Design for vegetable waste fermentation control systems based on semi-tensor product fuzzy controller

Abstract

A multivariable fuzzy controller was designed to solve the control problem of vegetable waste fermentation. The knowledge-based fuzzy reasoning was constructed according to the sample data from both the fermentation process and the rule of temperature change in the control process. It is represented using the mathematical method of semitensor product matrices. The structural matrix of the fuzzy logic was constructed and the complex fuzzy reasoning was converted into simple matrix operations. In addition, a new algorithm based on the least in-degree method is put forward, solving the problem of control rules being incomplete and inconsistent. It was used for the design of the fuzzy controller on vegetable waste fermentation. The result of experiment proved that this control method is effective; the control method improves the adjustment speed as well as the control precision.

Development of Novel Bioreactor Control Systems Based on Smart Sensors and Actuators

“…The KBCS has been implemented at two levels. The primary level entails the direct control of a specific process parameter via fuzzy logic, also known as fuzzy control (Wang et al, 2018). The higher level of implementation entails a knowledgebased supervisory control system that contains several modules (Figure 4) (Kohout et al, 2015).…”

“…A great number of applications of fuzzy control have been reported in wastewater treatment. For example, Wang et al (2018) reported the design of a multivariable fuzzy controller to solve the control problem of vegetable waste fermentation. They constructed a structural matrix of fuzzy logic to convert the complex fuzzy logic into a simpler matrix operation, and they also adopted a new algorithm based on the least in-degree method to solve the problem of incomplete and inconsistent control rules.…”

Algebraic Expression and Construction of Control Sets of Graphs Using Semi-Tensor Product of Matrices

“…Step 2: From M we know that there are 165 columns greater than or equal to 1 n in M and can get the following set K . {1, 2, 3,4,5,6,7,8,9,10,11,13,14,15,17,18,19,20,21,22,23,24,25,26,27,29,30,31,33,34,35,36,37,38,39,40,41,42,43,45,46,47 174,177,178,179,180,181,182,185,187,189,193,194,195,196,197,198,201,…”

“…Therefore, theoretically, STP can find its applications in great majority of science and engineering fields, especially in those can be modelled as discrete mathematical models. STP is well established and has been successfully applied in many areas such as neurosciences and neurology [17]- [19], automation control systems [20]- [22], systems science [23]- [25], computer science [26]- [28], and theory of graphs [29]- [34].…”