Abstract
In industrial steam systems, the process requires a specific pressure, and the maximum permissible operating pressure is different. If the inlet steam pressure to the steam consuming equipment exceeds the operating pressure, it may cause hazards. Therefore, the more precise control of the boiler pressure is important. Since we are dealing with a nonlinear, time-varying, and multivariable system, the control method must be designed to handle this system well. Most of the methods proposed so far are either not physically feasible or the system has considered very simple. Therefore, in this paper, while modeling the boiler and its pressure relations more precisely, we will introduce a recurrent type-2 fuzzy RBFN-based model reference adaptive control system with various uncertainties so that the uncertainty and inaccuracy of the model can be compensated. The experimental results prove the efficiency of the proposed method in boiler control.
Highlights
In industrial steam systems, the process requires a specific pressure, and the maximum permissible operating pressure is different
Most of the methods proposed so far are either not physically feasible or the system has considered very simple. erefore, in this paper, while modeling the boiler and its pressure relations more precisely, we will introduce a recurrent type-2 fuzzy RBFN-based model reference adaptive control system with various uncertainties so that the uncertainty and inaccuracy of the model can be compensated. e experimental results prove the efficiency of the proposed method in boiler control
Neural network-based control systems [16,17,18,19,20], fuzzy system [21,22,23], and fuzzy neural networks [24,25,26,27] have shown good performance. e following discusses some methods of boiler control based on computational intelligence
Summary
E general form of nonlinear systems is x_ f(x, u), which can be written as dx dt f1. Using the Taylor extension, we will have dx fx0 + Δx, u0 + Δu dt fx0, u0 + df x0, u0Δx + df x0, u0Δu dx du. Regardless of the nonlinear terms, we will have dx fx0, u0 + df x0, u0Δx + df x0, u0Δu, (7). The output of the nonlinear system model is as follows: y1 h1 x1, x2, . We can write the general vector symbol of the nonlinear system and the space model mode of the linear system as x_ f(x, u), (18). 0.2279 0.00427x01 −0.014 erefore, a linear state space form can be written for the boiler: x_ Ax + Bu, (27). E variables y, x, and u are the output, mode, and input of the boiler system at the operating points, respectively
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