Multimodel Fractional Predictive Functional Control Design with Application on an Industrial Heating Furnace

Abstract

This paper mainly targets the nonlinear characteristics in industrial heating furnace control and uses the multimodel method to decompose the global dynamics of the process into a series of local model sets. The corresponding predictive functional controller is designed using the local fractional order models of the set within their working range. The weight coefficients of the different models in the model sets are obtained based on the error of the different model sets at current moment, and the system performance at current moment is obtained by weighting between many models using the weight coefficient. Since the accuracy of the local models is increased, the error between the local model set and the process output is reduced, which decreases the influence of the system model inaccuracy on the system performance. Finally, the strategy is verified through the experiment on the temperature of a heating furnace.

A low-cost pole-placement MPC algorithm for controlling complex dynamic systems

“…Given a stable S-domain transfer function G m (s), it can be discretized to a Z-domain transfer function G m (z −1 ) at a given sampling time. Then G m (z −1 ) is decomposed into two part as (9). The part G m1 (z −1 ) can be designed into G in m (z −1 ) with proper K(z −1 ).…”

“…However, industrial practitioners appreciate the desired strong link between the selected or desired behavior and what is achieved; unfortunately, this link is often weaker in practice than required to be useful. Consequently, many researchers have proposed several modifications to PFC to improve the link between targeted and achieved behavior for various system types [9,10].…”

Temperature Control of Electric Furnace Using Adaptive Lag Compensator Based on Improved Gorilla Troops Optimization: Towards Energy Efficiency

“…Industrial goods, such as those involving metal melting, polymerization, drying, and other physicalchemical processes, commonly employ industrial heating furnaces (HFs) [1], [2]. Electric HF temperature processes are characterized by great inertia, time delay, and unpredictability, which makes it difficult for conventional control techniques to fulfill the growing expectations for control performance improvement that directly impact product quality [3], [4].…”

A non-integer sliding mode controller to stabilize fractional-order nonlinear systems

“…For example, Brownian fractional motion [4], porous media dynamics [5], time-lapse random walk theory [6], heat transfer process [7], electrochemical processes and flexible structures [8] and chaos theory are among these. The ability of fractional order calculators to improve the performance of controllers has been demonstrated [9]. In 1988, Oustaloup introduced a robust fractional-order controller called Crown, and created a starting point for entering fractional-order relationships into control.…”