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.
Highlights
The industrial process control sector has undergone a significant change in recent years with the incorporation of more complex, faster and multivariable processes
The flight movement and speed can be changed by varying the speeds of each independent blade, giving the drone six degrees of freedom (DoF)
This paper showed a formulation of a novel MIMO-APC approach for MIMO-ARX
Summary
Caruntu and Cosmin CopotThe industrial process control sector has undergone a significant change in recent years with the incorporation of more complex, faster and multivariable processes. It is necessary to incorporate advanced control techniques that allow these processes to be controlled. Model predictive control is becoming one of the most popular advanced control technique and has been use for more than four decades. This control strategy is based on numerical optimization [1]. This strategy considers the future values of a variable based on existing information on the process and the use of the explicit form of a mathematical model of the process’ internal dynamics, which is used to predict the evolution of the controlled variables over a prediction’s time horizon defined by the user. If the predictive model is able to predict the behavior of the system, the variable under control will match the desired variable [2]
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