Análise do desempenho de sistemas de controle

Abstract

When the controller s performance is reduced along the time and the control efficiency is no longer satisfactory, then it is necessary to develop certain techniques for continuously monitor control systems such as it can keep track of process changes maintaining the characteristics of efficiency of the original controller design. The divergences between controlled variables and the desired setpoint can be quantified by a single number that is known as performance index. In the present work, a novel way of evaluating the control process performance is introduced and applied as a new measure of performance for a control structure. For doing that, the knowledge of the invariants of the process under feedback and factorization tools of linear local models were applied for designing a new reference model. The proposed techniques were used as a reference of what can or cannot be achieved by a particular system operating under the action of a feedback control law: minimum variance control (MVC), proportional-integral (PI), and model predictive control (MPC). Hence, the proposed performance indexes are the tools for the evaluation of the behavior of the control system during regular industrial operation, not only based upon the physical point of view, in which the reference is treated as a realizable system, but also from a theoretical point of view, in which the model factorization procedure takes place, and therefore allowing the extraction of the invariant part of a system, changing the system to its easiest controlled form description, and then serving as a reference model for the calculation of the performance index, with a low computing cost and preserving the importance of interactions and high frequency effects for balancing the reference behavior. In this work was investigated, with success, the effect of coupling, interactions and quality degradation of the control for SISO (Single-Input and Signle-Output) and MIMO (Multiple-Input and Multiple-Output) processes. The results show that the proposed technique are effective and a viable tool for process monitoring and controlled systems performance evaluation.