Model based control of a four-tank system

Gatzke, Edward P.; Meadows, Edward S.; Wang, Chung; Doyle, Francis J.

doi:10.1016/s0098-1354(00)00555-x

Cited by 126 publications

(68 citation statements)

References 2 publications

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“…the voltages of the two pumps V p1 (t) and V p2 (t) (expressed in Volts). There is a strong coupling effect between the inputs and the outputs and the current setup exhibits non-minimum phase dynamics; a benchmark that has been investigated by several groups (Johansson (2000); Gatzke et al (2000); Vadigepalli et al (2001)). Autotuning of the PID controller via the MM algorithm described in section 2 has been performed using a relay test on the real plant for each loop, giving a robustness specification of 0.7 (i.e.…”

Section: Resultsmentioning

confidence: 99%

Robust PID Auto-tuning for the Quadruple Tank System

Ionescu¹,

Maxim²,

Copot³

et al. 2016

IFAC-PapersOnLine

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In multi-modular process architectures with independent but interacting subsystems, identification may not be the first choice at hand for closed loop control. A robust relay-based PID autotuning strategy is presented and validated on a quadruple tank system with non-minimum phase dynamics. The controller ensures a specified closed loop robustness, which is of great benefit to the overall performance. The experimental results suggest that the proposed method fulfils the robustness requirement and performs well in various operating conditions of the testbench.

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Section: Resultsmentioning

confidence: 99%

Robust PID Auto-tuning for the Quadruple Tank System

Ionescu¹,

Maxim²,

Copot³

et al. 2016

IFAC-PapersOnLine

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“…It includes level control of a four-tank laboratory system (Gatzke et al, 2000). This nonlinear system consists of four-tanks T 1 , T 2 T 3 and T 4 with the cross-sectional area A i , i = 1, .…”

Section: Examplementioning

confidence: 99%

A Novel Method for the Design of Switching Surfaces for Discretized MIMO Nonlinear Systems

Luis-Delgado

Al‐Hadithi

Jiménez

2017

International Journal of Applied Mathematics and Computer Science

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Designing variable structure control with sliding mode (VSC-SM) control schemes needs a switching function or a sliding surface which guarantees the global stability of the closed-loop system. Despite the fact that a wide range of design approaches has been proposed for solving this mathematical problem, the number of proposed methodologies for nonlinear systems is not very extensive, especially for discrete time nonlinear MIMO systems, and most of them require some coordinate system transformation. Therefore, it is not an easy task to find a design scheme that can be applied to discrete time nonlinear MIMO systems. The proposed methodology introduces a mathematical tool: a switching surface equation for a class of MIMO nonlinear systems through an explicit equation without any coordinate transformation. This equation makes use of an implicit linearizing process via the Taylor expansion that allows the use of linear procedures for the design of switching surfaces and the forward Euler method to obtain a discrete time dynamics representation. An illustrative example is included to show the advantages of the proposed design methodology.

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“…Actual laboratory plants as well as simulated versions of this benchmark have been used in several contributions, like for instance Gatzke et al (2000), and Mercangöz and Doyle (2007). Figure 1 depicts schematically the 4-tank process used in this study.…”

Section: Application To the Four-tank Benchmark Control Problemmentioning

confidence: 99%

A mixed-integer model predictive control formulation for linear systems

Moro¹,

Grossmann²

2013

Computers & Chemical Engineering

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Most industrial model predictive controllers (MPC) use the traditional two-layer structure developed in the early 1980's, where the upper layer defines optimal steady-state targets for inputs and outputs, while the lower layer calculates the control moves that drive the system towards these steady-state targets. Typically both layers use continuous quadratic programming (QP) formulations to derive the optimal solutions. On the other hand, advances in mixed-integer programming (MIP) algorithms and their successful application to solve large scheduling problems in reasonable time show that MIP formulations have the potential of being applied advantageously to the multivariable model predictive control problem. In this paper, we present mixed-integer quadratic programming (MIQP) formulations for both layers and show that several difficulties faced in the MPC practical implementation can be overcome with this approach. In particular, it is possible to set explicit priorities for inputs and outputs, define minimum moves to overcome hysteresis, and deal with digital or integer inputs.The proposed formulation is applied to 2 benchmark problems and to a simulated industrial system and the results compared with those achieved by a traditional continuous MPC. The solutions of the MIQP problems are derived by a computer implementation of the Outer Approximation method (OA) also developed as part of this work.

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Model based control of a four-tank system

Cited by 126 publications

References 2 publications

Robust PID Auto-tuning for the Quadruple Tank System

Robust PID Auto-tuning for the Quadruple Tank System

A Novel Method for the Design of Switching Surfaces for Discretized MIMO Nonlinear Systems

A mixed-integer model predictive control formulation for linear systems

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