Model predictive control for nonlinear batch processes with asymptotically perfect tracking

Lee, Kwang Soon; Lee, Jay H.

doi:10.1016/s0098-1354(97)87612-0

Cited by 17 publications

(8 citation statements)

References 2 publications

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“…subject to initial conditions 1 (0) = 0.9 and 2 (0) = 307.16 and the nominal values of the batch reactor are the same as taken by [24,25]. Initially, the batch reactor is on the nominal values, and the goal is to control the reaction temperature without restrictions in .…”

Section: Numerical Examplesmentioning

confidence: 99%

Suboptimal Control Strategies for Finite-Time Nonlinear Processes with Input Constraints

Rivadeneira

Adam

2013

Journal of Nonlinear Dynamics

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Novel techniques for the optimization and control of finite-time processes in real-time are pursued. These are developed in the framework of the Hamiltonian optimal control. Two methods are designed. The first one constructs the reference control trajectory as an approximation of the optimal control via the Riccati equations in an adaptive fashion based on the solutions of a set of partial differential equations called the α and β matrices. These allow calculating the Riccati gain for a range of the duration of the process T and the final penalization S. The second method introduces input constraints to the general optimization formulation. The notions of linear matrix inequalities allow us to recuperate the Riccati gain as in the first method, but using an infinite horizon optimization method. Finally, the performance of the proposed strategies is illustrated through numerical simulations applied to a batch reactor and a penicillin fed-batch reactor.

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Section: Numerical Examplesmentioning

confidence: 99%

Suboptimal Control Strategies for Finite-Time Nonlinear Processes with Input Constraints

Rivadeneira

Adam

2013

Journal of Nonlinear Dynamics

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“…In order to evaluate the proposed controller performance, we consider first a linear system (Lee & Lee, 1997) given by G(s)=1/15s 2 + 8s + 1. The MPC parameters were tuned as Q = 1500 , R = 0.5 and T = 1.…”

Section: Ilustrative Examplesmentioning

confidence: 99%

“…Consider now a nonlinear-batch reactor where an exothermic and irreversible chemical reaction takes place, (Lee & Lee, 1997). The idea is to control the reactor temperature by manipulating the inlet coolant temperature.…”

Section: Ilustrative Examplesmentioning

confidence: 99%

“…In order to evaluate the proposed controller performance we assume a true and nominal process given by (Lee et al, 2000;Lee & Lee, 1997) G(s)=1/15s 2 + 8s + 1 and G(s)=0.8/12s 2 + 7s + 1, respectively. The sampling time adopted to develop the discrete state space model is T = 1 and the final batch time is given by T f = 90T.…”

Section: Ilustrative Examplesmentioning

confidence: 99%

“…Form the point of view of the learning procedure, any detected state or output disturbance is taken like parameters that are updated iteration to iteration. Then, in (Lee & Lee, 1997; and (Lee et al, 2000), a real-time feedback control is incorporated into the Q-ILC (BMPC). As the authors declare, some cares must be taken when combining ILC with MPC.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Iterative Learning - MPC: an Alternative Strategy

Adam¹,

González²

2012

Frontiers in Advanced Control Systems

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An adaptive controller–observer scheme for temperature control of non‐chain reactions in batch reactors

Caccavale

Iamarino

Pierri

et al. 2007

Adaptive Control & Signal

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In this paper an adaptive controller–observer temperature control scheme is developed for a class of irreversible non-chain reactions taking place in batch reactors. The scheme is based on a nonlinear observer for the estimation of the heat released by the reaction, where the heat transfer coefficient is adaptively estimated. Tracking of the desired reactor temperature is achieved via a two-loop control scheme, where an independent adaptive estimate of the heat transfer coefficient is used as well. Remarkably, the observer and the controller can be designed and tuned separately. The convergence of both the nonlinear observer and of the overall controller–observer scheme is analyzed by resorting to a Lyapunov-like argument. A comparative simulation case study is developed to test the performance of the proposed scheme and compare it with other approaches already known in the literature

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Model predictive control for nonlinear batch processes with asymptotically perfect tracking

Cited by 17 publications

References 2 publications

Suboptimal Control Strategies for Finite-Time Nonlinear Processes with Input Constraints

Suboptimal Control Strategies for Finite-Time Nonlinear Processes with Input Constraints

Iterative Learning - MPC: an Alternative Strategy

An adaptive controller–observer scheme for temperature control of non‐chain reactions in batch reactors

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