The proposed approach is validated using twin rotor MIMO system and simulation results demonstrate the efficacy of here proposed while preserving the system stabilizability. MOPSO guarantees the appropriate selection of the fuzzy membership functions. This paper proposes a fuzzy-based L1feedback filter design tuned with multiobjective particle swarm optimization (MOPSO) to remove these bottlenecks. For MIMO systems, the parameters tuning is challenging and requires multi-objective performance indices to avoid instability. Thus appropriate tuning of the filter's parameters is crucial to achieving optimal performance. The feedback filter provides performance that trades off fast closed-loop dynamics, robustness margin, and control signal range. This paper proposes an efficient approach for tuning L1-feedback filter of the adaptive controller for multi-input-multi-output (MIMO) systems. In addition to the analysis of the closed‐loop system stability using methods from the Lyapunov theory, our findings are also illustrated through numerical examples. With this command governor‐based model reference adaptive control architecture, the tracking error of the selected states can be made arbitrarily small by judiciously tuning the design parameters. Moreover, using the unmatched uncertainty approximation obtained through radial basis function neural networks, the command governor signal is designed to achieve the desired command following performance of the user‐defined subset of the accessible states. Specifically, the matched uncertainty is identified and its effect upon the system behavior is entirely attenuated. In our proposed solution, online least‐squares solutions for the matched and unmatched parameters are obtained through integration method and they are employed in the adaptive control framework. In this paper, we propose a command governor‐based adaptive control architecture for stabilizing uncertain dynamical systems with not only matched but also unmatched uncertainties and achieving the desired command following performance of a user‐defined subset of the accessible states.
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