Fuzzy predictive control method capable of enhancing robustness based on disturbance observer

Abstract

The invention provides a fuzzy predictive control method capable of enhancing robustness based on a disturbance observer. The method comprises the following steps: 1) establishing a discrete fuzzy disturbance observer model, an auxiliary controller and a robust predictive controller; 2) solving a feedback gain of the auxiliary controller and a gain of the disturbance observer to obtain a minimum robust invariant set, and calculating a tight constraint set of control input and state variable of the robust predictive controller; 3) initializing the system state variable and assigning a value tothe state variable of a nominal model; 4) for the current nominal model state variable, solving the optimization problem that minimizes the upper bound gamma of predictive control performance, and obtaining a current disturbance estimated value; 5) calculating control input of the system and applying the control input to a controlled object; 6) applying the control input of a nominal system to thenominal model and calculating output of the current state and current state quantity output of a sampling system; and 7) performing substituting and setting k=k+1, and then, skipping to the step 4).The stability of a predictive control system is improved.

Description

technical field [0001] The invention belongs to the technical field of thermal control, and in particular relates to a fuzzy predictive control method based on a disturbance observer to enhance robust characteristics. Background technique [0002] Power plants operate in complex environments, such as changes in coal products, changes in ambient temperature and humidity, and load disturbances in the power grid. At the same time, there are serious mismatches in the internal model of the power plant, including modeling errors, coking in heat exchanger tubes, and coking in the furnace. This requires that the design of the control system should not be limited to stable conditions, but also consider the robustness and anti-disturbance ability of the system. Due to the highly nonlinear nature of the machine-furnace power generation system, it is difficult to realize the large-scale variable-condition operation of the system with the predictive controller designed based on the nomi...

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Problems solved by technology

Due to the highly nonlinear nature of the machine furnace power generation system, it is difficult to realize the large-scale variable operating conditions of the system by the predictive controller designed based on the nominal model

Traditional pipeline predictive control can explicitly deal with disturbances, but its design is usually based on a linear model or a time-varying linear system with multiple cells. The design is unavoidably conservative. At the same time, it faces the problems of unknown strong disturbances and model mismatches. The performance of predictive control will decrease, and even unstable results will appear

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