Layering predictive control system and method based on model predictive control

Abstract

The invention discloses a layering predictive control system and method based on model predictive control. The layering predictive control system based on model predictive control comprises an upper-layer traditional predictive control module, a sensor and a lower-layer real-time feedback correction module, wherein the input end of the upper-layer traditional predictive control module and the input end of the lower-layer real-time feedback correction module are respectively connected with the output end of the sensor, the input end of the sensor is connected with the output end of a controlled object, the upper-layer traditional predictive control module is connected with the lower-layer real-time feedback correction module, the output end of the lower-layer real-time feedback correction module is connected with the input end of the controlled object, and at every control moment, the lower-layer real-time feedback correction module can correct an optimized control quantity sent by the upper-layer traditional predictive control module and send the corrected optimized control quantity to the controlled object. The layering predictive control system and method based on model predictive control guarantee the control performance of a controller, popularize the application of a predictive controller to part of rapid systems, and have practicability and application value.

Description

technical field [0001] The invention relates to a control system and a control method thereof in the field of DC motor control, in particular to a layered multi-rate predictive control system based on model predictive control and a control method thereof. Background technique [0002] Model predictive control is an advanced control method developed in the 1970s. Because of its low requirements on the model, good robustness, and the ability to handle constraints more conveniently, it is favored by the industry, especially in industrial control objects such as chemical, oil refining, and electric power. However, the model predictive control system is a controller that adopts online rolling optimization, that is, the controller needs to take the current state of the system as the initial state at each sampling moment, and solve an optimization problem in the finite time domain online to obtain the current control quantity of the system. Therefore, in practical applications, th...

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Problems solved by technology

However, the loss of control quality due to the reduction of the degree of freedom of the optimization problem is still inevitable. At the same time, the design of the controller still needs to solve the optimization problem online at each control moment to obtain the real-time control quantity. Such time-consuming in some fast systems However, it should not be underestimated, especially in today’s increasingly obvious dual requirements for “fastness” and “cheapness” of the control system in practical applications. In some fast systems, the model predictive controller cannot even complete the control amount once in one cycle. There is no doubt that this problem has a negative impact on the control quality. Therefore, how to improve the traditional model predictive controller with a reasonable price to adapt to the control of fast systems, and to ensure the control performance of the predictive controller as much as possible. For Industrial applications have greater significance

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