Adaptive input-output linearizing control of a continuous stirred tank reactor

Iyer, Natarajan Mahadeva; Farell, A.E.

doi:10.1016/0098-1354(94)00085-3

“…In the context of linearizable systems, Sastry and Isidori [SI89] used parameter adaptation approach for improving the control of nonlinear processes. This approach was used by Iyer and Farell [IF95] to improve the performance of input-output control of one reactor. In a parallel study, Huberman and Lumer [HL90] introduced a simple adaptive control mechanism into nonlinear systems where a parameter in the system is updated using the difference between setpoint and output, and its derivatives.…”

Section: Unmeasured Disturbances and Their Modellingmentioning

confidence: 99%

Echtzeit-Implementierung von NMPC

Löw¹,

Obradović

²

2018

atp

1

0

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Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

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“…In the context of linearizable systems, Sastry and Isidori [SI89] used parameter adaptation approach for improving the control of nonlinear processes. This approach was used by Iyer and Farell [IF95] to improve the performance of input-output control of one reactor. In a parallel study, Huberman and Lumer [HL90] introduced a simple adaptive control mechanism into nonlinear systems where a parameter in the system is updated using the difference between setpoint and output, and its derivatives.…”

Section: Unmeasured Disturbances and Their Modellingmentioning

confidence: 99%

Echtzeit-Implementierung von NMPC

Löw¹,

Obradović

²

2018

atp

1

0

View full text Add to dashboard Cite

Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

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“…Adaptive control was proposed as a way of automatically adjusting the controller parameters in the face of changing aircraft dynamics. While the progress in this area was initially very slow, during the past decade, adaptive control theory has made significant advances and many applications have been reported in chemical engineering, mechanical engineering, robotics, bioengineering, and so on. − Control of such processes has also been studied in the context of robust control of processes with time-varying uncertain variables. , …”

Section: Introductionmentioning

confidence: 99%

“…Adaptive temperature control of stirred tank chemical reactors has been studied extensively. ,,− ,, The control methods proposed and/or used all calculate the manipulated input on the basis of estimates of rate of heat production by reactions and/or the overall heat-transfer coefficient. To obtain the estimates, Regnier et al., Ni, Wright and Kravaris, and Defaye et al used very simple models such random walk, Clarke-Pringle and MacGregor 11 used simple models, and Soroush and Kravaris used very detailed models of the parameters.…”

Section: Introductionmentioning

confidence: 99%

“…While the progress in this area was initially very slow, during the past decade, adaptive control theory has made significant advances and many applications have been reported in chemical engineering, mechanical engineering, robotics, bioengineering, and so on. [3][4][5][6][7][8][9][10][11][12] Control of such processes has also been studied in the context of robust control of processes with time-varying uncertain variables. 13,14 For tight temperature control and reliable monitoring of extremely exothermic chemical reactors, such as bulk polymerization reactors, accurate information on the reaction-heat-production rate and the overall heattransfer coefficient of the reactor/cooling equipment is essential.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Temperature Control of Multiproduct Jacketed Reactors

Tyner

¹

,

Soroush

²

,

Grady

³

1999

Ind. Eng. Chem. Res.

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This paper presents a new adaptive cascade temperature control system for jacketed stirred tank reactors in which multiple products are produced. The adaptive control system consists of two cascade input−output linearizing controllers and an inversion-based parameter estimator that estimates the rate of heat production by reactions and the overall jacket−reactor heat-transfer coefficient. Without a need for retuning, it can provide tight temperature control in highly exothermic, multiproduct, jacketed, stirred-tank reactors in which the reaction rate and the overall heat-transfer coefficient are unknown and vary significantly. The application and performance of the adaptive cascade control system are shown and compared with those of a proportional-integral (PI) cascade control system by an exothermic chemical reactor with varying overall heat-transfer coefficient. Compared to a PI cascade control system, the adaptive cascade control system exhibits better performance and is much easier to tune.

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Adaptive input-output linearizing control of a continuous stirred tank reactor

Cited by 9 publications

References 5 publications

Echtzeit-Implementierung von NMPC

Echtzeit-Implementierung von NMPC

Echtzeit-Implementierung von NMPC

Adaptive Temperature Control of Multiproduct Jacketed Reactors

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