Micro-electro-mechanical systems (MEMS) technology-based accelerometers and gyroscopes are small size, mass produced, low cost inertial sensors, which are now being used in aerospace, underwater vehicles, automotive, robotics, mobiles, gaming consoles, prosthetic devices and many other applications. MEMS inertial sensors are available in many grades in market and selecting the appropriate grade sensor is very important. Owing to interaction of different types of energies, different noises are generated in MEMS devices; these noises cause significant change in output and the first section of this paper illustrates that. In application, where MEMS inertial sensors are used, the accuracy, repeatability and reproducibility of inertia measurement is probed primarily by complex testing, using extensive range of physical stimuli. Noises in inertial measurement are generally dealt by designing a unit measurement model. Noises are treated as additive error in linear unit model and are modelled using various techniques so that errors can be compensated to improve the accuracy. This paper reviews the theory, framework and methodology used in the error model of a MEMS inertial sensor and stochastic modelling of measurement. Experimental results from the most commonly used Allan variance techniques are discussed. Error modelling methodology, consisting of testing and calibration methods, designing thermal model, stochastic modelling and parameter estimation techniques, is illustrated. Figures and tables under each section summarize features, merits, limitation and future research scope. This paper should serve as a single reference for researchers and engineers working on application specific system design and instrumentation using MEMS inertial sensors. Conclusion from the study should help in selecting the appropriate grade of sensor as well as the best error modelling as per the trade-off existing between accuracy and development cost of error modelling.

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This paper present an analysis and simulation of a inline Proportional Integral (PI) controller supported by a reduced order fuzzy logic controller for industrial applications demanding fast response under varied conditions. The classical PI control is integrated with fuzzy logic controller to make a hybrid control system with merits of both. For the proposed architecture of hybrid system, the fuzzy logic controller works in the specific range of operation, presents a fast response in settlement to steady state with minimum oscillations and without the necessity of switching between two controllers. Using small rule base fuzzy with in specific range reduces the computational burden dramatically and thus paves the way for its implementation on low cost microcontroller, hence making it suitable for industrial application under severe non linearities. The proposed control is simulated for speed control of brushless DC motor under MATLAB /Simulink environment.Keywords-PI controller, Hybrid fuzzy-PI, Digital signal processor, Motor drives, Low cost microcontroller. I. INTRODUCTIONThe permanent magnet brushless DC (PMBLDC) motor has emerged as a suitable option for variety of applications, viz. automotive, aerospace, consumer appliances, bio-medical instrumentation, automation industries and robotics etc. Major reason for its increasing popularity is simple structure along with small frame size, large torque to weight ratio, large operational range, good dynamic performance, high reliability, high efficiency and noiseless operation [1].The close loop control is implemented using conventional Proportional Integral Derivative (PID) controllers in the industrial applications owing to its simplicity and continuos nature. Good performance in wide range of operation makes PID controller a unanimous choice. However, in many applications the derivative term is being negated by setting the derivative gain to zero [2], leaving the versatile PI control for majority use in drive applications. In an industrial set up large load variation, nonlinearities and parameter variations are common phenomenon. Under such circumstances the performance of the controller deteriorates, resulting in large overshoot, longer rise and settling time. It is therefore, the control needs to be amended and must have an adaptive nature, so as to adjust itself according to the harsh environment, nonlinearities and parameter variations. Moreover, designing a PI controller becomes cumbersome if multiple objectives with conflicting interest have to be achieved [3]. A non linear controller like fuzzy logic controller (FLC) seems to be a