A continuous stirred tank heater simulation model with applications

Thornhill, Nina F.; Patwardhan, Sachin C.; Shah, Sirish L.

doi:10.1016/j.jprocont.2007.07.006

Cited by 193 publications

(110 citation statements)

References 36 publications

(32 reference statements)

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“…One possible extension by Kumar et al (2015) relies on formulating a multiple-input multiple-output linear system as a set of multiple-input single-output armax (Ljung, 1999) models and estimating parameters with an extended least-squares approach. The resulting controller is tested on a benchmark continuouslystirred tank heater system (Thornhill, Patwardhan, and Shah, 2008) with two tanks. The experimental results are promising, but much work remains on evaluating the performance of this approach for real-time control of multivariable systems and there are several theoretical aspects that require further investigation.…”

Section: Discussionmentioning

confidence: 99%

“…algorithm carried out using the benchmark Continuous Stirred Tank Heater (C.S.T.H.) system (Thornhill, Patwardhan, and Shah, 2008) at the Automation Lab in the Chemical Engineering Department at I.I.T. Bombay.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The efficacy of the proposed adaptive dual M.P.C. is evaluated through experimental studies on a heater-mixer system (Thornhill, Patwardhan, and Shah, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Experimental Evaluation of a MIMO Adaptive Dual MPC

Kumar¹,

Heirung²,

Patwardhan³

et al. 2015

IFAC-PapersOnLine

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Most decisions based on predictions from a model have uncertain outcomes. The uncertainty may be exogenous or endogenous to the modeled process, or both, and can greatly affect the degree to which the decision-maker's goals are met. In this thesis I study optimal control problems for systems with endogenous uncertainty that can be reduced by manipulating the system input. When the decision-maker can improve performance through actively reducing uncertainty ("active learning"), there is a dual nature to the optimal sequence of decisions; the decisions or inputs must direct the process toward the desired state and also ensure that information-rich data be generated so that decision-relevant uncertainty is resolved.The dual-control problem can be defined as that of minimizing the expected output errorwhere y * is the output reference, Y (t + N − 1 | t) represents future information up to time t + N − 1 in addition to all past information, and the model for the system output y is not fully known. My coworkers and I propose a novel reformulation technique for a probabilisticallyconstrained stochastic dual-control problem and show that the optimal strategy for minimizing this cost function involves active exploration of the plant to generate informative data. It is consequently necessary that the model that informs the decision-making include how future data resolve uncertainty. The reformulation permits practical algorithms for true dual control for a class of systems, allows new interpretation of earlier approaches, and may guide approximate dual-control designs where no exact results are obtainable. In addition to useful algorithms, this thesis contains a number of conceptual insights. In particular, the most recent results provide a foundation from which we argue that the conventional dual-control interpretation involving a trade-off between control and exploration is a false dichotomy. These derivations clearly show, for a specific system class, that control of the nominal model and a specific form of uncertainty reduction are both necessary components of the optimal control, vii as opposed to separate entities in which uncertainty reduction can be sacrificed for increased control performance.I consider the approaches to dual control presented in this thesis as falling into one of the following three categories: minimization of (i) a heuristic objective that is different from, yet still reduces, the dual objective ( * ); (ii) a systematic approximation of ( * ); and (iii) an exact reformulation of ( * ). This main contributions in this thesis are taken from the following three papers: Each of the three main papers introduce control designs that involve solving a finite-horizon optimal-control problem (O.C.P.) at every sampling instant, with the initial state set to the current state of the plant. Common to the algorithms is their foundation in model-predictive control (M.P.C.). The approaches each involve augmenting the standard O.C.P. in M.P.C. with cost-function terms and constraints, with the result that the c...

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Section: Discussionmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

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Experimental Evaluation of a MIMO Adaptive Dual MPC

Kumar¹,

Heirung²,

Patwardhan³

et al. 2015

IFAC-PapersOnLine

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“…Hot water and cold water are mixed in a stirred tank, heated by steam through a heating coil, and drained from the tank through a long pipe. A more detailed description of the CSTH model can be found in [31].…”

Section: Experiments Overviewmentioning

confidence: 99%

“…Under the closed-loop control, the CSTH model runs to a steady state from a nonsteady initial condition. The steady-state valve positions and instrument conditions in this experimental case are shown in Table 1 [31]. The simulation input and output represent electronic signals on 4-20 mA scale.…”

Section: The Csth Simulink Platform the Configuration Of The Csth Plmentioning

confidence: 99%

A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

Xie

Wang

2018

Journal of Electrical and Computer Engineering

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Research on stealthiness has become an important topic in the field of data integrity (DI) attacks. To construct stealthy DI attacks, a common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such assumption is relaxed and a covert agent is proposed based on the least squares support vector regression (LSSVR). By estimating a plant model from control and sensory data, the LSSVR-based covert agent can closely imitate the behavior of the physical plant. Then, the covert agent is used to construct a covert loop, which can keep the controller's input and output both stealthy over a finite time window. Experiments have been carried out to show the effectiveness of the proposed method.

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Fault‐tolerant control of linear multivariable controllers using iterative feedback tuning

Wang

2014

Adaptive Control & Signal

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This paper introduces the iterative feedback tuning (IFT) into a Youla parameterization scheme for fault-tolerant control. By off-line IFT-experiments of tuning Youla parameters, the proposed algorithm deals with a number of conditional failures that are described by the dual Youla parameter. The main contribution of this paper is to show how Youla scheme-based IFT can be constructed for multivariable linear timeinvariant systems. Particular attention is given to the issue of the structure of the Youla parameter (filter), in which both finite impulse response and infinite impulse response filters are presented and compared. As an illustration, the method is applied to a simulation model of a continuous stirred tank heater system. Copyright

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A continuous stirred tank heater simulation model with applications

Cited by 193 publications

References 36 publications

Experimental Evaluation of a MIMO Adaptive Dual MPC

Experimental Evaluation of a MIMO Adaptive Dual MPC

A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

Fault‐tolerant control of linear multivariable controllers using iterative feedback tuning

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