Adaptive control design using stability analysis and tracking errors dynamics for nonlinear square MIMO systems

Atig, Asma; Druaux, Fabrice; Lefebvre, Dimitri; Abderrahim, Kamel; Abdennour, Ridha Ben

doi:10.1016/j.engappai.2011.08.002

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…In this section, numerical simulations are carried out to evaluate the performance of the NE and NC adaptive rates selection using MOPSO algorithm. Let us consider the nonlinear system defined by the following equation (Atig et al, 2012)…”

Section: Simulation Examplementioning

confidence: 99%

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

Hamza,

Zribi,

Farhat

2024

Transactions of the Institute of Measurement and Control

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This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

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Section: Simulation Examplementioning

confidence: 99%

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

Hamza,

Zribi,

Farhat

2024

Transactions of the Institute of Measurement and Control

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“…The estimated tracking errors variations are given bywhere dy^l(t)dt can be described as follows (Atig et al, 2012a)…”

Section: Stability Analysismentioning

confidence: 99%

“…With the proposed strategy, closed-loop performances are improved; however, it still depends entirely on the starting parameter. Based on the exponential stability approach and to improve the closed-loop performances, authors in (Atig et al, 2012a) proposed a stable adaptive control design using Lyapunov stability analysis. The main drawback of this method is that the desired performances depend on the choice of a Lyapunov constant parameter.…”

Section: Introductionmentioning

confidence: 99%

Enhancement neural control scheme performance using PSO adaptive rate: Experimentation on a transesterification reactor

Yassin

Zribi

Atig

et al. 2022

Journal of Vibration and Control

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An arbitrary choice of the neural controller adaptive rate can have a negative effect on the performance of the closed-loop system. In this study, we propose a novel methodology for neural controller adaptive rate using Particle Swarm Optimization algorithm. The developed control scheme is composed of a recurrent neural networks emulator and controller with decoupled adaptive rates. Constraints on the adaptive rate are derived from the Lyapunov stability method. Particle Swarm Optimization is proposed as a mechanism to optimize the adaptive rate of the NC to improve the closed-loop performances. The advantages of the proposed new control algorithm are as follows: (1) online optimal choice of adaptive rate, which reduces the effort for searching an adequate neural controller adaptive rate when considering the conventional methods and (2) ensuring stability, faster convergence, disturbance rejection, and good tracking. The efficiency of the proposed PSO adaptive rate is demonstrated with numerical control of SISO nonlinear system. The obtained results prove the efficiency of the proposed NC compared to those obtained with existing methods. An application of the developed approach on a semi-batch reactor is presented to validate simulations results.

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“…Moreover, this method requires a very high number of measurements. Another solution based on recurrent neural networks approach is proposed in Atig et al (2010) Atig et al (2012) to represent this reactor. The neural networks are capable of approximating any continuous nonlinear functions and have been applied to nonlinear process emulation.…”

Section: Introductionmentioning

confidence: 99%

“…, the multimodel methodMihoub et al (2009b)Ltaief et al (2004and the neural emulator methodAtig et al (2012). The system's output of every approach is presented inFig.7.…”

mentioning

confidence: 99%

A semi-batch reactor modeling based on PWARX systems

Abderrahim¹,

Lassoued

2014

IFAC Proceedings Volumes

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Adaptive control design using stability analysis and tracking errors dynamics for nonlinear square MIMO systems

Cited by 14 publications

References 19 publications

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

Enhancement neural control scheme performance using PSO adaptive rate: Experimentation on a transesterification reactor

A semi-batch reactor modeling based on PWARX systems

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