6 results about "Nonlinear predictive control" patented technology

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 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 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 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.