Wind turbines are subjected to asymmetric loads and fatigue with subsequent increases in their dimension and capacity, leading to a reduction in their lifetime. To address this problem, the individual pitch control (IPC) technique is quite familiar in the control of wind turbines. IPC is used to reduce the tilt and yaw moments, simultaneously alleviating the turbine blade-root bending moments (BRBMs). This study discusses the performance of model predictive control (MPC), H-infinity ($H_{\infty }$), and proportional and integral (PI)-based IPC strategies integrated with collective pitch control. The performance of the reported controllers has been validated using the National Renewable Energy Laboratory (NREL) 5 MW full nonlinear reference wind turbine. Simulation studies are conducted at varying wind speeds and turbulent intensities as per international electrotechnical commission (IEC) norms. Comparative results in the time and frequency domains indicate that the $H_{\infty }$ based IPC achieves enhanced control performance in terms of reduction in BRBMs and damage equivalent load compared to MPC and PI-based control strategies.

In this paper, parameter variation of induction motor is estimated using sensor less vector control. Among different adaptive control of induction motor drive, the model reference adaptive system (MRAS) is one of the promising methods that has been used in this paper. MRAS model is important because it can be used over a wide range of speed variation. The error between adjustable and reference model is calculated in the stationary reference frame. The estimated speed is then calculated by an adaptive mechanism from measured terminal voltage and current of induction motor. In this paper an adaptive mechanism with fuzzy logic controller and performance of MRAS with PID controller implemented in MATLAB/Simulink environment. These schemes are applied to induction motor for sensor less speed control.