Operator based fault detection and compensation design of an unknown multivariable tank process

Abstract

In this paper, robust nonlinear tracking control design and operator based fault detection design employing robust right coprime factorization for an uncertain heat exchange tank process are proposed. To prevent unexpected temperature variation and accident of heating an empty tank, liquid level in tank must be controlled simultaneously with the liquid tempera ture, where control is Multi-Input Multi-Output (MIMO) control. Therefore, first, nonlinear models of liquid temperature process and liquid level system are obtained. This liquid level model is the system assumed the fault occurrence, meanwhile those models have uncertainties and coupling effects. Second, operator based nonlinear feedback tracking control system are realized by using robust right coprime factorization corresponding to the MIMO process. Third, operator based fault detection and compensation design is proposed, and this design is inserted into the control system. Moreover, tracking controllers are designed to obtain desired tracking performance. Finally, the effectiveness of the proposed models and methods is confirmed by simulations.

Multivariable Fractional-Order Controller Design for a Nonlinear Dual-Tank Device

“…The heat transfer model is extended to a fractional dimension in this paper, which can represent the outflow of heat from the solid wall of TANK1 and the change in liquid temperature near the individual tank walls caused by the flow of liquid in the tank. A model expressing the amount of heat absorbed by the solid wall of the tank in fractional calculus is shown in Equations ( 22) and (23).…”

“…In a previous study, the heat exchange and liquid level processes of a dual-tank device were modeled using integer calculus and operator theory to control the liquid temperature and liquid level, respectively, [23]. In that study, a multi-input multi-output control system was designed to perform single-input single-output target-value tracking for the heat exchange process in a dual-tank device and then perform target-value tracking for the liquid level process after the target-value tracking is stabilized.…”

Nonlinear Intelligent Control of Two Link Robot Arm by Considering Human Voluntary Components

“…Therefore, in this research, we use Support Vector Regression (SVR) [14][15][16][17][18], which is a kind of machine learning methods, to design the feedforward controller to reduce the modeling error and improve the tracking performance. The control system is designed based on operator theory [19][20][21][22][23][24] to compensate for the interference and uncertainty inside the controlled object that exist when controlling the robot arm. Finally, in order to confirm the effectiveness of the proposed control system, we conduct an actual machine experiment and verify its effectiveness.…”