Neural networks based adaptive control for a class of time varying nonlinear processes

Petre, Emil; Selişteanu, Dan; Şendrescu, Dorin

doi:10.1109/iccas.2008.4694355

Cited by 10 publications

(19 citation statements)

References 17 publications

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“…Figure 3 shows different peaks of overshoot at k = 100 and k = 200, which seems odd at first glance. One reason for different peaks at these two instants is because of the non-linearity at the plant input, which is lumped together for the two inputs, as given in equations (12) and (13). Hence, the effect of control signal u 2 (k) will be on both outputs y 1 (k) and y 2 (k).…”

Section: Minimum-phase Systemmentioning

confidence: 96%

A robust controller for non-linear MIMO systems using radial basis function neural network

Al-Duwaish

Rizvi

2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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This paper presents a controller design method for multi-input multi-output nonlinear systems using the artificial neural network. The designed controller uses radial basis function networks to generate optimal control signals abiding by constraints, if any, on the control signal, or on the system output. The salient features of the proposed controller include: no requirement of the explicit knowledge of the states of the system, no requirement of a priori knowledge of the non-linear model of the system, and ability of the controller to control time-varying systems or systems having non-minimum-phase characteristics. A recursive algorithm is developed to update the weights of the neural network. Non-linearities in the system as well as variations in system parameters are compensated by the neural network. Simulation results for non-linear systems are included at the end, and controller performance is analysed. A practical case study of single area non-linear automatic generation control is also considered to test the performance of the controller.

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Section: Minimum-phase Systemmentioning

confidence: 96%

A robust controller for non-linear MIMO systems using radial basis function neural network

Al-Duwaish

Rizvi

2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The model (1) describes in fact the behaviour of an entire class of bioprocesses and is referred to as the general dynamical state-space model of this class [12,13,17,18]. In (1), K Á u(x) represents the reaction kinetics, and the term -Dx ?…”

Section: Dynamical Model Of a Class Of Bioprocessesmentioning

confidence: 99%

“…Typical bioprocesses belonging to this class are the activated sludge bioprocess, the biomethanization process, the production of some enzymes, etc. [13,15,17,18]. The control objective is to make the output y of (2) to track a specified trajectory y ref .…”

Section: Remarkmentioning

confidence: 99%

“…There are many applications of NN adaptive control, such as robotics [19], chemical processes [20], including some applications for bioprocesses [17,[21][22][23][24]. However, most of these studies are focussed on NNs-based identification and on-line estimation algorithms for bioprocesses.…”

Section: Introductionmentioning

confidence: 99%

“…Another difficulty lies in the absence, in most cases, of cheap and reliable instrumentation. In order to overcome these difficulties several strategies were developed, such as exactly linearizing strategy [12,14,15], adaptive approach [12,13], optimal control (especially for fed-batch bioprocesses) [12], fuzzy and neural strategies [16,17], sliding mode control [18] and so on. The exactly linearizing control does not work when the kinetics are imprecisely known because the exact cancellation of the nonlinearities would not be possible.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural networks-based adaptive control for a class of nonlinear bioprocesses

Petre

Selişteanu

Şendrescu

et al. 2009

Neural Comput & Applic

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The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and timevarying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input-output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.

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Robust‐Adaptive Control Strategies for a Time Delay Bioelectrochemical Process Using Interval Observers

Petre¹,

Tebbani²,

Selişteanu³

2014

Asian Journal of Control

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International audienceThe paper addresses the design and the analysis of adaptive and robust-adaptive control strategies for a complex recycled wastewater treatment bioprocess. The design procedures are developed under the realistic assumptions that the bacterial growth rates are unknown and the influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately (asymptotic or interval based) state observer and with a parameter estimator used for on-line estimation of unknown kinetics. These approaches are applied to a complex time delay bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor. Numerical simulations are performed in order to validate the proposed algorithms

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Neural networks based adaptive control for a class of time varying nonlinear processes

Cited by 10 publications

References 17 publications

A robust controller for non-linear MIMO systems using radial basis function neural network

A robust controller for non-linear MIMO systems using radial basis function neural network

Neural networks-based adaptive control for a class of nonlinear bioprocesses

Robust‐Adaptive Control Strategies for a Time Delay Bioelectrochemical Process Using Interval Observers

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