This study presents design and hardware implementation of cascade neural network (NN) based flux estimator using field programmable gate array (FPGA) for speed estimation in induction motor drives. The main focus of this study is the FPGA implementation of cascade NN based flux estimator. The major issues in FPGA implementation are optimisation of cost (resource) and execution time. A simple non-linear activation function called as Elliott function is used to reduce the execution time. To reduce the cost, and effectively utilise resource, the concept of layer multiplexing is adopted. The lowest bit precision needed for good performance of the estimator is identified and implemented. The proposed NN based flux estimator using simple excitation function and minimum bit precision is implemented using layer multiplexing technique. The designed estimator is tested on Spartan FPGA kit (3sd1800afg676-4) and the results obtained are presented.

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In this paper, a novel reactive power based model reference neural learning adaptive system (RP-MRN-LAS) is proposed. The model reference adaptive system (MRAS) based speed estimation is one of the most popular methods used for sensor-less controlled induction motor drives. In conventional MRAS, the error adaptation is done using a Proportional-integral-(PI). The non-linear mapping capability of a neural network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. Thus, a neural learning algorithm is used for the adaptation mechanism in MRAS and is often referred to as a model reference neural learning adaptive system (MRNLAS). In MRNLAS, the error between the reference and neural learning adaptive models is back propagated to adjust the weights of the neural network for rotor speed estimation. The two different methods of MRNLAS are flux based (RF-MRNLAS) and reactive power based (RP-MRNLAS). The reactive power-based methods are simple and free from integral equations as compared to flux based methods. The advantage of the reactive power based method and the NN learning algorithms are exploited in this work to yield a RP-MRNLAS. The performance of the proposed RP-MRNLAS is analyzed extensively. The proposed RP-MRNLAS is compared in terms of accuracy and integrator drift problems with popular rotor flux-based MRNLAS for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated

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