Abstract
An optimal fuzzy controller design scheme is proposed to address the influence of time delay and disturbance on the control performance of nonlinear batch processes. First, a two-dimensional (2D) equivalent Takagi-Suguno (T-S) fuzzy error model is formulated. By introducing a quadratic performance index function and adopting 2D Lyapunov-Krasovskii theory, the existence condition of the optimal fuzzy control law is given. Furthermore, its solvable condition, which depends on time-delay bounds, is constructed in terms of linear matrix inequalities, and its gain is obtained by using an optimization algorithm. This design has the advantages of faster tracking and better tracking performance. Finally, two different algorithms (with and without optimization) are used to control the water level of a triple-capacity water tank. The results show that the presented strategy is more effective and feasible.
Highlights
To improve production efficiency and save production costs, there is an urgent need for advanced control strategies and optimization methods in industrial production mode [1]
ILC takes into account the batch and time direction characteristics of the batch process and has a better control effect than iterative learning control or feedback control
Fuzzy iterative learning optimal control for nonlinear time-delayed batch processes is discussed in the paper
Summary
To improve production efficiency and save production costs, there is an urgent need for advanced control strategies and optimization methods in industrial production mode [1]. Wang et al proposed a delay-range-dependent method for iterative learning guaranteed cost control for batch processes [25] and extended the results to optimal. Steady operation is the basic demand of the production process It is the lifelong goal of all enterprises and researchers that systems have better control performance to achieve the goal of energy conservation and emission reduction. Based on this objective, the optimal control of batch processes with time delay has attracted much attention. All the models considered in these results are linear system models For this reason, fuzzy iterative learning optimal control for nonlinear time-delayed batch processes is discussed in the paper. Rn is an n-dimensional Euclidean space and R(n+l)×(n+l) is a set of (n + l) × (n + l) real matrices. xtT,k denotes the transpose of xt,k . # represents a transposed element in the symmetric position
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