A novel neuro‐adaptive control scheme is proposed in the context of angular velocity tracking in DC–DC buck converter driven permanent magnet DC motor system. The controller builds upon the idea of backstepping and consists of a fast single hidden layer Hermite neural network (HNN) module equipped with on‐board (adaptive) learning to counteract the unknown non‐linear time‐varying load torque and to ensure nominal tracking performance. The HNN has a simple structure and exhibits promising speed and accuracy in estimating dynamic variations in the unknown load torque apart from being computationally efficient. The proposed method guarantees a rapid recovery of nominal angular velocity tracking under parametric and non‐parametric uncertainties. In order to verify the performance of the proposed neuro‐adaptive speed controller, extensive experimentation has been conducted in the laboratory under various real‐time scenarios. Results are obtained for start‐up, time‐varying angular velocity tracking and under the influence of highly non‐linear unknown load torque. The performance metrics such as peak undershoot/overshoot and settling time are computed to quantify the transient response behaviour. The results clearly substantiate theoretical propositions and demonstrate an enhanced dynamic speed tracking under a wide operating regime, thus confirming the suitability of proposed method for fast industrial applications.

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This paper examines a two‐dimensional analytical magnetic model presented for double excitation synchronous machines (DESMs). DESMs employ both permanent magnets (PMs) and excitation coils (ECs), and therefore they offer the advantages of permanent magnet synchronous machines and electrically excited synchronous machines. Due to the linearity assumptions, the problem is divided in three sub‐problems: magnetic field of the PMs, magnetic field of the ECs, and magnetic field of the armature reaction. ECs, which have non‐overlapping structure, are in the rotor slots and carrying DC currents. The armature reaction field prediction is presented for both overlapping and non‐overlapping windings. The magnetic flux density of a brushless PM motor with six stator slots and four rotor slots has been calculated by the proposed analytical method. The results of the analytical method are compared with those of the finite element method to evaluate the effectiveness of proposed model. The presented model can be used for machines with any radius independent magnetization pattern PM and here radial, parallel and multi‐segment Halbach magnetization patterns are used as examples. Moreover, results are validated by numerical method.

Modeling of reaction conditions in the enzymatic synthesis of betulinic acid ester with multi-layer perceptron (MLP) which is a class of feed forward artificial neural network (ANN) has been done. Error to test data depends on not only trained method but also number of neuron in hidden layer. Often, it is much more of error to training data. In this manuscript Radial Basis Function (RBF) has been utilized. It determines the number of neuron due to desired error. In appropriate ANN, error for training data and test data are close. Therefore RBF’s parameters are set by multi objective meta-heuristic algorithm, in a way that, mentioned purposes have been obtained. The results of this method will be compared with previous methods.