Abstract:Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of … Show more
“…This section presents three multivariable control strategies for water levels in the lower tanks, starting from the linear model in (25). The strategies highlight the interaction and multivariable zero RHP problems.…”
Section: Proposed Control Systems Using Decouplingmentioning
confidence: 99%
“…In one of the first studies on QTS [19], a decentralized PI control was designed for the QTS configured with a multivariable RHP zero. Other authors have applied internal model control [22], multivariable H ∝ control [3], quantitative feedback control [23], LQG optimal control [24], predictive control [25,26], and distributed model predictive control [27]. More recent works have applied nonlinear techniques to the QTS such as sliding mode control [28,29], feedback linearization [20], fuzzy control [30,31], and neural networks [32], among others.…”
The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This study outlines the practical grey-box modeling procedure conducted for the QTS at the University of Cordoba and provides guidance for its implementation. A configurable nonlinear model was developed and controlled in a closed loop using different controllers. Specifically, decentralized control, static decoupling control, and simplified decoupling control were compared. The simulation designs were experimentally validated with high accuracy, demonstrating that the conclusions reached with the developed model can be extrapolated to the real system. The comparison of these three control designs illustrates the advantages and disadvantages of decoupling in certain situations, especially in the presence of non-minimum phase zeros.
“…This section presents three multivariable control strategies for water levels in the lower tanks, starting from the linear model in (25). The strategies highlight the interaction and multivariable zero RHP problems.…”
Section: Proposed Control Systems Using Decouplingmentioning
confidence: 99%
“…In one of the first studies on QTS [19], a decentralized PI control was designed for the QTS configured with a multivariable RHP zero. Other authors have applied internal model control [22], multivariable H ∝ control [3], quantitative feedback control [23], LQG optimal control [24], predictive control [25,26], and distributed model predictive control [27]. More recent works have applied nonlinear techniques to the QTS such as sliding mode control [28,29], feedback linearization [20], fuzzy control [30,31], and neural networks [32], among others.…”
The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This study outlines the practical grey-box modeling procedure conducted for the QTS at the University of Cordoba and provides guidance for its implementation. A configurable nonlinear model was developed and controlled in a closed loop using different controllers. Specifically, decentralized control, static decoupling control, and simplified decoupling control were compared. The simulation designs were experimentally validated with high accuracy, demonstrating that the conclusions reached with the developed model can be extrapolated to the real system. The comparison of these three control designs illustrates the advantages and disadvantages of decoupling in certain situations, especially in the presence of non-minimum phase zeros.
“…The ARX structure effectively model various engineering and applied sciences problems such as time series prediction, pneumatic positioning system, wheeled robots, MIMO systems, and behavior modeling [ 78 , 79 , 80 , 81 , 82 ]. The block diagram of the ARX model is presented in Figure 2 , where and are polynomials with a degree and respectively, and given in (1) and (2).…”
Section: Arx Mathematical Modelmentioning
confidence: 99%
“…The autoregressive exogenous model (ARX) is used in different engineering problems such as time series data prediction [ 78 ], pneumatic positioning systems [ 79 ], wheeled robots [ 80 ], multiple-input–multiple-output (MIMO) systems [ 81 ], and human driving behavior modeling [ 82 ]. Various identification techniques were proposed for the parameter estimation of ARX.…”
In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.
“…It is observed that stiction embedding nonlinear MPC only can guarantee good performance in set-points tracking and also stiction compensation. Piñón et al 6 validate the multiple-input multiple-output adaptive predictive controller (MIMO-APC) with the two simulated processes: a quadrotor drone and the quadruple-tank process. The simulation shows excellent set-point tracking behavior in the quadruple tank, in comparison to that with the control strategies previously reported in the literature.…”
A three-tank process
has difficulty in controller design because
of nonlinear flow and interactions between tanks. This paper addresses
the design methodology of the model-predictive controller (MPC) for
the three-tank system. The control performance of the proposed MPC
controller is compared with the proportional plus integral (PI) controller
by both simulations and experiments on the real three-tank pilot with
the industrial ABB 800xA automation system. The MPC controller shows
a faster response for the two tanks: In the simulation, the settling
times are about 120 s for both tanks of the MPC controller. On the
other hand, the settling times for the PI controller are about 200
s for the first tank and 150 s for the second tank. The experiments
confirm these results.
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