Optimal control for steel annealing processes as hybrid systems

Cho, Y.C.; Кwon, Wook Hyun; Cassandras, Christos G.

doi:10.1109/cdc.2000.912820

Cited by 12 publications

(10 citation statements)

References 5 publications

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“…The simulations for computation of optimal control for SAP as hybrid systems are carried out and the results are compared in different groups. The results by the best method in each group are then compared with those obtained through forward algorithm (Cho et al 2001(Cho et al , 2004. Obviously these comparisons prove the supremacy of the modified PSO algorithms, pf-PSO and ePSO methods over the other methods considered in this paper.…”

Section: Introductionmentioning

confidence: 92%

“…In this paper f (u) is considered as a monotonically increasing function of the optimal speed u. The arbitrary function f (u) is approximated as f (u) = 5.4u + 29 (Cho and Cassandras 2000). This approximated trajectory has been used in a real heating furnace model of a steel annealing process.…”

Section: Problem Formulation Of a Single Stage Steel Annealing Processesmentioning

confidence: 99%

“…This method is presented in Cho and Cassandras (2000), Cho et al (2004) and the results obtained through FA are included in Table 2. The specifications used for FA are maintained for all the other methods considered in Table 1 in order to compare the performance efficiently.…”

Section: Experimental Set Up Of Various Optimization Algorithmsmentioning

confidence: 99%

“…Although the number of such subproblems appears to be combinationally large in the number N of jobs to be completed, it was shown in Cho et al (2001) that only N such problems need to be solved and that the solution yields the global optimum under appropriate technical conditions on the objective function. This paper describes how the optimization algorithms such as genetic algorithm (GA) and particle swarm optimization algorithm (PSO) approaches can be adapted to steel annealing processes and includes some explicit numerical results and comparisons for a single furnace SAP (Cho and Cassandras 2000;Cho et al 2004).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms

Arumugam¹,

Chandramohan²,

Murthy

2011

Optim Eng

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7 This paper elucidates the computation of optimal controls for steel annealing processes as hybrid systems which comprise of one or more furnaces integrated with plant-wide planning and scheduling operations. A class of hybrid system is considered to capture the trade-off between metallurgical quality requirement and timely product delivery. Various optimization algorithms including particle swarm optimization algorithm (PSO) with time varying inertia weight methods, PSO with globally and locally tuned parameters (GLBest PSO), parameter free PSO (pf-PSO) and PSO like algorithm via extrapolation (ePSO), real coded genetic algorithm (RCGA) and two-phase hybrid real coded genetic algorithm (HRCGA) are considered to solve the optimal control problems for the steel annealing processes (SAP). The optimal solutions including optimal line speed, optimal cost, and job completion time and convergence rate obtained through all these optimization algorithms are compared with each other and also those obtained via the existing method, forward algorithm (FA). Various statistical analyses and analysis of variance (ANOVA) test and hypothesis t-test are carried out in order to compare the performance of each method in solving the optimal control problems of SAP. The comparative study of the performance of the various algorithms indicates that the PSO like algorithms, pf-PSO and ePSO are equally good and are also better than all the other optimization methods considered in this chapter.

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Section: Introductionmentioning

confidence: 92%

Section: Problem Formulation Of a Single Stage Steel Annealing Processesmentioning

confidence: 99%

Section: Experimental Set Up Of Various Optimization Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms

Arumugam¹,

Chandramohan²,

Murthy

2011

Optim Eng

View full text Add to dashboard Cite

show abstract

“…Many chemical processes include discontinuous actuators, physical constraints or manufacturing distinct phases such as, filling/emptying a reactor or heating/cooling a product (see for example [1][2][3][4][5]). Drastic, instantaneous changes in a continuous behavior of a process, caused by such factors, can be modelled more conveniently as discrete events.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of a CSTR with two arbitrarily switching modes using modal state feedback linearization

Yazdi

Jahed‐Motlagh

2009

Chemical Engineering Journal

175

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Forward decomposition algorithms for optimal control of a class of hybrid systems

Cho

Cassandras

Pepyne

2001

Intl J Robust & Nonlinear

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SUMMARYThis paper considers optimal control problems for a class of hybrid systems motivated by the structure of manufacturing environments that integrate process and operations control. We derive new necessary and su$cient conditions that allow us to determine the structure of the optimal sample path and hence decompose a large non-convex, non-di!erentiable problem into a set of smaller convex, constrained optimization problems. Using these conditions, we develop two e$cient, low-complexity, scalable algorithms for explicitly determining the optimal controls. Several numerical examples are included to illustrate the e$cacy of the proposed algorithms.

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Optimal control for steel annealing processes as hybrid systems

Cited by 12 publications

References 5 publications

On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms

On the optimal control of steel annealing processes via various versions of genetic and particle swarm optimization algorithms

Stabilization of a CSTR with two arbitrarily switching modes using modal state feedback linearization

Forward decomposition algorithms for optimal control of a class of hybrid systems

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