Stability analysis of adaptive control using fuzzy adapting rate neural emulator: Experimental validation on a thermal process

Abstract

In this study, an adaptive control based on fuzzy adapting rate for neural emulator of nonlinear systems having unknown dynamics is proposed. The indirect adaptive control scheme is composed by the neural emulator and the neural controller which are connected by an autonomous algorithm inspired from the real-time recurrent learning. In order to ensure stability and faster convergence, a neural controller adapting rate is established in the sense of the continuous Lyapunov stability method. Numerical simulations are included to illustrate the effectiveness of the proposed method. The performance of the proposed control strategy is also demonstrated through an experimental simulation.

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

“…In order to evaluate the performance of the proposed method, we calculate the NMSE values for considered methods. The obtained NMSE values are equal to 0.06 with conventional NC, 0.02 with the strategy suggested by Rhili et al, 2021 and 6.3.10 −4 with the proposed strategy.…”

“…The stability of the system can be guaranteed; however, we note a large tracking error between t = 200s and t = 660s. We conclude that the developed method is more efficient in terms of speed of convergence during all phases, in terms of precision and robustness during the tracking and regulation phases.

Figure 9.Evolution of the desired and system outputs (method Rhili et al, 2021).

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“…To evaluate the performance of the proposed algorithm, comparative studies with the method proposed in Rhili et al, 2021 are elaborated. The obtained results are illustrated in Figure 9.…”

“…In the initialization phase [0, 25 s ], this error takes low values with the developed method compared to conventional NC, and the decrease in the error can reach 57%. In the regulation phase [200 s , 400 s ], we note a diminution around 36% of the tracking relative error for the proposed method compared to the method suggested by (Rhili et al, 2021). With reference to the results, we can conclude that the proposed strategy is more efficient than the one used in (Rhili et al, 2021), in terms of precision, speed of convergence, and robustness during the tracking and regulation phases.…”

“…In this context, to avoid the problem linked to the Lyapunov constant parameter, authors in (Rhili et al, 2021) proposed a new NC adaptive rate ensuring stability and faster convergence in the sense of the continuous Lyapunov stability method. The main drawback of this method is the requirement of some stability conditions.…”