Abstract
Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required, for example in cascade control loops or supervisory Model Predictive Control (MPC) applications. To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced. This simple controller can maintain linear performance over the full operating range of a uniform tank. As will be demonstrated, the implementation overhead compared to a regular PI controller is negligible, making it ideal for industrial implementation. Implementation details and parameter identification for adaptive implementation are discussed. Simulations coupled with experimental results using a large-scale laboratory level control system using commercial industrial control equipment validate the approach, and illustrate its effectiveness for both level tracking and disturbance rejection.
Highlights
To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced
A very accurate technique based around feedback linearization and PI control is employed, in order to create a simple controller which can maintain linear performance over the full operating range of a uniform tank
The focus of this paper has been upon accurate liquid level control in single tank systems which can be actuated continuously, and modulation of the level setpoint is required
Summary
Tank level control is one of the most commonly encountered applications in industry [1] [2] [3]. Accurate level control of plant where modulation of the level setpoint is required is considered; such situations commonly arise in cascade control loops [4], for example, or situations in which supervisory Model Predictive Control (MPC) is implemented [5]. The linear approximation is accurate around the chosen operating point, if the system is perturbed away from this operating point (for example due to a disturbance or setpoint change), performance deteriorates rapidly This arises since the linear approximation employed to tune the PI controller becomes less accurate due to non-linearity, and a fixed gain controller is not able to adapt or re-schedule its gains to match the new process conditions. The focus of this research is to develop a simple but effective solution to this issue
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