Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

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 predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

Grey-Box Modeling and Decoupling Control of a Lab Setup of the Quadruple-Tank System

“…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.…”

“…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.…”

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

“…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).…”

“…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.…”

Model-Predictive Control for the Three-Tank System Utilizing an Industrial Automation System

“…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.…”