Fuzzy Control System Performance Enhancement by Iterative Learning Control

Robust stability analysis for an enhanced ILC-based PI controller

“…Based on the continuous model in (30), a simplified linear discrete-time model for the three-tank system (26) can be obtained as…”

“…In the sequel, ILC controllers will be designed for the nonlinear system (26) based on the linear model (32). In other words, there is considerably large mismatch between the plant and the model.…”

“…A smaller ATE(k) value indicates a better tracking performance in the kth batch. First, it is assumed that there is no disturbance, i.e., w ≡ 0 in (26), and also no constraint on the manipulated variables. In this case, the output curves under the proposed ILC-based PI control are shown in Fig.…”

“…The current hot issues for the direct ILC might be dealing with uncertainties, nonlinearities, and constraints [25][26][27][28]. In contrast, the closed-loop stability and robustness issues for the indirect ILC are still open problems, even if the controlled plant is linear.…”

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

“…The study and selection of the most viable method from among the many offered by the myriad topics mentioned above is not straightforward. One way is to exploit the advantages of neurofuzzy systems for combining the semantic transparency and intrinsic robustness of fuzzy systems (Precup et al, 2008) with the learning ability of artificial neural networks.…”