DC motors are being used as an actuators in the processcontrolapplications and also extensively used in industry for applications such as robot arm drives, machine tools, rolling mills, and aircraft control , .This paper presents the implementation of a control algorithm, mostly used in real-time controlapplications, a classical PI controller and a rule based Fuzzy Logic controller to control the speed of a DC motor. Design of a PID controller needs a mathematical model. However, exact modeling becomes complicated when the control algorithm is to be designed for a non-linear system. Due to the linear nature of the controller, even if a superlative control design is achieved, the efficiency of the algorithm will be reduced. In order to eliminate these concerns, the concept of non- linear Fuzzy Logic controller (FLC) is being used. The FLC’s are used in processes where the exact mathematical modeling and transfer functions are not known for both linear and non-linear applications. Although an accurate understanding of the inputs and outputs is required, a precise mathematical correlation between the system characteristics is not necessary. The relationship between inputs and outputs of the system are specified by the user through a rule-based approach. This paper discuss about
Also the IMC-PID controller allows good set-point tracking but sulky disturbance response especially for the process with a small time-delay/time-constant ratio. But, for many processcontrolapplications, disturbance rejection for the unstable processes is much more important than set point tracking. Hence, controller design that emphasizes disturbance rejection rather than set point tracking is an important design problem that has to be taken into consideration.
We consider that by achieving such an experimental model like the one described above, but especially by means of the experiments conducted on it, the main benefit will consist in the increase of the amount of knowledge on this topic and a significant facilitation in achieving future complex monitoring applications for systems with a relatively large volume of data and for heterogeneous data acquisition systems.
Kourti and MacGregor , provide a different approach based on principal components analysis. The T² is expressed in terms of normalized principal components scores of the multinormal variables. When an out-of-control signal is received, the normalized score with high values are detected, and contribution plots are used to find the variables responsible for the signal. A contribution plot indicates how each variable involved in the calculation of that score contributes to it. Computing variable contributions eliminates much of the criticism that principal components lack of physical interpretation. This approach is particularly applicable to large ill conditioned data sets due to the use of principal components. Contribution plots are also explored by Wasterhuis et al. .
(COTS) sensors and microprocessor boards. While JOEfeatured a digital signal processor board, controller boards based on microprocessor such as the 68HC11, ARM and the ATmega series of the Atmel architecture have become the staple in recent years. Arduino is an open prototyping platform based on ATmega processors and a C language-like software development environment, and can be connected with a variety of COTS sensors  . It is fast becoming popular platform for both education  and product development, with applications ranging from robotics , to processcontrol ,  and networked control  .In this paper, we report a student project on the design, construction and control of a two-wheel self-balancing robot. The robot is driven by two DC motors, and is equipped with an Arduino Uno board which is based on the ATmega328 processor, 3-axis MEMS (Micro Electrical Mechanical Systems) accelerometer and 3-axis MEMS gyroscope. Two control designs based on the linearized equations of motion is adopted for this project: a proportional-integral-differential (PID) control. The approach is found to be robust to modelling errors which can be incurred during experimental determination of such electrical and kinematic parameters as moments of inertia and motor gains. Simulation and experimental results are presented, which show that stability of the upright position is achieved with PI- PD control within small tilt angles.
King. et.al. (1977)  illustrated the implementation of fuzzy logic algorithms to the control of dynamic processes in industry. Fuzzy logic can be used with a view to automate those processes where modeling difficulties and poorly-defined processes result in imperative need for manual control. Heuristic approach towards nonlinear time varying process systems was developed. Fuzzy Logic control approach was applied to two industrial applications. One application was the control system for temperature control of a steam boiler and second being as the temperature control of a stirred tank. During the experimentation in the first experiment it was found out that gains and time constants obtained were varying as according to the initial conditions of the controlprocess outputs, also it was found that the process was highly nonlinear. This led to the result that it is not possible to achieve better control responses using the similar controller parameters in a constantly changing dynamic process. In the second experiment it was found out that the process was oscillating about a set point, and the primary cause of the instability of the system was time delay, thus time delay rules were considered in the controller structure. The results in the second experiment show that good control responses in terms of oscillations and settling time can be achieved. The heuristic rules based approach can itself be automated to obtain better results. For further research application of an adaptive-learning scheme to synthesize control rules with performance criterions can be developed.
ProcessControl Technology unit, is a fully contained bench top apparatus consisting of a Process Module, and a Control Console with a built in power supply. A Windows based software with full control and data mber of experiments in processcontrol are included covering flow, level, pressure, temperature, and combinations of the processes. The Control Console is easily connected to a PC using the USB connection or to a PLC using a D type connector. The Console has a mimic of the Process Module on the front and includes fault switches, and test points from all of the transducers. Level is measured using a 0 to10V magnetostrictive sensor; pressure is measured using a gauge 0 to 5bar sensor and flow using a flow rate sensor. PT100 is used to measure temperature The front of the control module has a schematic of the Process Rig, ON/OFF indicator, six illuminated fault switches, test points, indicators to show status of the elements on the rig, and a backlight switch to turn on the backlights for the displays on
Nondestructive testing systems are not mass-produced products. The small number of marketable systems and the specifics of individual test requirements lead to high engineering expenditures at low sales volumes. Especially process integrated NDT systems are often “unique items”, leading to high costs for purchase and maintenance. However, these costs are faced with a variety of savings, which are often overlooked. These include not only the saved non-conformity costs (costs due to further processing, eliminating the nonconforming material, call-back, product liability, etc.) but also the saved costs for manual tests (destructive and nondestructive) and the possibility to increase productivity and yield.
- The selection of a Shewhart control chart is based on empirical rules: Shewhart control charts are appropriate for constant process means, with randomly changing means Shewhart control charts would be appropriate; abrupt process changes are controlled using acceptance control charts. But there are no defined guidelines when choosing sample size or sample frequency. The chart is selected according to expense and required explicity. Two track control charts for control of location and deviation are mostly used. The M ean /stan d ard d ev iatio n ch arts are comparably specific; the expense therefore is high. Where computer aided statistical control is not in use, average/range charts which are easy to use are definitely recommended ,  and  , - Calculation of the control limits based on six- sigma, dependent on the confidence level 1 - a and sample size n, which is often chosen to be n = 5.
Poisson process is used to model the occurrences of events and the time points at which the events occur in a given time interval, such as the occurrence of natural disasters and the arrival times of customers at a service center. It is named after the French mathematician Siméon Poisson (1781-1840). In this paper, we first give the definition of the Poisson process (Section 2). Then we stated some theroems related to the Poisson process (Section 3). Finally, we give some examples and compute the relevant quantities associated with the process (Section 4).
Computer graphics is an exclusive art of drawing lines, pictures, charts, etc using computers with the help of proper programming. It is made up of number of pixels. The smallest graphical picture or unit which is represented on the computer screen is called Pixel. Computer Graphics is used for manipulating and representing an image data by computer. The continuous development in this field has created a vast change in media like video game and animation industry. The paper aims to provide a step-by-step understanding of the process of developing computer graphics. The applications of computer graphics have also been discussed in detail.
o f an extremely hard and therm ally stable TiAIN coating w ith the sliding and lubricating properties o f th e o u te r W C /C co a tin g . D u rin g the d ry m achining process the coating takes the place o f the coolant to protect the cutting edges from wear, w h ile sim u lta n e o u s ly e n su rin g re lia b le chip evacuation. These lubricious coatings reduce the generation o f heat by decreasing the am ount o f friction. Coatings such as m olybdenum disulfide and tungsten carbide-carbon have low coefficients o f friction and can lubricate the cutting action. These coatings are soft and have a relatively poor tool life. To co m p en sate for th is lim itatio n , these coatings are often used with hard under-layers such as titanium carbide, titanium alum inium nitride, alum inium oxide or some com bination o f these. Diamond-like carbon (DLC) coatings are the first kind o f coating for the dry machining o f aluminium a llo y s . The surface o f a D LC co atin g is exceptionally smooth and has an extrem ely low friction coefficient, 0.05 to 0.2 |i, for aluminium alloys. DLC coatings are based on the same carbon chemistry as diamond and graphite and feature an amorphous structure that provides a high hardness a n d g o o d lu b ric a tio n ([1 0 ] an d [2 1 ]). T h e
It is clear that there are two different high frequency dynamics at work here by virtue of the pseudo sinusoidal representations for the components of AP risk process. One dynamic ( 𝑟 𝐵 𝜖 ,𝜖↓0 𝑡 , 𝜔 ) is pulling towards the “origin” 12 , and the other is pulling away ( 𝑟 𝐵 𝜖 ,𝜖↑0 𝑡 , 𝜔 ). Thus, we have just constructed the local time behavior of AP risk processes in an 𝜖 -disk, and shown that risk aversion and risk seeking run on different time clocks near the origin. In equation (60), the quantity 𝑟 0 sinh 𝑡 is increasing in time away from the origin, and it is zero at the origin. It approaches 0 from the left. By contrast, in equation (61) the process converges to 𝑟 0 when approached from the right since it is right continuous. This asymmetric result, derived from Arrow-Pratt risk measure for von Neuman Morgenstern utility functions, predicts Tversky and Khaneman‟s (1992) asymmetric value function loss aversion result--relative to a
significantly in the past few years because of poor quality and if the same trend continues, it can be safely speculated to decrease even further in the years to come-by. Thus, it has become more important than ever to provide an effective system to accurately control and continuously monitor the quality of production. The continuous flow of new innovations in terms of process methods in production in textile industry over the past few decades have definitely led to increased automation and increased speed of operations. However, in order to gain cost competitiveness over the low labor costs in developing countries and to respond quickly to the global customer demand, many spinning mills and dye houses have invested mostly in advancements in technology that yield significant benefits in productivity, and made adjustments in their deficiencies in quality by offsetting or widening the control limits (Dicken, 2003). This certainly leads to a situation where the manufacturer fails to detect a true out-of-control situation resulting in frequent false alarms and unwarranted process calibrations. Hence, despite the widespread use of statistical tools and standard procedures alongside appropriate well advanced equipment and personnel, most manufacturing and processing plants have not been able produce quality products with desirable profits to thrive and compete in domestic as well as international markets (Jordan, 2001).
The speed of information and the new technologies have established a globalized environment of high competition in which price, term, quality Controlling variables involved in production process in order to make it more efficient is one of the growing concerns of entrepreneurs. Many systems used in companies need processes of monitoring and production control (Jacobi et al., 2002). defines a process as "a set of causes that provoke one or more effects". These causes are called manufacturing or service factors that can be: raw materials, machinery, measures, environment, labor and method. The production inspection, enables quality control during manufacturing, ie, control charts exhibit a focus on defect s way, by preventing the exit of imperfect products, it can be considered Industry needs to control different process parameters and product data. In industry 4.0, these data and ogies will be the basis for all analysis and control systems that will present information that allows driven decisions based on statistical techniques adds quality to g processes. The use of Statistical ProcessControl in a systematized and integrated way is the first step to be in line with industry INTERNATIONAL JOURNAL
The basic block diagram of a BLDC motor drive is shown in Fig. 1. In BLDC motor the rotor position is known by the hall sensor. The speed control of the motor is done with the help of a PI controller. The reference speed (ωr*) and actual motor speed (ωr) are compared and the speed error (ωre) is computed. The speed controller takes the speed error as input and using suitable logic, generates the reference torque command (T*). The output of the controller is given to the control DC voltage source which is fed into the voltage source inverter. The three-phase ac output generated by the inverter is fed to the BLDC motor.