Analyzing Transient Response Characteristics of Glycerin Heating Process | Chapter 3 | Theory and Applications of Engineering Research Vol. 8

 The glycerin heating process system is an important performance characteristic that determines the correctness of a system that responds to the change in input temperature. Controlling the temperature of the glycerin purification process system was not an easy task, as an increase in operating temperature would significantly reduce the quality of the purified glycerin. This is because an unlimited increase in temperature beyond the set point and an excessive prolongation of the heating process would result in the formation of an excessive secondary oxidation product in the final purified glycerin. This paper discusses the transient response characteristics of the glycerin heating process using a parallel PID controller. The glycerin heating process behavior was determined experimentally using a step input test and modelled as the First Order plus Delay Time. PID controller is the essential element in the process control system used to regulate the measured variables to the desired value, existence of overshoot, and larger settling time even in disturbances or changes in the input signal. The controller parameters were adjusted using Ziegler- Nichols, Cohen-Coon, and Wang tuning methods, each of which was analyzed based on the corresponding integral error criterion value. The Integral Square Error, Integral Absolute Error and Integral Time-weighted Absolute Error criteria value were used to evaluate the efficiency of the glycerin heating process. The transient response performances in terms of overshoot, rise time and settling time were also evaluated. Simulation work has shown that the process has experienced high overshoots for Ziegler-Nichols and Cohen-Coon, and has taken a longer time to settle. Wang's method exhibits no overshoot but a slow response. The lower gain PID controller was found to improve the process response in terms of overshoot but increase the rise time and settling time. The results indicate that the desired process performance was more or less influenced by the interaction between the tuning parameters. The study concludes that the PID controller with lower gain improves the transient response of the glycerin heating process in terms of overshoot but takes a longer time to settle. The Ziegler-Nichols PID controller is not recommended for controlling the glycerin heating process due to process response oscillations that are difficult to eliminate without prolonging the heating cycle.

Author(s) Details:

Zuriati Janin,
Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia.

Hazilah Mad Kaidi,
Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia.

Robiah Ahmad,
Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia.

Please see the link here: https://stm.bookpi.org/TAER-V8/article/view/13787

Enhancing Load Frequency Control in a Four-Area Power System Network with an Optimal ANN Controller | Chapter 8 | Theory and Applications of Engineering Research Vol. 4

This research describes an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity. Automatic load frequency control is the main area of concern in the operation of an interconnected power system. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprises of transfer function models of two thermal units, one nuclear unit and one hydro unit. To accommodate for non-linearity, the model integrates the generation rate constraint (GRC) of distinct units. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The network training is based on the data collected from the optimal controller for different perturbations or step load changes. As the inputs are applied to the network, the outputs are compared to the target values, and the supervised learning rule is used. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers. The proposed controller performs satisfactorily under random step load changes and thus desirable dynamic control of the system is achieved.

Author(s) Details:

Basavarajappa Sokke Rameshappa,
Department of Electrical and Electronics Engineering, Bapuji Institute of Engineering and Technology, Davanagere Visvesvaraya Technological University, Belagavi, Karnataka, India.

Nagaraj Mudakapla Shadaksharappa,
Department of Electrical and Electronics Engineering, Bapuji Institute of Engineering and Technology, Davanagere Visvesvaraya Technological University, Belagavi, Karnataka, India.

Please see the link here: https://stm.bookpi.org/TAER-V4/article/view/13102

Designing of a Self-Learning Artificial Neural Network Controller for Critical Heating, Ventilation and Air Conditioning Systems | Chapter 2 | Advances and Challenges in Science and Technology Vol. 9

 Artificial affecting animate nerve organs networks (ANN) has emerged as a effective learning method to perform complex tasks in very non linear dynamic atmospheres. This work addresses the stability and adeptness problems accompanying standard Heating, Ventilation and Air-Conditioning (HVAC) systems by executing a self-learning ANN boss. Although traditional plans such as Proportional, Integral, and Derivative (PID), On-Off controllers, thus are used, they abandon to give intelligence and encourage mathematical complicatedness in implementation. They take more protracted to acquire a large size of stability, use plenty energy, and produce oscillations and peak overshoots. This work focuses on engaging a self-learning ANN located intelligent boss to scheme the air cooling whole. It uses the user's inputs to estimate the fan and water flow speed so that provide comfort accompanying the least amount of strength consumption and relieving time. The type of practice checked here is appropriate to many other types of non-uninterrupted control issues.  In neural topology, the Back Propagation (BP) method has been secondhand. The PID and Self Learning ANN controllers have been distinguished, and MATLAB Simulink has been used to display the results. Using the Self Learning ANN boss architecture, honest-time fittings for the HVAC system has happened created and distinguished with the PID controller.

Author(s) Details:

Raghavan Chandran Ilambirai,
Department of EEE, SRMIST, Kattankulathur, Tamil Nadu, India.

Shanmugapriya Subramaniyan,
Department of EEE, SRMIST, Kattankulathur, Tamil Nadu, India.

Geethanjali Subramaniyan,
Department of EEE, SRMIST, Kattankulathur, Tamil Nadu, India.

Please see the link here: https://stm.bookpi.org/ACST-V9/article/view/12618

On the Influence of Time Discretization of Signals on the Stability of Modern Electromechanical Automatic Control Systems (ACS) | Chapter 3 | Fundamental Research and Application of Physical Science Vol. 5

 The new automatic control orders are the systems with rhythm semiconductor converters power, mathematical sensors of mechanical variables – collar and movement speed, and digital control instruments – regulators. All these maneuvers “break” the signals in time, that is to say, they make them individual. Time discretization significantly influences the stability of processes in ACS. Theoretical reasoning of ACS even with one individual element demands a transfer from constant frequency traits and transfer functions of all links of the ACS structure to the discrete traits. That significantly demands the change of manufacturing approaches to the ACS analysis. In current years, due to a meaningful reduction in the inspecting time in mathematical sensors and controllers, the influence of discreteness on the ACS stability has tired, and little attention has existed paid to it in research. However, the analytical “content” of the problem has not changed because the 80s of the 20th century.Moreover, this question is very relevant in extreme-precision high-speed mechanical control systems, exceptionally in those using sliding processes.In this paper a new approach is projected, which resides in interpreting the discretization movement by a link that suppresses input signals accompanying a high difference frequency. This link considerably simplifies the engineering estimates of ACS with discretization and shows new potential for the synthesis of specific systems. Such possibilities involve the efficient dispersion of sampling pauses between the channels of widely used PID controllers and the channels of elaboration and formation of slip courses in systems accompanying a variable structure accompanying sliding processes (SVS accompanying SP) that have been promptly developing in recent age. It is specifically main that the proposed belief technique remains active when the discretization intervals are corresponding with the opportunities of transient processes in the ACS under consideration. Theoretical supplyings have been confirmed and confirmed by posing, the results of which are given in this place chapter.

Author(s) Details:

Vladimir Kodkin,
Power Engineering Faculty, South Ural State University, Chelyabinsk-454080, Russia.

Ekaterina Kuznetsova,
Power Engineering Faculty, South Ural State University, Chelyabinsk-454080, Russia.

Alexander Baldenkov,
Power Engineering Faculty, South Ural State University, Chelyabinsk-454080, Russia.

Alexander Anikin,
Power Engineering Faculty, South Ural State University, Chelyabinsk-454080, Russia.

Please see the link here: https://stm.bookpi.org/FRAPS-V5/article/view/10646

Optimal PI Pitch Control of SCIG Wind Turbine Using Grey Wolf Optimizer for Dynamic Stability| Chapter 5 | Novel Perspectives of Engineering Research Vol.7

For maximum performance in power limiting to rated value and decreasing loading on wind turbine components, the wind turbine's Proportional Integral (PI) pitch control must be tuned to perfection. The Ziegler Nichols (ZN) tuning method has traditionally been used to tune the PI controller for pitch angle control. Soft computing meta-heuristic optimisation approaches are now commonly utilised to fine-tune PI controllers for precise and optimal wind turbine control (WT). The Grey Wolf Optimization (GWO) technique is developed in this paper to optimise the PI controller for SCIG-WT pitch angle control. The goal is to use GWO to reduce the Integral Time multiplied Square Error (ITSE) objective function, which is based on the WT's power error. This ensures the greatest possible combination of optimised pitch control PI parameters, and thus the best possible dynamic response of the SCIG WT working in power limitation mode under disturbances such as sudden high wind speeds and changes in air density in bad weather. The proposed GWO tuning methodology is compared to a meta-heuristic optimisation technique, notably the classic Particle Swarm Optimization (PSO), the evolution-based Genetic Algorithm (GA), and the Ziegler Nichols (ZN) tuning method, in order to validate its performance. In comparison to other algorithms, the GWO demonstrates its superiority and efficacy in terms of fast convergence of tuning characteristics. Furthermore, compared to PSO, GA, and ZN tuned PI controllers, the GWO tuned PI controller pitch control gave the least settling time and overshoot in pitch angle from rapid changes above rated wind speed or air density in the SCIG-WT. In addition, when compared to PSO, GA, and ZN tuned PI controllers, it had the least undershoot of WT active power and rotor speed. As a result, when compared to PSO, GA, and ZN tuned PI controllers, the GWO tuned PI controller used in pitch control provided improved dynamic stability of SCIG-WT output.

Author(s) Details:

Aliyu Hamza Sule,
Electrical Engineering Department, Hassan Usman Katsina Polytechnic, P.M.B. 2052 Katsina, Katsina State, Nigeria.

Ahmad Safawi Bin Mokhtar,
Electrical Power Engineering Department, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.

Jasrul Jamani Bin Jamian,
Electrical Power Engineering Department, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia.

Please see the link here: https://stm.bookpi.org/NPER-V7/article/view/5902

 

Investigating the Performance of a Simple Robotic System Using PID Controller | Chapter 02 | Novel Perspectives of Engineering Research Vol. 6

 The steady state error owing to applications of PID controller for different types of input signals, such as unit step, unit Ramp, and unit Parabolic, has been calculated in theoretical studies to determine the performance of a simple robotic system. A programming language in the 'C' programming language has also been developed to determine the overall gain of the system for various frequencies and hence plot the frequency response of the same. There have been some numerical computations performed. The findings have been compiled, graphed, and discussed. We also ignored the Coulomb force and errors caused by disturbance torque in the output shaft in this investigation.

Author(S) Details

Sreyasree Mandal
School of Computing & Information System, Grand Valley State University, Michigan, USA.

N. K. Mandal
University of Engineering & Management, Kolkata, India.

View Book:- https://stm.bookpi.org/NPER-V6/article/view/5496

Intelligent Control in Robotics: Design and Application | Book Publisher International

 In the realm of robotics, this book focuses on the construction of intelligent controllers. These advanced controllers used both conventional and non-convective control methods. Each controller design was given, and the performance of each was verified using numerical simulations of robotic systems.

First, parallel robotic systems were presented. They have demonstrated for decades that they are capable of high speeds and acceleration. In order to achieve adequate tracking performance despite non-linearity, time-varying parameters, and uncertainties, a conventional PID controller was developed to operate a four degrees of freedom parallel robot, namely the VELOCE robot.

Then there were robotic under-actuated systems and their control problems. An under-actuated system, the inertia wheel inverted pendulum, was examined for control using the adaptive fuzzy control technique. The fluctuation of the robot's characteristics, the non-linearity of the model to be controlled, and measurement errors are all taken into consideration in this control technique.

The exoskeleton-type robot for neuromotor rehabilitation was the last form of robotic system discussed in this book. The goal is to be able to provide devices that can control the mechanical forces that are distributed along the patient's limbs as they move. We concentrate on upper-limb robot control, which allows us to track the sick patient's preferred movement trajectories. The adaptive sliding mode control was presented as a way to provide a control that would allow researchers to analyse the physical human-robot interaction in a repeatable manner.

Author(s) Details

Imen Saidi
Automatic Research Laboratory, LA.R.A, National Engineering School of Tunis, ENIT University of Tunis El Manar Tunis, Tunisia.

Nahla Touati
Automatic Research Laboratory, LA.R.A, National Engineering School of Tunis, ENIT University of Tunis El Manar Tunis, Tunisia.

View Book:- https://stm.bookpi.org/ICRDA/article/view/5015