Control of nonisothermal CSTR with time varying parameters via dynamic neural network control (DNNC)

Nikravesh, Masoud; Farell, A.E.; Stanford, Thomas G.

doi:10.1016/s1385-8947(99)00108-4

Cited by 58 publications

(36 citation statements)

References 28 publications

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“…CSTR is an industrial process plant that introduces the opportunity for a diverse range of process dynamics [10], [11]. The objective of control is to manipulate the jacket temperature so it keeps the temperature of the system at the desired level.…”

Section: Dynamic Model Of Non-isothermal Reactormentioning

confidence: 99%

“…The CSTR was chosen for this case study because the dynamic behavior of the CSTR has been studied extensively and it is well known to exhibit strong parametric sensitivity [11], [12]. More importantly, the CSTR model has become one of the standard test applications for theoretical results in the area of nonlinear control [11], [12].…”

Section: Dynamic Model Of Non-isothermal Reactormentioning

confidence: 99%

“…More importantly, the CSTR model has become one of the standard test applications for theoretical results in the area of nonlinear control [11], [12].…”

Section: Dynamic Model Of Non-isothermal Reactormentioning

confidence: 99%

See 2 more Smart Citations

Controller Design for Non-Isothermal Reactor Based on Imperialist Competitive Algorithm

Yazdani¹,

Movahed²,

MahmoudZadeh³

2013

IJCTE

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Abstract-Due to its nonlinearity and time-varying properties, control of chemical processes has been an interesting and challenging subject for researchers. Obviously, designing a controller in chemical environments is a multi-dimensional task. Not only the controller must have an acceptable accuracy, but it should offer also the capability to surmount the functional variation occurred in the plant. Although PID controller is considered as one of the most popular option, however the problem of the PID gains tuning must be taken into account. Therefore, to achieve appropriate results, the optimal tuning of PID gains is required. This paper introduces an optimal fuzzy approach emerged by using a socio-political optimization algorithm, imperialist competitive algorithm (ICA), for online tuning of PI controller settled for temperature control of a non-isothermal reactor. Fuzzy gain scheduling method handles the duty of PI controller gains tuning to form an intelligent controller. The proposed controller has two inputs which are error and derivative of error and its outputs are proportional and integral gain. The controller is updated continuously to track the changes occurred in the process. To obtain the high performance control, ICA has been employed to determine the optimum membership functions of the input and output variables. The performance criterion has been chosen so that to minimize the sum of square error. In addition, conventional PI controller and classic online PI controller have been applied to the system for the performance comparison. The results indicate that the proposed method is more flexible than other recommended approaches in presence of system variations and offering an outstanding performance.Index Terms-Fuzzy controller, imperialist competitive algorithm (ICA), gain scheduling.

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Section: Dynamic Model Of Non-isothermal Reactormentioning

confidence: 99%

Section: Dynamic Model Of Non-isothermal Reactormentioning

confidence: 99%

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Controller Design for Non-Isothermal Reactor Based on Imperialist Competitive Algorithm

Yazdani¹,

Movahed²,

MahmoudZadeh³

2013

IJCTE

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“…The movement of a colony towards the imperialist is shown in (11). (11) where β and γ are arbitrary numbers that modify the area that colonies randomly search around the imperialist.…”

Section: International Journal On Smart Sensing and Intelligent Systementioning

confidence: 99%

Temperature Control of a Continuous Stirred Tank Reactor by Means of two Different Intelligent Strategies

Rahmat

Yazdani

Movahed

et al. 2011

International Journal on Smart Sensing and Intelligent Systems

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“…The mathematical model equations are obtained by a component mass balance (1) and energy balance principle (2) in the reactor. The mathematical model of CSTR is [11], [12], [13]:…”

Section: Dynamic Model Of Cstrmentioning

confidence: 99%

Applying a Novel Fuzzy-PI Controller on the Model of Continuous Stirred Tank Reactor

Yazdani¹,

Movahed²,

MahmoudZadeh³

2011

IJMLC

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Abstract-Continuous Stirred Tank Reactor (CSTR)presents a significant role in the most of chemical process industries. The inherent nonlinear dynamic of CSTR results in various difficulties of stabilizing and controlling of the process. Likewise, functional changes with elapsing of the time and different disturbances associated with real environment are the other hints which must put into perspective. In this paper, a novel strategy based on the combination of fuzzy gain scheduling and a socio-political optimization algorithm, imperialist competitive algorithm, has been employed to design an intelligent Fuzzy-PI controller so that it surmounts the problem of system's parameter variation throughout the controlling of CSTR temperature. The controller has two inputs which are error and derivative of error and two outputs that are the proportional gain and integral gain. The controller is updated continuously to track the changes which are occurred in the system. To obtain the high performance control, imperialist competitive algorithm was employed to determine the optimum membership functions of the input and output variables. The performance of the proposed controller has been consider based on the Sum of the Square Error (SSE) and Integral Absolute Error(IAE) Criteria. In addition, conventional PI controller and classic online PI controller were applied to the system for the performance comparison. The results clearly indicate the robustness of Fuzzy-PI controller in dealing with uncertainties throughout the tracking of the reference signal. Fuzzy-PI controller also embodies an outstanding accuracy of tracking which leads to the least tracking error criterion.

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Control of nonisothermal CSTR with time varying parameters via dynamic neural network control (DNNC)

Cited by 58 publications

References 28 publications

Controller Design for Non-Isothermal Reactor Based on Imperialist Competitive Algorithm

Controller Design for Non-Isothermal Reactor Based on Imperialist Competitive Algorithm

Temperature Control of a Continuous Stirred Tank Reactor by Means of two Different Intelligent Strategies

Applying a Novel Fuzzy-PI Controller on the Model of Continuous Stirred Tank Reactor

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