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This paper demonstrates the unique property of Model Reference Adaptive Controlled (MRAC) system. Reference Model is used in the MRAC control structure plays a major role for the transient characteristics of the output response of controlled plant. An inversion based Non-minimum phase (NMP) system always gives unbounded response, MRAC scheme not only stabilize this inverse NMP system, it also force the output trajectory to follow the path of the reference model. Lyapunov stability theory based control logic has been applied to obtain the required control parameters with adjustable gains.

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The state of the ampere-hour capacity of the battery depends on the condition of materials used in it. Large reduction of capacity ends with maintenance or replacement of the battery. Modern battery materials include application of nanomaterials and nanotechnology in various stages of production. This article attempts to monitor the capacity of battery used for vehicles which are made of different types of materials using switching transients. The analytical part was done using wavelet-based decompositions. Data sets of large number of coefficients have been developed for learning. Their statistical behaviour has been studied, and monitoring was initially carried out by some selective parameters. Then the artificial neural network-based algorithm was developed which includes all features of statistical variation for better monitoring. Case studies have been carried out followed by comparison. The study ends with a satisfactory monitoring.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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This paper presents a systematic formulation of control-oriented linear fractional transformation (LFT) modelling of the linear dynamical system, truly integrates the objective of control theory. A novel methodology has been introduced for modeling quality improvement to achieve certain performance specification considering the modeling uncertainties arising due to the difference between the mathematical model and the actual system and the presence of disturbance signal during the formulation of LFT framework. For the convenience of compact modelling, the generalized transfer function of the linear dynamical system has been represented into the LFT framework by incorporating the real parametric uncertainties enter rationally into the system modelling. The generalized LFT modeling algorithm is convenient to address the issues like identifiability and persistence of excitation for a huge class of system model structures can be accommodated because of its general nature. The proposed modelling algorithm has been applied to a benchmarked industrial mechatronics system, to verify the effectiveness of control theory.

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