Non‐linear adaptive control of a heat exchanger

Abstract

In this contribution, two methods for adaptation of non-linear adaptive controllers are presented and compared, namely the data-driven and the knowledge-based adaptation. A dynamic Takagi}Sugeno fuzzy model is utilized to model the non-linear process behaviour. Based on this model, a non-linear predictive controller is designed to control the process. In the presence of time-variant process behaviour and changing unmodelled disturbances, high control performance can be achieved by performing an on-line adaptation of the fuzzy model. First, a local weighted recursive least-squares algorithm is used for adaptation. It exploits the local linearity of the Takagi}Sugeno fuzzy model. In the second approach, process knowledge that is obtained from theoretical insights is utilized to design a knowledge-based adaptation strategy. Both approaches are compared and their e!ectiveness and real-world applicability are demonstrated by application to temperature control of a heat exchanger.

An approach to improve the performance of adaptive predictive control systems: theory, simulations and experiments

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On-line identification of computationally undemanding evolving fuzzy models

“…The air-conditioning system, which is located in the USA at the Iowa Energy Centre [7], consists of a supply air duct, a return air duct, an outside air duct, and an air-handling unit with a cooling coil and a heating coil. Fresh air entering the air-conditioning system through the outside air duct is mixed with the air that is extracted from the building via the return air duct before it passes through the cooling and heating coils.…”

“…The pioneering work on the so-called self-organising controller used a fuzzy model based on a Mamdani rules [11]. More recently, self-tuning control schemes have been proposed that are based on T -S fuzzy models [5,7,8] and fuzzy relational models (FRMs) [17].…”

Robust nonlinear adaptation algorithms for multitask prediction networks

“…The goal of adaptation is to improve the prediction accuracy in subsequent rounds. Online adaptation is currently present in a variety of research fields like behavior prediction, 1,2,6‐8 speech recognition, 9 image recognition, 10 and machine translation 11 …”