Development of first principle models and linearization, State estimation for linear perturbation models (Luenberger observer), Models for unmeasured disturbances and Kalman filtering, Innovation form of state space model, Extended Kalman filtering , Development of Grey box models (using ANN etc.), Discrete time series models: FIR and ARX models, Development of ARX models by least square estimation, Unmeasured disturbance Modeling: ARMAX, OE, Box-Jenkin?s models, Parameter estimation using prediction error method and instrumental variable method, Distribution of bias and variance errors, Input signal requirements for identification experiments, Control Relevant Identification, MIMO system identification?Issues of directionality, scaling and input signal design. U-D factorization and AUDI algorithms, Recursive approaches to identification and forgetting factor specification, Introduction to nonlinear time series models (Hammerstein / Weiner / NARX) and parameter estimation, Subspace identification techniques and development of state space models, Closed loop identification, Introduction to adaptive control.