The Dynamic Elements of the Processes Industry Essay

Cashman, G. B., Rosenblatt, H. J., & Shelly, G. B. (2013). Systems Analysis and Design (10th ed.). Boston, MA: Thomson - Course Technology.

A phase-locked loop (PLL) generates a signal that has a fixed relation to the phase of a reference signal and is given in Fig. 4. A phase locked loop responds to both the frequency and the phase of the input signals, automatically raising or lowering the frequency of the controlled oscillator until it is matched to the reference in both frequency and phase. Here three phase PLL was implemented with three basic functional blocks: the phase detector, a PI controller and a voltage controlled oscillator. The phase detector is a circuit capable of delivering an output signal that is proportional to the phase difference between its two input signals. The PI controller is a low pass filter and used to suppress high frequency component and provide dc controlled signal to voltage controlled oscillator which acts as an integrator. The output of the PI controller is the inverter output frequency that is integrated to obtain inverter phase angle θ. When the difference between grid phase angle and inverter phase angle is equal, Vq =

System identification techniques are used by control engineers to gain an insight into how the process behaves and to design a model of the system as accurately as possible. The First-Order Plus Deadtime (FOPDT) model is a characterization of the dynamic response of the system to an input. It allows for calculation of the three-model parameters – process gain, process time-constant and process dead-time. [2] The standard method to develop FOPDT models is the process reaction curve (PRC) technique. To develop the PRC, the system must

The control degrees of freedom are the number of variables that can be controlled in the process, and the knowledge of them it is important in order to develop the process of the control system.

DC motors are widely used in industrial applications, robot manipulators and home appliances, because of their high reliability, flexibility and low cost, where speed and position control of motor are required. This paper deals with the performance evaluation of internal model control method with a clear objective to control the speed of separately excited DC motor. In this paper, Speed of a SEDC motor is controlled using IMC. Separately excited DC motors are highly versatile and flexible in aspects of speed control. The basic property of separately excited DC motor is that speed of separately excited DC motor can be adjusted by varying its terminal voltage. Therefore, the separately excited DC motor control is better than other kinds of motors. SEDC motor is a highly controllable electrical actuator

To proof the effectiveness of presenting method, the step responses of closed loop system were compared with that of the existing methods in the literature. Simulation results exhibit that the performance of the PID controlled system could be significantly enhanced by the MGA-based method [10].

These parameters are to be controlled for attaining a steady state motion. For this a control system needs to be designed which provides feedback in response to the errors which are present in the state of motion. [1]

In this paper, we proposed the adaptive ∆-causality control scheme with prediction and investigated the effect of the proposed scheme by QoE assessment in a

The feedback control law presented in (21), linearizes the system input-output map and tracks the desired reference signal yR(t). Moreover, by applying (21), the closed loop system converts into e ̇=(A-BK)e , in

The general process of the control system in which the PID is used is shown in figure below

Abstract. Reset controllers has began in 1959 with the Clegg integrator; It was not until the Horowitz work on quantitative design procedure was developed in 1974, firstly around the Clegg integrator in Krishnan and Horowitz and then another progress has made in 1975 around the first order reset element (FORE) in Horowitz and Rosenbaum.

Minimizing the error is the most important function in PID controller. In which the controller compute the error between the measured value and the required set point of the process variable, result in, a correct value of controller parameters according to the set point was given and also the error remains low as much as possible. The response obtained by the proportional term is dependent on the

First of all, an appropriate model of under control system should be acquired in predictive control, which is called predictive model. This model should be capable of predicting system’s behavior to provide the designer with required outputs in prediction horizon k using system’s information till the moment t. In mathematic words, it should be able to

Along with the system design, detailed numerical models (e.g. control models, mathematical models, data constraints) can be added into the analytical framework to decrease the uncertainty of simulation results. In this process, the qualitative models should be validated to ensure consistency with numerical models. During this process, the expressions of propositional logics and linear temporal logics are replaced by High Order Logics. An example of numerical model implementation is demonstrated in Fig 5; after specifying plant power, a form of proportional-integral-derivative (PID) control can be implemented and assigned to the software “Control Routine”. Note that the meaning and actual values of each parameter are gathered from the

Effective control systems use mechanisms to monitor activities and take corrective action, if necessary. The supervisor observes what happens and