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A multi-objective criterion and stability analysis for neural adaptive control of nonlinear MIMO systems: an experimental validation
Abstract
This paper presents a multi-objective indirect neural adaptive control design for nonlinear square multi-variable systems with unknown dynamics. The control scheme is made of an adaptive instantaneous neural emulator, a neural controller based on fully connected real-time recurrent learning (RTRL) networks and an online parameter updating law. A multi-objective criterion that takes into account the minimisation of the control energy is considered. The contribution of this paper is to develop a new controller parameter optimisation based on the Lyapunov stability analysis while ensuring control issues with environmental and economical objectives. Performance of the proposed approach in terms of regulation, tracking and minimisation of the control energy is evaluated by numerical simulations of a disturbed nonlinear multi-variable system. The obtained control scheme is then applied in real time to a disturbed MIMO thermal process.
... Among these algorithms, we can mention the Dynamic Back Propagation algorithm [9], and the Real-Time Recurrent Learning RTRL algorithm [10,11]. These emulators have been successfully applied for nonlinear system emulation. ...
Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (PSO) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to accelerate the convergence speed and to improve the precision degree. The obtained results are compared with those reached with an intelligent tuning strategy. An experimental validation of the new emulator adaptation is carried on chemical reactor. Efficiency of the proposed method is proved according to the obtained performances.
... During the last two decades, the problem of stability analysis for T-S (Takagi and Sugeno, 1985) fuzzy systems has been the subject of considerable research efforts due to their practical applications (Feng and Zheng, 2016;Idrissi et al., 2017;Lian et al., 2017;Elleuch et al., 2018;Er Rachid et al., 2019;Maity et al., 2022). The T-S model has been shown to provide a powerful tool to approximate complex nonlinear systems (Pourhashemi et al., 2019;Naamane and Tissir, 2020;Atig et al., 2022). It combines flexible fuzzy logic theory and fruitful linear system theory into a unified framework to represent the complex nonlinear systems by using fuzzy sets and the membership functions Naamane et al., 2017;Sevgen, 2019). ...
This paper deals with the problem of robust stability analysis for uncertain Takagi-Sugeno (T-S) fuzzy systems with time varying delays. The approach is based on constructing an appropriate Lyapunov-Krasovskii functional (LKF) combined with state vector augmentation by using the delay-partitioning idea. By dividing uniformly the delay interval into N segments, with N as a positive integer, the LKF is chosen with different weighted matrices corresponding to different segments. The parametric uncertainties are assumed to be norm bounded. On this basis, new sufficient conditions ensuring the robust stability are expressed in terms of linear matrix inequalities (LMIs). Furthermore, the Finsler’s lemma and the Seuret-Wirtinger’s based integral inequality approach have been employed to derive less conservative results. Finally, numerical examples are provided to illustrate the effectiveness and the merit of the obtained results, compared with some previous works in the literature.
Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to solve the problem a multi-objective optimization that considers the minimization of the control energy can be adopted. In this paper, we propose to use a multi-objective indirect neural adaptive control concept for nonlinear systems with unknown dynamics. The control scheme consists of an adaptive instantaneous neural emulator and neural controller built on fully connected real-time recurrent learning networks (RTRL). The multi-objective particle swarm optimization (MOPSO) algorithm is used as a mechanism to find NE and NC adaptive learning rates that optimize the proposed objective functions: control energy minimization and closed-loop preservation. Comparative studies and experimental validation on a semi-batch reactor, used to generate cleaner biofuels, are performed to confirm the effectiveness of the proposed strategy.
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