Automática y Robótica en Latinoamérica

International countries’ initiatives regarding the I4.0 paradigm

The World Economic Forum (WEF) is a relevant international organization for public-private cooperation engages I4.0 and involves other social strategies. WEF reports crucial recommendations about smart factory initiatives around the world. Table 1 indicates I4.0 some countries’ initiatives, and many of them usually come from the original German I4.0 or WFE [22]. [21], [22]. In the future cases of LA I4.0 implementations, it should be necessary to have a look at the relevant results in advanced countries, as well as those, which already have obtained effects.

Table 1

Relevant I4.0 initiatives, platforms and tools for the implementation of I4.0

Source: Own elaboration.

Medellín as a Centre for the Fourth Industrial Revolution from Colombia

The case for Colombia, Medellín is its second central city. In 2013, beating the other finalists New York and Tel Aviv, Medellín honored with a City of the Year award, because of its innovative initiatives as well as the mobility, environmental sustainability [22]. Medellín’s economic dynamics is focused on a diversity of aspects that are a focus in several clusters: i) fashion and advanced manufacturing, ii) sustainable energy iii) sustainable habitat; iv) business tourism; v) Medellín health city, vi) digital business and vii) cluster of coffee. Different entities are part of the innovation ecosystem of the city that is born from the strategic plan of science, technology, and innovation of Medellín 2011-2021, based on the 22@Project of Barcelona, which focuses the efforts on promoting innovation. Public organisms, as the “Corporación Ruta N” generates innovations in the city, together with companies, universities, the State, and society to a social transformation [22].

This corporation has achieved several milestones such as the creation of a business landing to improve the relationship of companies, and promotion the technological change to the Smart Manufacturing by financing projects in I4.0 and IoT [23]. The Capital N program founds projects for new entrepreneurship, administer public funds for innovation projects, in technology and markets for the search for business opportunities. Examples of the I4.0 tests are given by other enterprises in Antioquia (Department of Colombia): the project “Transformación digital” for Invesa, (2018), Pintuco: “Seguimiento energético de la Producción” (2017-2018), acting as consulting SIMAC S.A.S, and others. In 2018, the WEF selected Medellín to create the first the Centre for the Fourth Industrial Revolution Network (C4IR), to accelerate the benefits and minimize the risks of emerging technology. [21]. This center aims to include institutions advancing public-private cooperation on the governance of emerging technologies. It joins the centers in Israel and Arab Emirates C4IR networks that focuses on nine areas of interest: i) artificial intelligence and machine learning, ii) IoT, robotics, and smart cities, iii) blockchain and distributed ledger technology, iv) autonomous and urban mobility, v) drones and tomorrow’s airspace, vi) precision medicine, vii) digital trade, viii) fourth industrial revolution for the earth, and ix) data policy.

Discussion, Conclusion and Future Work

The industry of LA underlies the multinationals, and if it takes I4.0 as a paradigm, it will be reflected sooner rather than later. In the automotive sectors, as well as in agro-industry and critical systems such as water, gas, oil, electricity, and transport, they are candidates to be digital leaders. Quickly multinationals establish vital technologies in I4.0 by CPPS mentioned above, but in SMEs, there is a criticism derived from the associated costs. As a reflection, in the case of LA, the transitions of the labor force, in an era of automation, will be strongly affected by the cost of labor that is relatively low versus adequacy, training in the automation process for its costs and time required. Perhaps our most important barrier is that we do not think about ourselves and, on the contrary, we think about what multinationals do. In any scenario, having a human capital with the required capabilities is a challenge in any country.

Thus, as a further design, a CPPS architecture for LA should support communication between people, machines, and products more efficiently, since their technology may allow data acquisition processes for more autonomous, but limited control [20]. It remains indispensable for specific tasks to interact with humans (for example, maintenance), this will be achieved through adaptable (efficient and reconfigurable) interfaces and IMS. These needs require more decentralized applications supported by IIoT, where CPPS work comfortably, although typical hierarchical control levels, including programmable logic controllers (PLCs), prevail in the industry [24]. The application of I4.0 paradigm in LA implies the adaptation of elements of monitoring and supervision in a non-fully automated floor plant, and the need for training of technical personnel and engineers in modeling techniques, control of integral supervision, analytical data that are novel, both in LA and in the rest of the world [18].

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Type II fuzzy logic controller for a liquid level system

Jorge L. Díaz Rodriguez1, γ, Oscar M. Duque Suarez2, Jair Araujo Vargas2, Carlos Clavijo Pérez2

1 Electrical Engineering Program, Universidad de Pamplona, Pamplona, Colombia.

2 Mechatronic Engineering Program, Universidad de Pamplona, Pamplona, Colombia.

γ. Corresponding author: jdiazcu@gmail.com

Abstract

This paper deals with the design and implementation of a type II logic fuzzy controller. Thus, using artificial intelligence (AI) techniques for better performance over a classical PID controller. The type II logic fuzzy controller was designed using the Fuzzy Type 2 toolbox using the Matlab professional software. The results through the prototype of level control allowed identifying the advantages of the fuzzy logic controllers type II with respect to its predecessor: the type I fuzzy logic controllers. This was verified using the dynamic response performance parameters as the maximum overshoot and the setting time.

Keywords: Fuzzy logic controller, type II fuzzy logic controller, level control system.

Introduction

Fuzzy logic systems have been applied successfully to a wide range of problems in different applications. One of these types of applications refers to the use of fuzzy logic systems for the modeling and approximation of fuzzy inference systems. This is used to model human knowledge or for linear or non-linear approaches. However, the existence of uncertainties and the lack of information in many global problems make it difficult to model such problems using only expert knowledge. Although, type I fuzzy logic systems are the best-known model of fuzzy logic they have received attention for decades. The advance in the research of fuzzy sets and type II fuzzy systems and its better performance on systems of type I fuzzy logic controllers, caused that they motivated many researches to solve problems and in this way to control different systems [1-12].

A prototype was used in this work to know the advances of these investigations and the benefits of this type of controller. For which the results were verified and contribute to the development and implementation of this type of control strategy.

Control system of a liquid level

The process used as a test bench for this work is shown in Figure 1. The system has two tanks of rectangular shape, which the lower tank will have as a function to store the fluid so that by means of the pumping machine be pumped to the upper tank. The fluid level in the upper tank is measured by differential pressure sensor. The control action is carried out through a solenoid valve, which will have the function of controlling the amount of liquid that flows through the pipe in order to regulate the flow of the fluid.

Figure 1

Level bench for liquids

Source: Prepared by the authors

Fuzzy logic controller (FLC) design

The fuzzy logic controller design of the type I controller will first be designed to establish the parameters of the type II fuzzy controller, and in this way, show the differences between them. The development of a fuzzy logic controllers was done under the structure of a PD controller; therefore, it can be established that the controller will depend on two input linguistic variables. In this way, part P will be under the signal that occurs from the difference in closed loop between the reference value with respect to the measured value (Error), and part D will be established from the difference between (current error) with respect to (previous error) calling this as the error delta (D.Error).

Input variables

For the universe of discourse of type I and type II fuzzy input drivers, it will depend on two variables that are named (Error) and (D. Error). For both variables, trapezoidal and triangular membership functions will be used, given that they have a low mathematical computational cost with respect to the other membership functions.

Error variable

The universe of discourse of the input variable (Error) for both type I fuzzy and type II fuzzy, will have five membership functions: large negative (NG), small negative (NP), zero (ZE), small positive (PP) and large positive (PG). Figures 2.a and 2.b show the membership functions in the universe of discourse for the error variable for type I and type II fuzzy, respectively.

Figure 2

a) Universe of the Error variable for the type I fuzzy; b) Universe of the Error variable for the type II fuzzy

Source: Prepared by the authors

D.Error variable

The universe of discourse of the input variable (D.Error) for type I fuzzy as well as type II fuzzy, will have 3 membership functions: large negative (NG), zero (ZE), and large positive (PG). Figures 3.a and 3.b show the membership functions in the speech universe for the variable (D.Error) for type I and type II, respectively. This parameter will serve to a large extent to verify that the measurement system is always in good working order since when presenting a fault, the difference of the error will be significant, in the same way when there is a sudden change in its level measurement due to disturbances, etc.

Output variable

For the universe of discourse of the type I and type I fuzzy logic controllers, it will depend on a single variable that is called controlled variable. For both control strategies, the Sugeno method is used since this avoids a higher computational cost, which is essential in the control scope at the time of implementation.

Figure 3

a) Universe of the D.ERROR of the type I fuzzy; b) Universe of the D.ERROR of the type II fuzzy

Source: Prepared by the authors