S. Thangavel

Robust stabilization is an important characterization to get improved in the control system to optimize the performance of the desired system. Most of the controllers available suffer from problems such as difficulty in the tuning process, sluggishness in response time, quick and global convergence, etc. This paper considered proportional-integral optimized with genetic algorithm (GA-PID)controller on inverted pendulum for the control of the angle position. A MATLAB script for a GA was developed to obtain optimum PID parameters that would keep the pendulum angle at equilibrium (i.e., returns the pendulum to the desired point as quickly as possible) by minimizing an objective function (integral time absolute error [ITAE]). Furthermore, obtained outcomes confirm the performance of holding on the upright position from the downward direction such that the angle of the pendulum is manipulated to control the inverted position in its erect position in a rapid manner. In the experimental analysis, the developed GA-based PID controller is compared with the conventional PID controller to evaluate the performance of the inverted pendulum. The obtained results show that the GA-based PID controller confirms the enhanced performance indexes by holding minimum settling time and peak overshoot on comparing with the conventional PID controller.

Servo electromechanical systems are used in industrial automation to attain high accuracy, reliability, linearity, and high aspect ratio. Such technology possesses the advantage of compact structure and easy control over electro-pneumatic and electro-hydraulic systems. The major drawback of this technology is the high friction/vibration and also the jerk of servo electromechanical drives that are caused by load variation and speed regulation. When the load is varied, the force acting on the ball screw leading along the axial direction is varied, resulting in the creation of vibrations that lead to fatigue and wear. The major cause for this nature is magnetic loading and unloading capability of electrical machines, selection of controller tuning values, and feedback mechanism. It is necessary to control the magnitude of vibration to get smooth control on the toolpath during load variation. To arrest the vibration, the position control of the servo motor is implemented. In this proposed work, the design requirement of the servo mechanism, such as the stability of the driving mechanism, is examined in detail with mathematical modeling of the servo system. Simulation of the servo mechanism performance according to design and operating parameters is performed based on the derived mathematical model. To analyze the performance of the position control, gain-phase margin controller is compared with conventional Ziegler Nichols and auto-tune PI controllers. Further, the machine learning algorithm of K-means clustering is executed by taking the motor current parameter because the motor current is proportional to the torque, which gets direct impact by the load variations. Further, the cluster assignment on the motor current attributes is undertaken to infer either that the load variation is gradual or that it gives sudden fluctuations during the position control on the trajectory path.

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.