16 results about "Multi model predictive control" patented technology

The invention belongs to the field of industrial automation and mechanical control, and discloses a heavy-load mechanical arm trajectory tracking and control method and system under a dynamic working condition, and the method comprises the steps of kinetic parameter identification, multi-model predictive control and fault-tolerant control, real-time collection of motion state data of a mechanical arm, and real-time control of the mechanical arm. A recursive least square method and a particle swarm optimization algorithm are combined, and dynamic parameters of the mechanical arm are updated and optimized in real time; a dynamic prediction model set under different dynamic load working conditions and motion states is constructed, an optimal model is dynamically selected, and a mechanical arm trajectory tracking strategy is adjusted through a model prediction control algorithm; the motion state of the mechanical arm is monitored in real time, fault detection and diagnosis are synchronously carried out, redundancy control is started once a fault is detected, the control strategy is dynamically adjusted in combination with the redundancy freedom degree of the mechanical arm and the distributed control architecture, normal operation of the system is maintained, the robustness and reliability of the system are improved, and the requirements of complex operation scenes are met.

The invention discloses a rescue drilling machine multi-model predictive control method for complex stratum drilling. The method comprises the following steps that a bit pressure control system model of a rescue drilling machine with voltage as input and bit pressure as output is established for each stratum based on a finite element method; performing rigid-flexible mode separation on the bit pressure control system model of each stratum based on a frequency domain analysis method, and establishing a reduced-order model of the bit pressure control system model of each stratum; a corresponding feeding damping coefficient is determined under each stratum, the multiple stratums are in one-to-one correspondence with the multiple order reduction models, and each stratum is provided with a controller in advance; and when the stratum condition changes, the corresponding controller is switched to predict and control the bit pressure according to the stratum condition. According to the method, the formation change can be quickly responded, and the bit pressure is adjusted to keep the stability and efficiency of the drilling process, so that the bit pressure can be always stabilized within the safety range of the set value under different formations.

The application belongs to the field of industrial automation and mechanical control, and discloses a heavy-load mechanical arm trajectory tracking and control method and system under dynamic working conditions, which comprises dynamic parameter identification, multi-model predictive control and fault-tolerant control, real-time acquisition of mechanical arm motion state data, real-time updating and optimization of the dynamic parameters of the mechanical arm by combining the recursive least square method and the particle swarm optimization algorithm, construction of a dynamic prediction model set under different dynamic load working conditions and motion states, dynamic selection of the optimal model and adjustment of the mechanical arm trajectory tracking strategy by using the model predictive control algorithm, real-time monitoring of the mechanical arm motion state, simultaneous fault detection and diagnosis, activation of redundant control once a fault is detected, dynamic adjustment of the control strategy by combining the redundant degrees of freedom of the mechanical arm and the distributed control architecture, maintenance of the normal operation of the system, and improvement of the robustness and reliability of the system to adapt to the requirements of complex operation scenarios.

The invention discloses a multi-scale cooperative regulation and control method for a green hydrogen metallurgy comprehensive energy system, and belongs to the technical field of energy system regulation and control. Firstly, various flexible and adjustable resources in the green hydrogen metallurgy comprehensive energy system are analyzed in detail, adjustment characteristics of the resources on different time scales are summarized, secondly, cross-link adjustment characteristics of the resources in multi-energy-mass interaction are discussed, and particularly, the time delay phenomenon in the multi-energy-mass transmission and conversion process is concerned. The invention further provides an equivalent modeling method aiming at the heterogeneous problem of each link, and provides support for multi-link collaborative regulation and control of the system. And finally, researching the influence of different production tasks on the multi-energy mass flow distribution, and analyzing the dynamic association of different production processes on the multi-energy mass flow distribution. Therefore, an extended resource-task network modeling method is adopted, and a multi-model prediction control strategy combining a prediction model and a local model is combined, so that the control efficiency of the system is improved, and multiple requirements of energy efficiency, flexibility, risk control and the like are met.

The invention relates to the technical field of thermal power generating unit deep peak regulation, and discloses a thermal power generating unit deep peak regulation method based on multi-model predictive control, and the method comprises the steps: collecting a historical power output value and a future meteorological data value of a thermal power generating unit, building a load prediction model, determining an operation scheme of each unit, and carrying out the simulation operation of the determined operation scheme. And constructing an emission consumption model, simulating the operation scheme, inputting operation data into the emission consumption model, calculating to obtain an emission consumption index, and if the emission index does not reach the standard, outputting prediction data again through the load prediction model until data of the emission consumption model is met. According to the method, collected data comprise electric power output values of historical thermal power generating units under the condition that meteorological data values of future prediction days are equivalent, the future meteorological data values comprise future temperature and humidity values, the dispatching proportion distributed to k units is solved through a load prediction model, and the dispatching condition of each unit is determined. Therefore, automatic scheduling is realized.

The invention discloses a multi-model prediction control method and system adopting a B / S architecture for a coal power unit. The system comprises a data acquisition module which is used for acquiring operation data of the coal power unit in real time; the control algorithm module is used for establishing a control algorithm model by adopting a built-in control algorithm and a self-defined algorithm according to the collected data and is used for calculating an optimal control strategy; the predictive control application module combines data with an algorithm model in a visual configuration mode, and accurately controls the coal power unit according to a control strategy output by the algorithm model to realize optimization; and the monitoring and feedback module is used for monitoring the operation state of the coal power unit in real time and feeding back an operation result to the control algorithm model to realize dynamic adjustment of a control strategy. The method is beneficial to improving the operation efficiency of the coal power unit, reducing the coal consumption, reducing the emission, and being beneficial to indexes such as energy conservation and emission reduction.

The present invention discloses a multi-model predictive control method based on multi-model gap measurement PGNN feedforward dynamic compensation, which relates to the technical field related to coordinated control of generator sets. The predictive model estimates the future state of the system and guides the controller to make adjustments in advance to solve the deviation and overshoot problems that are easy to occur in the main steam temperature when the unit is running at a wide load, and realizes accurate and rapid response to load changes through feedforward compensation. This solution integrates technologies such as PGNN predictive model, feedforward dynamic compensation and multi-model predictive control to improve the flexibility of the control system. Through accurate prediction of the predictive model and optimization of valve opening feedforward dynamic compensation, the control system can better respond to changes in the main steam temperature, improve the performance of the control system, eliminate deviations, maintain stability, and enable the main steam temperature to more accurately follow the set value, thereby achieving better control effects and improving control quality.

The invention provides a coordinated control method for an ultra-supercritical coal-fired power generating unit, and the method comprises the steps: constructing a sub-model set needed by a multi-model prediction control algorithm based on a gap measurement method, and independently carrying out the prediction and rolling optimization of each sub-controller through the prediction control algorithm; and the optimal control quantity is obtained by using the weighting strategy of the controller and acts on the unit coordination system. According to the invention, coordinated control of the ultra-supercritical coal-fired power generation unit can be realized, so that the unit can quickly track a load instruction and ensure stability of main steam pressure and outlet temperature of a steam-water separator, and the stability of the unit is improved; the fluctuation of fuel quantity is reduced, and the high efficiency of the unit is improved; the variable load rate of the unit is increased, and the flexibility of the unit is improved.

The application discloses an intelligent multi-model predictive control method and system based on a twin model. The intelligent multi-model predictive control method comprises the following steps: constructing a basic multi-model predictive controller by using multiple working condition basic models and linear interpolation blocks, realizing stable control in the basic multi-model predictive controller, and generating a twin model; constructing an intelligent multi-model predictive controller by using multiple twin models and a selection module; and sequentially performing multi-working condition twin model matching in the intelligent multi-model predictive controller, and finally realizing all-working condition optimal control. The application can realize accurate control in each working condition, and completes the diagnosis and optimization of a controlled system online.

The present invention discloses a multi-model predictive control method for a rescue drilling rig for drilling in complex strata, comprising the following steps: establishing a drill pressure control system model for the rescue drilling rig using voltage as input and drilling pressure as output for each stratum based on the finite element method; performing rigid-flexible mode separation on the drill pressure control system model for each stratum based on the frequency domain analysis method, and establishing a reduced-order model of the drill pressure control system model for each stratum; determining a corresponding feed damping coefficient for each stratum, and assigning a one-to-one correspondence between multiple strata and multiple reduced-order models, wherein each stratum is pre-configured with a controller; and when the stratum conditions change, switching the corresponding controller to predictively control the drill pressure according to the stratum conditions. The method of the present invention can quickly respond to stratum changes and adjust the drill pressure to maintain the stability and efficiency of the drilling process, thereby ensuring that the drill pressure can always be stable within a safe range of the set value in different strata.

The skid-mounted on-demand preparation system comprises a power dispatching module, a hydrogen and nitrogen synthesis module, a micro-channel ammonia synthesis reactor, a thermal coupling absorption module, a precise spraying module and a control module, the hydrogen and nitrogen synthesis module comprises a double-PEM electrolytic bath and a PSA nitrogen making machine, and the thermal coupling absorption module comprises a secondary water absorption tower. The precise injection module comprises a double-fluid atomization spray gun and a porous nozzle array, and the control module comprises a multi-model predictive control and mixed integer nonlinear programming algorithm and is used for regulating and controlling start and stop of the double-PEM electrolytic cell, synthetic reaction parameters of the micro-channel synthesis ammonia reactor and an injection strategy of the precise injection module in real time. According to the ammonia water immediate production and immediate use system driven by renewable energy sources, self-adaptive adjustment of the ammonia water concentration is achieved, the denitration response time is shortened to be within 6.2 minutes, and the requirements of high-temperature and high-concentration working conditions of multiple scenes such as thermal power, steel, cement and glass are met.

The invention discloses an engine multi-model predictive control method and system based on an organic liquid hydrogen carrier, a computer readable storage medium and electronic equipment, and belongs to the field of automobile engine control. Building an initial simulation model of the hydrogen engine system, and performing optimization training to obtain a simulation model; coupling the simulation model, and performing intensive training to obtain a heat coupling simulation model; predicting the working condition of the engine by using the heat coupling simulation model to obtain a predicted working condition of the engine; and when the predicted working condition of the engine meets the hydrogen supply adjusting condition of the engine, a control signal is generated, the amount of heat provided by the heat source to a hydrogen supply module is controlled, and then the output power of the engine is adjusted. According to the method provided by the embodiment of the invention, the problem that the hydrogen supply device cannot release hydrogen to the engine in time, so that the working efficiency of hydrogen combustion in the engine and the output power of the engine are influenced is solved.

The present invention discloses a gas turbine T-S model optimization method based on a multi-model predictive control algorithm. The method comprises data preprocessing, using five adjacent sampling points to perform cubic polynomial fitting to preprocess the data and improve the data quality; establishing a characteristic vector, that is, constructing a characteristic vector combination of input and output variables; performing antecedent structure identification on the characteristic vector; performing consequent parameter identification on the data obtained in step S3; obtaining a T-S fuzzy model; obtaining a step response vector model of a corresponding working condition in the T-S fuzzy model obtained in step S5, and using a DMC algorithm to obtain an optimal input quantity to optimize the gas turbine output.

The present invention discloses a multi-scale collaborative control method for a green hydrogen metallurgical integrated energy system, belonging to the field of energy system control technology. First, a detailed analysis is conducted on various flexible and adjustable resources within the green hydrogen metallurgical integrated energy system, and their regulation characteristics at different time scales are summarized. Secondly, the cross-link regulation characteristics of these resources in the interaction of multiple energy materials are explored, especially focusing on the time delay phenomenon in the transmission and conversion process of multiple energy materials. Then, an equivalent modeling method for the heterogeneous problems of each link is proposed to provide support for the multi-link collaborative control of the system. Finally, the impact of different production tasks on the distribution of multiple energy materials flows is studied, and the dynamic correlation of different production processes on the distribution of multiple energy materials flows is analyzed. To this end, an extended resource-task network modeling method is adopted, and a multi-model predictive control strategy combining prediction models and local models is combined to improve the control efficiency of the system and meet multiple requirements such as energy efficiency, flexibility, and risk control.

The application discloses a flight simulator somatosensory simulation method based on multi-model predictive control, relates to the technical field of flight simulators, and uses a flight scene identifier to monitor flight parameters from a flight simulator in real time, identifies a flight scene, guides a somatosensory predictive model switcher and a motion platform constraint strategy switcher, and makes them smoothly switch to the best somatosensory predictive model and motion platform constraint strategy that match the current scene and the working space of the motion platform, are transported to a high-performance model predictive controller to output optimal control parameters to a motion platform actuator, and finally integrate the somatosensory simulation method that can effectively utilize the working space of the motion platform and provide the high somatosensory fidelity required by flight training under different flight task scenes. The application widens the application scene of a single somatosensory simulation method, guarantees the high somatosensory fidelity of pilot training under multiple scenes, and has important significance for improving the training quality of pilots in a flight simulator.