State Feedback and State Observer | Chapter 6 | Modern Control Systems: Theory, Design and Applications

In order to improve the performance of the control system, it was needed to design relative controllers and state observers to be suitable for corresponding work conditions for state variables that could depict the each other relationship between inner and outside parameters. Aiming at different performances and conditions, it was needed to know state parameters and to adjust some parameters to make the control system keep a stable state. This chapter mainly narrated state feedback, output feedback and state observer.

 

Author (s) Details

 

Zhou Gaofeng
Zhongyuan University of Technology, China.

 

Zhang Qi
Zhongyuan University of Technology, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49473-76-8/CH6 

Fuzzy Rule Suram for Controlling Drying Process System | Chapter 1 | Techniques and Innovation in Engineering Research Vol. 5

 The invention of Fuzzy Rule Suram to control the drying process system is completed activity in order to get optimal strength use. In its exercise, Fuzzy Rule Suram uses The look-up tables. The look-up table is the actuator plant to do the advantage of fuzzification. Rule Suram is based on fluffy philosophy and has two weather variables: ambient hotness and humidity. It is secondhand in the wood drying process. The mistake value and change mistake of the membership function of united states of america variable are presented by the usual triangle and trapezium maps. The study resulted in four fluffy rules in 81 conditions to control the production system, that can be innate a difference of weather and air conditions. It is used to lower the heater's energetic energy use. One cycle of due drying is a serial of the condition of the room to process as use as a wood variety. In its incident, the Suram Fuzzy Rule Algorithm to control the drying process system allows for possibility the addition of additional influencing variables. Extended fluffy rule Suram is an algorithm grown to control a drying structure using engine as an energy beginning by modifying the profit of the fuzzy enrollment function {0.5,1]; and has been grown taking into account the wind speed in the exercise of the fuzzy rule for drying espresso, dan The results of the analysis show that maximizing the use of solar radiation reaches 40.86%.

Author(s) Details:

Zakarias Situmorang,
Faculty of Computer Science, Universitas Katolik Santo Thomas, Indonesia.

Alice Erni Husein,
International affairs office, Universitas Katolik Santo Thomas, Jl. Setiabudi No. 479-F Tanjungsari Medan-20132, Indonesia.

Please see the link here: https://stm.bookpi.org/TAIER-V5/article/view/9734

Improvement of PID Controller Based on Expert System (Neural Network)| Chapter 4 | Novel Perspectives of Engineering Research Vol.7

 The proportional integral derivative PID controller was improved with the use of a Neural Network and simple hardware implementation, which will improve the control system in our high-turnover industries. The multilayer perception model is the most often used neural network model (MLP). Because it requires a desired output sequence to learn, this sort of neural network is known as a supervised network. However, we present a non-linear control of a stochastic differential equation to Neural Network matching in this paper; the model has been validated, analysed, and compared to other current controllers. The objective is to have control systems in our process industries that can achieve, improve, decrease waste, and are more flexible in terms of conversion level, as well as track set point change and reject load disturbance. This research is a first attempt at designing a simplified neutral network and proportional integral derivative PID control system for a class of non-linear processes, as well as characterising their operational features. Finally, by remodelling the proportional integral derivative PID controller with Neural Network Technique and connecting the plant process control, we were able to achieve a good result where all of the traditional proportional integral derivative PID controller's features were retained and improved using MAT-LAB. The result was great since the process industries' waste and loss were dramatically reduced to a bare minimum.

Author(s) Details:

C. E. Uchegbu,
Department of Electrical and Electronic Engineering, Abia State University Uturu, Abia State, Nigeria.

I. I. Eneh,
Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu, Nigeria.

M. J. Ekwuribe,
Department of Electrical and Electronic Engineering, Abia State University Uturu, Abia State, Nigeria.

C. O. Ugwu,
Department of Electrical and Electronic Engineering, Enugu State University of Science and Technology, Enugu, Nigeria.

Please see the link here: https://stm.bookpi.org/NPER-V7/article/view/5901