describe the heating link of system equipment, according to the latest renewable energy and load demand forecast data within day, with the minimum economic cost and the minimum of dynamic supply-demand deviation index as optimization goal, with power balance constraint and equipment characteristic constraint as constraint, carry out within day rolling scheduling, provide the power scheduling instruction of each equipment in the next scheduling period;Real-time control layer takes the heat-electricity coupling link of gas internal combustion engine and its waste heat recovery equipment and heat pump as control object, based on multivariable predictive control method, the power scheduling instruction issued by dynamic scheduling layer is corrected in real time, using the power scheduling instruction after correction acts on system, can reduce the disturbance influence brought by prediction deviation, improves system energy supply accuracy and operating stability.

The present invention pertains to the field of power grid balance control, and particularly relates to a control method and system for a double reheat unit to quickly respond to power grid demands. The method comprises: acquiring boiler-side and turbine-side operation data via integrated sensors, performing preprocessing and then constructing and training a multivariable predictive control model, and acquiring boiler-side initial control parameters; second, by introducing a dynamic compensation strategy, acquiring boiler-side real-time heat flow distribution, and optimizing the boiler-side initial control parameters to ensure the accuracy of boiler-side control; third, acquiring turbine-side initial control parameters by using a turbine-side peak shaving strategy and a particle swarm algorithm; and fourth, constructing a wind and photovoltaic power output prediction model, and dynamically adjusting the output of the double reheat unit on the basis of the prediction result, and when the required output exceeds a threshold, applying pre-calculated boiler-side and turbine-side control parameters to control the unit to perform power generation, so as to ensure a quick response to power grid demands. The present invention effectively improves the response speed and operational efficiency of the double reheat unit, satisfying the requirements of rapid power grid changes.

The invention provides an adaptive cycle engine multivariable adaptive prediction control method based on data driving, and belongs to the technical field of aero-engines, and the method comprises the steps: building an adaptive cycle engine equivalent data model based on full-format dynamic linearization based on the input and output data of an adaptive cycle engine; obtaining a multi-step forward output prediction equation; performing real-time estimation on a pseudo gradient matrix in the prediction equation based on an autoregression and projection algorithm to form a prediction control rolling optimization framework; embedding a model-free adaptive control algorithm based on single-step optimization into the framework to form a composite control strategy, and designing a control input performance index taking tracking error minimization as a target to obtain an optimal control parameter; and on the basis of a compound control strategy, real-time optimal control parameters are generated by utilizing real-time input and output data of an engine, and multi-variable accurate cooperative control is realized. According to the invention, the long-time-domain prediction, constraint processing and global optimization capabilities of the control system are improved.

The invention discloses a multi-target cooperative temperature control system and method for a closed test container, and belongs to the technical field of precise temperature control. The system comprises a closed container, a plurality of temperature control units arranged in the container, a temperature sensing network used for detecting the temperature of each temperature control unit and the environment temperature, a central processing unit and a cooperative control module. And the cooperative control module dynamically coordinates the working states of the global intracavity temperature control unit and the local fine-tuning temperature control units and performs cooperative control based on a multivariable prediction control algorithm according to the set temperature and the actually measured temperature of each temperature control unit, so as to decouple the thermal coupling effect between the temperature control units, and control the temperature control units. Therefore, the temperature in the cavity of the closed test container can reach a dynamic balance. The device effectively solves the problem of mutual interference during multi-target independent temperature control in a closed test container, realizes high-precision and high-stability cooperative temperature control, and is particularly suitable for parallel experiments and tests in the fields of biological pharmacy, material science and the like.

The invention discloses a multivariable predictive control method and a related device, belongs to the field of advanced process control, and is applied to a multivariable predictive control system comprising a simulation controller, a real controller, a hybrid predictive model and a real control object. A simulation control input variable is determined by a prediction output variable of a current control period of a real controller determined by a previous prediction period and a current real control parameter of the real controller, and a prediction sub-model matched with the simulation control input variable in a hybrid prediction model is used for prediction. And obtaining a predicted output variable of the next control period of the real controller. The prediction result of the previous prediction period of the hybrid prediction model and the real control parameters of the real controller jointly participate in prediction of the prediction output variable of the next control period, and compared with a mode of performing variable prediction only by analyzing historical data in the prior art, the prediction precision is improved.

The invention relates to the technical field of chemical production control, and discloses a digital twinning-based electrolytic chlorine production full-flow intelligent regulation and control system, which comprises a data processing module, a digital twinning module, a digital twinning module and a control module, the digital twinning module is used for constructing a digital twinning model and calculating to obtain the current efficiency, the actual product concentration, the predicted value of the outlet temperature of the electrolytic cell, the electrode state and the salinity; the state estimation module is used for constructing and updating a state vector; the prediction control module is used for constructing a target function and solving the target function to obtain a control instruction; the safety monitoring module is used for executing safety protection measures; by constructing the digital twinborn model and combining a multivariable predictive control algorithm, accurate and stable control over the sodium hypochlorite yield and concentration under the fluctuation condition of multiple parameters such as seawater salinity, temperature and pH is achieved, the control precision is improved, the fluctuation range of the product concentration is narrowed, and the problem that the product quality is affected by seawater quality changes is effectively solved.

The invention relates to the technical field of textile, and discloses a stitch self-adaptive control method of a multifunctional computer sewing machine, which comprises the following steps of: acquiring fabric drawing force, shearing force, vertical pressure and local elastic modulus in real time through a multi-axial force sensor and a flexible strain sensor, and generating a fabric mechanical distribution diagram; needle penetration resistance, stitch friction force and needle hole deformation are monitored through an optical sensor, and a stitch path and control parameters are called or initialized; generating a global stitch topological structure through topological optimization based on the state vector and the sewing target; generating an optimized trace path through geometric optimization in combination with the topological structure and the initial path; real-time control parameters such as needle speed, thread tension and presser foot pressure are generated through multivariable predictive control; and executing stitch formation and updating the state and the memory bank in real time. According to the method, comprehensive perception and closed-loop control of dynamic mechanics and needle and thread interaction of complex fabrics are achieved, and the sewing requirements of multi-layer composite, elastic or non-uniform fabrics are met.

The invention belongs to the technical field of industrial gas storage, transportation and distribution, and discloses a gas holder pressure self-balancing system which comprises a measuring unit, an executing unit, a multivariable predictive control unit and a man-machine interaction unit, and the multivariable predictive control unit comprises a balance predictive model module and a collaborative optimization module. The balance prediction model module creatively adopts a graph neural network to map gas holders, pipe network nodes and a connection relation thereof into graph data, overcomes the defects that an existing gas holder pressure control method neglects network coupling, is complex in centralized calculation and lacks topology self-adaption and toughness guarantee, accurately models a gas holder group topology and a coupling relation through a graph structure, and improves the control accuracy of the gas holder group. A distributed optimization algorithm is utilized to realize efficient cooperative control, and a safety toughness evaluation mechanism is built in, so that rapid, accurate and robust balance of pressure of multiple gas holders is finally realized.

This application relates to an energy consumption optimization and regulation method based on multivariate predictive control, comprising the following steps: S1, Four-element coupled state-space modeling: establishing a four-element coupled discrete-time state-space model of an industrial integrated energy system, comprising electricity, heat, cooling, and energy storage; S2, Adaptive weighted objective function construction: constructing a multi-objective optimization objective function containing peak-valley electricity price weighting factors, wherein the objective function includes an output tracking error term, a control increment smoothing term, an energy consumption cost term, and a constraint violation penalty term; S3, Composite disturbance correction; S4, Hierarchical lightweight solution. The energy consumption optimization and regulation method and dynamic response system based on multivariate predictive control proposed in this invention address the core pain points in industrial energy consumption control, such as strong multivariate coupling, weak dynamic disturbance robustness, and lag in peak-valley electricity price response, and construct a technical solution covering the entire process of modeling-optimization-correction-solution.