RESUMEN
Feedforward control of measurable disturbances is a useful complement to feedback control because feedforward control performs control actions before a disturbance response occurs in a process output. In contrast to the typical model-based design approach, a novel data-based method for designing both the feedback and feedforward controllers is presented in this paper. The controller design directly exploits closed-loop plant data and does not require an identification of process and disturbance models. A feedback proportional-integral-derivative controller and feedforward lead-lag compensator are sequentially designed on the basis of the closed-loop response data for a set-point change and for a disturbance input, respectively. Because the controller design process uses plant data integrals, the proposed method is robust against measurement noise. Moreover, the proposed design method can be applied to improve existing underperforming feedback and feedforward controllers using routine closed-loop operating data. Simulation studies demonstrated that the proposed method outperforms existing methods in designing a feedback-feedforward control system.
RESUMEN
A systematic data-based design method for tuning proportional-integral-derivative (PID) controllers for disturbance attenuation is proposed. In this method, a set of closed-loop plant data are directly exploited without using a process model. PID controller parameters for a control system that behaves as closely as possible to the reference model for disturbance rejection are derived. Two algorithms are developed to calculate the PID parameters. One algorithm determines the optimal time delay in the reference model by solving an optimization problem, whereas the other algorithm avoids the nonlinear optimization by using a simple approximation for the time delay term, enabling derivation of analytical PID tuning formulas. Because plant data integrals are used in the regression equations for calculating PID parameters, the two proposed algorithms are robust against measurement noises. Moreover, the controller tuning involves an adjustable design parameter that enables the user to achieve a trade-off between performance and robustness. Because of its closed-loop tuning capability, the proposed method can be applied online to improve (retune) existing underperforming controllers for stable, integrating, and unstable plants. Simulation examples covering a wide variety of process dynamics, including two examples related to reactor systems, are presented to demonstrate the effectiveness of the proposed tuning method.
RESUMEN
Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology.