17 results about "Nonlinear predictive control" patented technology

The invention belongs to the technical field of fine chemical reaction safety testing and automatic chemistry, and particularly discloses a nonlinear model predictive temperature control method of a semiconductor temperature control type synthesis reactor. The method comprises the following steps: constructing a dynamic heat transfer model of the system based on the semiconductor temperature control type synthesis reactor; designing a nonlinear model prediction controller according to the heat transfer model of the synthesis reactor; constructing a multi-constraint objective function for nonlinear predictive control according to the characteristics of the controlled object; and solving an optimal control sequence for the nonlinear model prediction controller by adopting a projection gradient method. Compared with a traditional PID temperature control algorithm, the temperature of the reactor can be accurately predicted and controlled through the designed nonlinear model prediction temperature control algorithm, it is ensured that the chemical reaction is conducted at the optimal temperature, and the robustness and adaptability of a control system are improved.

The invention discloses a PINN-based magnetic levitation vehicle nonlinear predictive control method and device and a medium, and relates to the field of rail transit, and the predictive control method comprises the following steps: S1, constructing a dynamic model; s2, learning and approaching unknown dynamic and time-varying parameters in the dynamic model by adopting a physical information neural network; s3, designing an optimization cost function meeting system input and output constraints based on the unknown dynamic state of the dynamic model predicted by the physical information neural network; and S4, constructing a step-by-step variable terminal constraint set to process input time lag. In a controller design and theoretical analysis process, a non-linear dynamic form of a system is completely reserved, and a stepping variable terminal constraint set suitable for input time delay is constructed, so that the controller can still maintain stability and dynamic performance under the condition that a system state obviously deviates from a balance point. According to the method, the operation safety and the control precision of the maglev vehicle under complex working conditions such as parameter drift and time lag disturbance are effectively improved.

The invention relates to the technical field of intelligent control of refrigeration equipment, and discloses an intelligent energy-saving control method and system for a refrigeration display cabinet. The method comprises the following steps: acquiring temperature distribution and human traffic data in the refrigeration display cabinet through a multi-parameter sensor network, inputting the data into an ARIMA model after moving average filtering processing, and predicting a refrigeration load fluctuation trend; establishing a dynamic energy efficiency ratio indicator based on the ratio of the compressor power to the load prediction value, and quantifying the energy-saving effect; the compressor frequency and the fan rotating speed are adjusted by combining a nonlinear predictive control algorithm, and a cooperative control instruction is generated; and the influence of the instruction on the system load is analyzed through the refrigerant flow tendency function, the defrosting period is synchronously optimized, and an energy-saving control strategy is formed and executed. According to the invention, predictability and collaboration of energy efficiency management of the refrigeration display cabinet are improved.

The invention discloses a ship power regulation and control method and system based on a hydrogen fuel cell, and relates to the technical field of ship power control, and the method comprises the steps: carrying out the dynamic adjustment of power output based on an adaptive power distribution decision, calculating the output power needed by the hydrogen fuel cell through a nonlinear prediction control method, and obtaining the theoretical hydrogen supply flow; based on the theoretical hydrogen supply flow, the opening degree of a micro proportional valve is adjusted, the dynamic response characteristic of the hydrogen fuel cell is optimized, and the actual power of the hydrogen fuel cell is obtained; and comparing the actual power of the hydrogen fuel cell with the instantaneous power demand, and dynamically adjusting the hydrogen supply flow and optimizing the power distribution strategy in combination with a reinforcement learning algorithm and a nonlinear prediction control method to obtain an optimized power distribution strategy. According to the invention, the output power of the hydrogen fuel cell is calculated by using a nonlinear predictive control method, and the opening degree of the micro proportional valve is adjusted in real time, so that the extra load of the hydrogen fuel cell caused by power fluctuation is reduced, and the stability and the energy efficiency are improved.

The invention relates to the technical field of automatic control, and discloses a suction force self-adaptive adjusting method based on a suction cup base, comprising the following steps: collecting current attitude disturbance information of to-be-adsorbed objects including a lamp, glass, a steel plate, a plastic box body and a wood material plate, the attitude disturbance information comprises a pitch angle, a roll angle, an angular velocity and a vertical acceleration; estimating the offset of the gravity center of the to-be-adsorbed object relative to the center of the sucker array based on the attitude disturbance information; according to the offset, target adsorption force of the object to be adsorbed is determined, and distribution is carried out according to a set load distribution weight; the invention further discloses a suction force self-adaptive adjusting system based on the suction cup base. The system comprises a posture sensing module; an offset calculation module; an adsorption force distribution module; and an adsorption control module. According to the invention, dynamic coordination and accurate tracking control of multi-sucker adsorption force are realized by fusing attitude sensing, adsorption modeling and nonlinear predictive control.

The application relates to the technical field of intelligent control of refrigeration equipment, and discloses an intelligent energy-saving control method and system for a refrigeration display cabinet. The method comprises the following steps: collecting temperature distribution and passenger flow data in the refrigeration display cabinet through a multi-parameter sensor network, inputting the data into an ARIMA model after sliding average filtering processing, and predicting a refrigeration load fluctuation trend; a dynamic energy efficiency ratio indicator is established based on the ratio of compressor power to the predicted value of the load, and the energy-saving effect is quantified; the compressor frequency and the fan rotating speed are adjusted in combination with a nonlinear prediction control algorithm, and a cooperative control instruction is generated; the influence of the instruction on the system load is analyzed through a refrigerant flow tendency function, the defrosting cycle is optimized synchronously, an energy-saving control strategy is formed, and the strategy is executed. The application improves the predictability and cooperativeness of energy efficiency management of the refrigeration display cabinet.

The invention provides a nonlinear predictive control method for a Novolens polypropylene process, and relates to the technical field of nonlinear predictive control of a chemical process, and the method comprises the following steps: S1, building a mechanism model corresponding to a controlled loop according to the internal reaction mechanism of the Novolens polypropylene process, and obtaining the real-time gain of the process; s2, obtaining a time constant and a lag time parameter of the controlled Novolens polypropylene process model; s3, constructing a corresponding mechanism state space model through the real-time gain, the time constant and the delay time; s4, on the basis of the mechanism state space model, state prediction of the controlled Novolens polypropylene process is obtained; s5, an objective function is introduced, and the optimal control input increment of the controlled Novolens polypropylene process is obtained; and S6, performing calculation according to the optimal control input increment to obtain optimal control. The method is used for accurately realizing optimization control of the Novolens polypropylene process.

The embodiment of the invention discloses a path following control method based on a quasi-linear parameter change model and a related device. A quasi-linear parameter system model in a linear or semi-linear form is constructed according to an unmanned aerial vehicle kinematics nonlinear system model so as to capture nonlinear characteristics of the system, and the nonlinear model can be accurately approached. The speed-based linearization is a strategy for accurately representing system nonlinear dynamics in a linear form, and the precision of a system model is greatly improved compared with that of a linear system. As a linear form is reserved, the speed of the algorithm is greatly improved compared with that of a control algorithm of a nonlinear system. A qLPV system is adopted for modeling, the limitation that a linear system is low in modeling precision and a nonlinear predictive control algorithm is low in speed is overcome, the dynamic characteristics of a nonlinear system can be accurately described, meanwhile, a linear form or a semi-linear form is reserved, solving can be conducted through a fast algorithm solver, a fast control algorithm is designed, and real-time control is achieved.

This invention discloses a method and system for ship power regulation based on hydrogen fuel cells, relating to the field of ship power control technology. The method includes: dynamically adjusting power output based on adaptive power allocation decision-making; calculating the required output power of the hydrogen fuel cell using a nonlinear predictive control method to obtain the theoretical hydrogen supply flow rate; optimizing the dynamic response characteristics of the hydrogen fuel cell based on the theoretical hydrogen supply flow rate and adjusting the opening of a micro proportional valve to obtain the actual power of the hydrogen fuel cell; comparing the actual power of the hydrogen fuel cell with the instantaneous power demand; and dynamically adjusting the hydrogen supply flow rate by combining reinforcement learning algorithms and nonlinear predictive control methods to optimize the power allocation strategy, resulting in an optimized power allocation strategy. This invention reduces the additional load on the hydrogen fuel cell caused by power fluctuations by using a nonlinear predictive control method to calculate the output power of the hydrogen fuel cell and combines this with real-time adjustment of the micro proportional valve opening, thereby improving stability and energy efficiency.

The application discloses a kind of green hydrogen synthesis ammonia energy-saving reaction control methods based on hydrogen nitrogen ratio regulation, belong to synthetic ammonia preparation technical field.The application is realized hydrogen nitrogen ratio dynamic accurate regulation by real-time acquisition of hydrogen nitrogen flow at reactor inlet, using nonlinear predictive control algorithm, and the best stoichiometric ratio is stably maintained;Fusion low-temperature plasma assisted catalysis technology reduces reaction activation energy and reaction temperature;Build hierarchical reaction heat gradient recovery system to improve energy utilization rate;Synchronous coupling green electricity system realizes production load dynamic self-adaptive regulation.This method greatly reduces the energy consumption of synthetic ammonia reaction, adapts renewable energy output characteristics, realizes the low-carbon energy-saving flexible production of green hydrogen synthesis ammonia.

The present invention discloses a time-space integrated control method for a ballast water heat treatment system, including obtaining a multi-stage series treatment system of a cylinder water heat source for ballast water heat treatment to simplify obtaining an equivalent plug flow reactor model; obtaining the time-space temperature data of the equivalent plug flow reactor model, and constructing an optimal temperature time-space model for ballast water heat treatment according to the time-space temperature data; and constructing a nonlinear predictive controller for controlling the temperature of heated ballast water according to the optimal temperature time-space model. The present invention solves the problem that the existing heat treatment technology does not effectively take into account practical factors such as cabin space allocation and efficient energy utilization, the existing modeling method has linear assumption limitations, insufficient local structure retention, and cannot fully explore the nonlinear characteristics and uncertainty factors in the ballast water heat treatment process to explore the connection between temperature fluctuations and changes in microbial concentrations, resulting in the inability to effectively achieve the control accuracy and efficiency of the ballast water heat treatment system.

The present invention relates to a distributed intelligent predictive control method for flexible load variation of a thermal power unit. This method addresses the nonlinear dynamic characteristics of the unit during load variation by constructing a nonlinear predictive control model using data-driven machine learning. To address the real-time nature and complexity of nonlinear predictive control, a distributed control strategy is employed to divide the unit into two parallel computational sub-control systems: the boiler and the turbine. Information exchange between the two sub-control systems is achieved using a Nash equilibrium game method, as well as global optimization decision-making for the entire thermal power unit. Compared to existing technologies, this method can reduce operating parameter fluctuations while ensuring the unit's tracking accuracy over a wide range of load variations, thereby achieving safe, economical, and stable operation of the thermal power unit.

The invention discloses a predictive control system model mismatch diagnosis method, which belongs to the technical field of industrial process manufacturing, and comprises the following steps: respectively simulating predictive control systems in a model normal state and a model mismatch state; collecting a one-dimensional time sequence of each key variable of the simulation prediction control system; performing image visualization on the one-dimensional time sequence through a preset image conversion matrix, generating an image data set in a model normal state and a model mismatching state, and labeling a corresponding state label on each image; and inputting the marked image data set into an image classification neural network, and carrying out real-time diagnosis on a prediction control system by extracting image features. The method solves the problems that in the prior art, for a multivariable, strong-coupling and nonlinear predictive control system, the hysteresis quality is high, and the real-time diagnosis precision of a model is not high.

The invention provides a steering and torque distribution integrated control method for a four-wheel independent drive vehicle based on nonlinear predictive control, which comprises the following steps of: firstly, establishing a nonlinear state-space equation of the four-wheel independent drive vehicle, and establishing an optimal control problem based on the state-space equation; and then the optimal control problem of the original continuous time system is converted into a nonlinear optimization problem by using a forward Euler method and a multiple targeting method, and the singularity of the nonlinear optimization problem is effectively eliminated and the convexity of the nonlinear optimization problem is improved by adding a regular term in an optimization target. Finally, a moderate-scale sequential quadratic programming mode is designed, and compared with the prior art adopting hierarchical nonlinear predictive control, an open source solver IPOPT and the like, the control precision and the solving speed can be effectively improved.

The present invention belongs to the fields of fine chemical reaction safety testing and automated chemistry technology, and specifically discloses a nonlinear model predictive temperature control method for a semiconductor temperature-controlled synthesis reactor. The method comprises the following steps: constructing a dynamic heat transfer model of the system based on the semiconductor temperature-controlled synthesis reactor; designing a nonlinear model predictive controller based on the heat transfer model of the synthesis reactor; constructing a multi-constraint objective function for nonlinear predictive control based on the characteristics of the controlled object; and solving the optimal control sequence for the nonlinear model predictive controller using the projected gradient method. Compared to traditional PID temperature control algorithms, the nonlinear model predictive temperature control algorithm designed by the present invention can achieve accurate prediction and control of the reactor temperature, ensuring that the chemical reaction proceeds at the optimal temperature and improving the robustness and adaptability of the control system.

The application discloses a magnetic levitation vehicle nonlinear prediction control method and device based on PINN and a medium, relates to the field of rail transit, and comprises the following steps: S1, constructing a dynamic model; S2, learning and approximating unknown dynamics and time-varying parameters in the dynamic model by using a physical information neural network; S3, designing an optimization cost function meeting input and output constraints of the system based on unknown dynamics of the dynamic model predicted by the physical information neural network; and S4, constructing a step-variable terminal constraint set to process input time delay. In the controller design and theoretical analysis process, the nonlinear dynamics form of the system is completely reserved, and a step-variable terminal constraint set suitable for input time delay is constructed, so that the controller can still maintain stability and dynamic performance in the case that the system state deviates from the equilibrium point significantly. The method effectively improves the running safety and control precision of the magnetic levitation vehicle in the face of complex working conditions such as parameter drift and time delay disturbance.