A Control Design Approach for TITO Systems Using Measured Data

Sofiane Khadraoui, Raouf Fareh, Hazem Nounou, Mohamed Nounou

Research output: Contribution to journalArticle


This paper deals with the design of fixed-structure controllers for two-input two-output (TITO) systems using frequency-domain data. In standard control approaches, a plant model is first derived, then a suitable controller is designed to meet some user-specified performance specifications. Basically, there are two common ways for obtaining mathematical models: white-box modeling and black-box modeling. In both approaches, it is difficult to obtain a simple and accurate model that completely describes the system dynamics. As a result, errors associated with the plant modeling may result in degradation of the desired closed-loop performance. Moreover, the intermediate step of plant modeling introduced for the controller design is a time-consuming task. Hence, the concept of data-based control design is introduced as a possible alternative to model-based approaches. This promising methodology allows us to avoid the under-modeling problem and to significantly reduce the time and workload for the user. Most existing data-based control approaches are developed for single-input single-output (SISO) systems. Nevertheless, a large class of real systems involve several manipulated and output variables. To this end, we attempt here to develop an approach to design controllers for TITO systems using frequency-domain data. In such a method, a set of frequency-domain data is utilized to find an adequate decoupler and to tune a diagonal controller that meets some desired closed-loop performance measures. Two simulation examples are presented to illustrate and demonstrate the efficacy of the proposed method.

Original languageEnglish
Article number011013
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Issue number1
Publication statusPublished - 1 Jan 2019


ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications

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