38 results about "Multi model predictive control" patented technology

The invention discloses an indoor thermal environment control method based on model reduction and multi-model prediction control. The method includes the three main steps that firstly, a temperature variation section of an indoor user region is segmented, modeling is conducted on the equilibrium state position of each segment through computional fluid dynamics (CFD) software and the proper orthogonal decomposition (POD) technology, and a low-order linear building thermal environment in a multi-model type is reconstructed; secondly, multi-model switching time is reasonably selected through an optimization algorithm; thirdly, accurate control over the user region temperature is conducted on each sub model through a prediction control method. Low-order models of the building indoor thermal environment are constructed through the POD technology and the multi-model method, the influence of space distribution on the temperature variation can be fully taken into consideration in a temperature regulation strategy, and accuracy of indoor temperature control is improved. Compared with an existing temperature control strategy adopted in large space, the indoor thermal environment control method has the advantages that accuracy is high, and energy saving of a building is facilitated.

The invention discloses an MMPC (Multiple Model Predictive Control)-based supercritical unit coordination and control method which comprises the following steps: pre-arranging a plurality of local predictive models according to the nonlinear working condition response of a supercritical unit; selecting a load parameter to serve as a control variable through a model identification method, and obtaining the output increments of all local predictive models through an interpolation formula; and in each control period, piling up the output increments of all local predictive models through a DMC (Dynamic Matrix Control) prediction control algorithm so as to obtain an actual output increment which is used for correcting a feedforward passage. According to the invention, the MMPC method is adopted for the coordination and control of the supercritical unit; a selection method of the control variable, a process variable and a perturbation variable for prediction control is proposed; and a specific construction scheme of the MMPC in a coordination and control system is also provided. The method has the advantages of advancement, strong practicability, good robustness, and quick and stable control effect, as a result, an ideal control effect is obtained.

The invention discloses a subcritical coordinative control method based on multiple model predictive control. The method comprises the following steps: determining a plurality of preset local prediction models according to a non-linear work condition range, as well as dynamic responses of main steam pressure and power of a subcriticla machine set; designing a controller for each local prediction model, selecting a control variable, and obtaining an output increment of each local prediction model through an interpolation formula; and during each control period, obtaining an actual control output increment according to an output increment of each controller in a weighing manner; and correcting a feed-forward channel. By adopting the method disclosed by the invention, control output during a transition period can be smoothed, as a result, load and pressure response characteristics of the machine set can be improved effectively; and the subcritical coordinative control method based on multiple model predictive control can adapt to large-scale change in work conditions.

The present invention discloses a multi-model prediction control method of a carbon dioxide after combustion capture system. The prediction control method takes the carbon dioxide after combustion capture system based on the chemisorptions as a controlled object, takes the barren liquor valve opening and the turbine low pressure cylinder steam extraction valve opening as the system control input, takes the carbon dioxide capture rate and the reboiler temperature as the system output, and comprises the steps of firstly, based on a sub-space identification method, utilizing the data generated by the system operation to establish a local state space model of the system at different working condition points; then using a gap measurement method to research the nonlinear distribution of the controlled object; and further establishing a prediction controller at an appropriate local working condition point, designing a degree of membership function to weight and combine, and establishing a multi-model prediction control system of the carbon dioxide after combustion capture system. The method of the present invention has a good global nonlinear control capability, can adapt to the large-range variable working condition demands of the system effectively, rapidly tracks a carbon dioxide capture rate setting value, and enables the deep, rapid and flexible operation level of the carbon dioxide capture system to be improved.

The invention relates to a multi-model predictive control method for component content in the process of extracting rare earth. The method comprises the following steps of: establishing a multi-linear model with concentration extraction series of 1 at the component content detection point in the process of extracting and separating the rare earth; converting the established multi-linear model into a controlled autoregressive integral moving average (CARIMA) model by a generalized predictive control algorithm; and calculating to obtain the change of controlled quantity (flow quantity of an extracting agent or acid liquid) according to the deviation between a component content on-line estimated value and a preset value. The method is suitable for on-line detection and automatic control of the component content in the process of extracting and separating the rare earth.

The invention discloses an SCR denitrification control method based on variable-constraint multi-model prediction control. The method is implemented by combining a multi-model prediction control algorithm, an intelligent model weighing algorithm and variable constraint conditions. The method comprises the following steps that 1, a precise NOx prediction model is obtained through the intelligent model weighing algorithm; 2, the optimal real-time ammonia demand is calculated by beans of continuous rolling optimization of the multi-model prediction control algorithm and the constraint conditions changing by following the working conditions. According to the method, the conflict generated between the model precision and the control algorithm when an existing prediction control strategy is applied in an SCR denitrification system is solved; compared with a traditional simple multi-linear-model linear weighing method, the model precision of the algorithm is improved; traditional immobile constraint conditions are changed into the variable constraint conditions changing by following the working conditions, constraint adjustment is more accurate, therefore, the computing burden of a resolution control instruction is reduced, a project is easy to implement, the large lag characteristic of the SCR system is overcome, and the NOx concentration regulation quality is significantly improved.

The invention provides a multi-model prediction control method and system for a draught fan, a storage and a controller. The multi-model prediction control method for the draught fan comprises the steps that linearized models of the draught fan at all wind speed points are acquired, and dynamic differences among the linearized models of the wind speed points are acquired according to the clearance metric; wind speed ranges are divided according to the dynamic differences, and linearized model sets, formed by the linearized models of the corresponding wind speeds, of the wind speed ranges are built; optimal control input of a prediction controller, for draught fan control, of each wind speed range is acquired according to the linearized model sets of the wind speed ranges; the corresponding prediction controller is called according to switching laws of the linearized models to acquire the real-time optimal control input. Accordingly, overall working condition control over a draught fan system can be achieved, especially the complete low-redundancy linearized model sets are built according to the clearance metric, therefore, the multi-model prediction control effect of the draught fan system is improved, and the method is suitable for overall working condition control over an independent or distributed or parallel wind generation set.

The invention discloses a multi-model predictive control design method for a supercritical thermal power generating unit coordination control system based on a spatial metric algorithm, is used for controlling a supercritical coal-fired unit machine-furnace coordination system, and belongs to the field of thermal power engineering and automatic control. At present, machine-furnace coordination control systems of most power plants are designed based on conventional PID; during large-range variable-working-condition operation, performance of the coordinated control system is degraded due to thecharacteristics of large hysteresis, nonlinearity and time variation of a machine-furnace coordinated controlled object, so that the problems of low load regulation rate, poor precision, large main steam pressure and steam temperature fluctuation and the like of a unit in the operation process are caused. According to the invention, the traditional predictive control is properly simplified to realize the simplified design of linear predictive control; secondly, specific sub-model selection is realized in combination with a space measurement algorithm; and finally, a weighted soft switching strategy with simple calculation is provided for the integration of a plurality of sub-controllers, so that load regulation performance of a 660MW unit and operation stability of the unit can be remarkably improved, and the engineering requirements are met.

The invention discloses a nonlinear suppression method for solid oxide fuel cells based on multi-model predictive control. The nonlinear multi-variable coupling problem of solid oxide fuel cells is solved by online multi-model weighted predictive control. A feedforward method is adopted to compensate for the disturbance of a resistive load to achieve disturbance restraint. At the same time, constraints on hydrogen flow the air flow are added to performance indexes, and the feasibility of a control system is achieved through a feedback correction link based on a Kalman filter, and the control effect is improved. It is ensured that the fuel cells maintain a constant output voltage after external resistance changes, and the fuel utilization rate is within a reasonable range of 0.7 to 0.9.

The invention relates to the technical field of doubly fed variable speed pumped storage unit power control, in particular to a multi-model predictive control method of the doubly fed variable speed pumped storage unit. The multi-model predictive control method comprises the following steps of using a linearized system model to carry out prediction, feedback correction and rolling optimization toobtain a reference power command of DFIM-VSPS, wherein the output of a control object is the voltage and frequency of a PCC point, and a controlled system is a power system including the doubly fed variable speed pumped storage unit and wind power; and selecting wind speed as measurable disturbance, load fluctuation and fault as unmeasurable disturbance, and switching between multiple model predictive controllers according to measured wind speed, wherein the steps are as follows: 1, a modeling of the doubly fed variable speed pumped storage unit, specifically, the modeling comprises a water diversion system, a water pump turbine, a doubly fed asynchronous motor, a control system and a power grid; and power control of the doubly fed variable speed pumped storage unit based on MMPC. Multi-model predictive control has better control performance and robustness in case of large disturbance and stabilizing wind power fluctuation.

The invention discloses a genetic algorithm optimized and weighted multi-model predication function control method. The method comprises the following steps: firstly, expanding an integer-order multi-model predication control method to a fractional-order multi-model predication control method; converting a nonlinear model into a linear partial model through an established partial state space model of a controlled object; introducing a genetic algorithm into a selection of a partial model set and kernel bandwidth of a working section. Therefore, the range of a working region of the partial model of a system is enlarged, the quantity of the models which work at present is reduced, the controlled quantity occupied rate on the system of the main partial model in the system is increased, the control precision of the system is increased and the working efficiency of the device is more efficient.

The invention discloses an extended state observer-integrated supercritical thermal power unit turbine-boiler coordinated control method. According to the method, firstly, a plurality of local linear models are established at different working condition points of a power unit through the nonlinear analysis method according to the nonlinear model of a 1000 MW supercritical thermal power unit. Secondly, based on the plurality of local linear models, an extended state observer-integrated multiple model predictive controller (MMPC) is designed. Through the introduction of an extended state observer, the disturbance of the power unit in different channels and the model uncertainty are estimated and compensated. Meanwhile, the disturbance rejection performance of the predictive controller is improved. According to the technical scheme of the invention, based on the above algorithm, the large-scale tracking function for the load of the power unit is realized on the premise that the global stability of the control system is ensured. Meanwhile, the good disturbance rejection performance is realized.

The invention relates to a multi-model predictive control method for the variable working condition sewage treatment process, which is characterized in that an optimal control model is selected from multiple control models online according to a switching index at each sampling moment, a future response of a system is predicted according to historical information and future input of the selected optimal control model, an optimization problem in a finite time domain is solved by taking the ammonia-nitrogen concentration and the nitrate-nitrogen concentration as a control target so as to acquire a control input sequence based on the current moment, control input which firstly acts on the selected optimal control model is selected from the control input sequence, the optimal control model is reselected at the next sampling moment, optimization problem solving is carried out, and closed-loop optimization control is formed.

The invention discloses a multi-model intelligent optimizing prediction control method for boiler loads under the low-load condition. The method specifically comprises the steps that first, load points are selected to establish a controlled object model; second, a prediction controller is designed at all the load points according to the controlled object model, and thus the optimum control increment of the prediction controller is obtained; third, if the optimum control increment in the second step meets the constraint conditions, no treatment is required; and if not, a particle swarm algorithm needs to be adopted to search for the optimum control increment; and fourth, multi-model prediction control is adopted, and an improved recursive Bayesian weighting algorithm is used for weighing the output of all sub controllers according to deviation of the output of all sub models and the actual output. Through the self-adaptation intelligent optimizing control technology, stable and safe operation of a unit under the low-load coal supply economical blending combustion condition is achieved.

In order to solve the problem which cannot be solved by traditional MPC complex automatic driving control under multiple working conditions, the invention provides novel multi-model predictive control(Multi-model predictive content, MMPC) algorithm, and the algorithm is applied to a man-machine cooperative control system of an intelligent automobile. The method comprises steps of adopting Gospson-Kessel algorithm to perform clustering analysis on the real vehicle test data to obtain a clustering center and training sample data of each typical steering working condition; utilizing LS-SVM to build a multi-model structure, and enabling the built sub-models of all types to serve as prediction models of the MPC algorithm; constructing an objective function of multi-objective optimization, andadopting NSGA-II algorithm to solve a multi-objective optimization problem, and obtaining an optimal control quantity, so as to solve a problem that a conventional MPC cannot adapt to the automatic driving control under the conditions of strong parameter time-varying characteristics and complex multiple working conditions.

The invention relates to an accurate ammonia spraying control system and method for a denitration device based on accurate concentration prediction. The accurate ammonia spraying control system comprises a power station information system, a denitration device inlet NOx concentration prediction model, a multi-model prediction control module and a denitration device control object. Accurate feedforward is provided for control over the ammonia injection amount of the denitration device by predicting the NOx concentration of an inlet of the denitration device in advance, meanwhile, a multi-model prediction control module under the variable load working condition is established, accurate control over the ammonia injection amount of the denitration device is achieved, and defects of large delay, large inertia and strong nonlinearity of a denitration system are overcome; under the large-range variable load working condition, economical efficiency and stability of ammonia spraying amount control of the denitration device are greatly improved under the condition that it is guaranteed that the NOx concentration of an outlet reaches the standard.

The invention discloses a multi-model prediction control method based on a gap metric weighting function. A systematic and effective multi-model weighting algorithm is provided through gap metric; submodel prediction controllers are synthesized to form a multi-model prediction controller; and optimization control is performed on a nonlinear system. Compared with the traditional weighting function,such as a trapezoidal weighting function, the weighting function only has a single setting parameter; the workload of parameter setting is greatly reduced; a weight number can be calculated off lineand is stored in a query table for future use; the on-line calculation load is greatly reduced; the algorithm reserves the advantages of the traditional weighting algorithm, overcomes the disadvantages of the traditional weighting algorithm and is an effective multi-model weighting algorithm; and the algorithm has the great benefits for improving performance of the multi-model prediction controller.

The invention discloses ammonia spraying apparatus based on a sound wave measuring temperature field and a flow field. The ammonia spraying apparatus comprises the sound wave measuring temperature field, a flow field component, an intelligent feed-forward controller, a first comparator, a second comparator, a third comparator, a proportional integral (PI) regulator, an ammonia spraying main valve,a selective catalytic reduction (SCR) reactor, an unpredictable disturbance observer, an NOx concentration prediction controller and a multi-model predictive controller; the sound wave measuring temperature field and the flow field assembly comprise an acoustic wave sensor, a data acquisition device and an industrial control computer. According to the apparatus provided by the invention, an acoustic wave method is adopted to measure the smoke temperature field and the flow field, the intelligent feed-forward controller is designed according to the change of the NOx concentration at an inlet,the NOx concentration at a desulfurization outlet and the flow field of flue gas, and parameters of the feed-forward controller are dynamically modified so as to realize accurate compensation of totalworking conditions for measurable disturbance, so that ammonia spraying control is more timely and accurate.

Aiming at safety problems caused by two common faults such as a constant deviation fault of an actuator of a wind power generation system, the invention provides a fault fan control method based on robust self-adaption control and sliding mode variable structure control. Firstly, multi-model predictive control is adopted as a fault tolerance strategy of the wind power generation system, and when the wind power generation system breaks down, a corresponding control strategy is switched according to the fault type, so that the purpose of fault tolerance is achieved. On the basis, it is considered that a control law needs to have universality instead of specific model structures or specific parameters, fault fan model errors and uncertainty of parameters of a fault fan are considered, and the robustness and the self-adaption degree of fault-tolerant control are improved through robust self-adaption control and sliding mode variable structure control. According to the method, the corresponding control strategy can be effectively switched according to the fault type, and the purpose of stable fault tolerance is achieved.

The invention discloses a reheat steam temperature adjusting method based on hierarchical scheduling multi-model predictive control. Transfer function models of influences of a plurality of flue gas baffles on last-stage reheat steam temperature are built in low, medium and high load sections of a thermal power generating unit, prediction controllers are designed according to the sub-models, the control quantity calculated by each controller is weighted to act on a system, different model sets are switched according to different operation conditions of the unit, and the control precision and the algorithm operation efficiency are both considered. According to the method, the automation degree of the flue gas baffles can be improved, and the workload of operators is reduced. Meanwhile, whenthe load is changed in a large range, the use of reheat desuperheating water is reduced, and the control precision of the steam temperature of an outlet of a last-stage reheater can be improved.

The invention provides a fan active power tracking method based on a dynamic multi-model prediction controller. The method comprises the steps that nonlinear models of a fan are built and subjected to linearization and discretization at working condition points, and a linear multi-working-condition multi-model of the fan is obtained; a model prediction controller is built according to the linear multi-working-condition multi-model of the fan; parameters of models in the model prediction controller are updated and corrected according to the actual running state of the fan, and a dynamic multi-model prediction controller is built; and the effectiveness of the dynamic multi-model prediction controller in the aspect of fan active power tracking is verified based on an actual experiment platform or high-fidelity simulation software. The defects of a traditional fan active power tracking strategy are overcome, fluctuation during fan output power tracking is restrained on the basis of the dynamic multi-model prediction controller, and the economic performance of the fan is improved.

The invention discloses an extended state observer-integrated supercritical thermal power unit turbine-boiler coordinated control method. According to the method, firstly, a plurality of local linear models are established at different working condition points of a power unit through the nonlinear analysis method according to the nonlinear model of a 1000 MW supercritical thermal power unit. Secondly, based on the plurality of local linear models, an extended state observer-integrated multiple model predictive controller (MMPC) is designed. Through the introduction of an extended state observer, the disturbance of the power unit in different channels and the model uncertainty are estimated and compensated. Meanwhile, the disturbance rejection performance of the predictive controller is improved. According to the technical scheme of the invention, based on the above algorithm, the large-scale tracking function for the load of the power unit is realized on the premise that the global stability of the control system is ensured. Meanwhile, the good disturbance rejection performance is realized.

The invention discloses a thermal power generating unit main steam pressure control method and system based on three-stage control series connection, and the method comprises the steps: designing a three-stage controller series connection structure, wherein the three-stage controller series connection structure comprises a multi-model prediction controller, a boiler master controller and a fuel master controller which are sequentially connected in series; setting parameters of boiler master control and fuel master control in the three-stage controller series connection structure; and designing model prediction controllers under multiple working conditions, summing the outputs of the model prediction controllers under the multiple working conditions and then performing accumulating, and taking the accumulated output as the overall output of the multi-model prediction controller. According to the method and system, the main steam pressure control response speed can be improved, the main steam pressure dynamic deviation can be reduced, the automatic power generation control stability can be improved, the fuel quantity can be rapidly adjusted during load changing, and therefore the main steam pressure response speed is increased, and the dynamic deviation is reduced.

The invention discloses a multi-model predictive control design method for a coordinated control system of a supercritical thermal power unit based on a space metric algorithm, which is used for the control of the coordinated control system of a supercritical coal-fired unit and belongs to the field of thermal power engineering and automatic control. At present, the coordinated control system of machines and furnaces in most power plants is based on the conventional PID design. When operating in a wide range of variable operating conditions, the characteristics of large hysteresis, nonlinearity, and time-varying characteristics of the controlled objects in the machine-boiler coordination degrade the performance of the coordinated control system, resulting in the unit running During the process, problems such as low load regulation rate, poor precision, and large fluctuations in main steam pressure and steam temperature occurred. The present invention appropriately simplifies the traditional predictive control to realize the simplified design of linear predictive control, secondly combines the space metric algorithm to realize the selection of specific sub-models, and finally proposes a weighted soft switching strategy with simple calculation for the synthesis of multiple sub-controllers, which can significantly Improve the load regulation performance and unit operation stability of the 660MW unit to meet the needs of the project.

The invention discloses a multivariable nonlinear system adaptive equalization multi-model decomposition and control method. Aiming at the complexity of a non-linear multivariable system, the method comprises the following steps: firstly, gridding the multivariable non-linear system by utilizing a gridding algorithm based on gap measurement; secondly, decomposing the multivariable nonlinear systemby utilizing a multi-model decomposition algorithm based on clearance measurement; and based on each obtained sub-model, designing a prediction controller by using a prediction control algorithm, andfinally performing weighted synthesis by using a trapezoidal weighting mode to perform global control on the system so as to obtain the adaptive equalization multi-model decomposition and multi-modelprediction controller of the multivariable nonlinear system. According to the invention, the steps of decomposing and controlling the system are greatly simplified, and the efficiency is improved.

The invention discloses a 660MW thermal power generating unit multi-model prediction control method based on proximity principle weighting. The method comprises the following steps: firstly, analyzing dynamic characteristics of a controlled object of a 660MW thermal power generating unit, and determining an input quantity, namely a control quantity, and an output quantity, namely a controlled quantity, of a 660MW thermal power generating unit coordination system as a basis for research and development of a control strategy; establishing a local transfer function model of the unit under different load conditions; aiming at the local transfer function models under different load working conditions, designing corresponding local model prediction controllers; and based on the principle of proximity, performing weighting processing on the action of each local model prediction controller. The performance of the 660MW thermal power generating unit coordination control system can be improved, and safe, stable and economical operation of the unit is guaranteed.

A multi-model generalized predictive control system based on dynamic optimization comprises a dynamic optimization layer, an MPC layer and a basic control layer. The dynamic optimization layer is located in the upper layer and calculates the optimization value of a critical control variable as the optimal set value of the MPC layer; the MPC layer is located in the lower layer and adjusts a to-be-optimized variable based on a rolling optimization prediction algorithm on the condition that the to-be-optimized variable satisfies a model dynamic behavior, thereby tracking the optimal set value obtained in the S1; the basic control layer is located in the bottom layer and transmits the final optimization value of the to-be-optimized variable to an actuating mechanism. According to the present invention, the cost consumption of the system is reduced, the economic benefit of the system is improved, the transient performance and the model parameter jump adjusting capability of the system can be improved, at the same time, the interference of disturbance on the output of the system can be eliminated effectively.