# What is a fuzzy control system?

by Stephen M. Walker II, Co-Founder / CEO

## What is a fuzzy control system?

A fuzzy control system is an artificial intelligence technique that uses fuzzy logic to make decisions and control systems. Unlike traditional control systems, which rely on precise mathematical models and algorithms, fuzzy control systems use approximate or "fuzzy" rules to make decisions based on imprecise or incomplete information.

Fuzzy control systems are particularly useful in situations where it is difficult to define exact rules or parameters for decision-making, such as in complex or dynamic environments. They can be used in a wide range of applications, including robotics, automotive engineering, and medical diagnosis.

In a fuzzy control system, inputs are converted into linguistic variables using membership functions, which assign a degree of truth to each input value. These values are then processed using fuzzy rules, which combine the inputs in various ways to produce an output. The output is then converted back into a numerical value using defuzzification techniques.

## How does a fuzzy control system work?

A fuzzy control system is a control system based on fuzzy logic, a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).

The fuzzy control system consists of three main components:

1. Fuzzification — This is the process of converting a crisp input value to a fuzzy value. The input variables in a fuzzy control system are generally mapped by sets of membership functions known as "fuzzy sets". The fuzzifier transforms the physical values as well as the error signals to a normalized fuzzy subset.

2. Fuzzy Rule base and Interfacing engine — This is the knowledge base that stores the membership functions and the fuzzy rules. The fuzzy control rules, basically the IF-THEN rules, can be best utilized in designing a controller. The rules are evaluated using techniques such as approximate reasoning or interpolative reasoning.

3. Defuzzification — The defuzzifier converts the fuzzy quantities into crisp quantities from an inferred fuzzy control action. This is necessary because, for control purposes, a crisp control signal is required.

The fuzzy control system works by mapping sensor or other inputs, such as switches, thumbwheels, and so on, to the appropriate membership functions and truth values. This is done in the input stage. The processing stage then applies the fuzzy logic rules to the inputs and derives a fuzzy output. Finally, the output stage converts the fuzzy output back into a crisp value, which can then be used to control the system.

Fuzzy control systems are particularly useful in situations where the model of the system is complex or not well understood, or when the system is nonlinear or has many inputs and outputs. They are widely used in various control applications, including automotive systems, domestic appliances, and industrial process control.

## What are the benefits of using a fuzzy control system?

The advantages of fuzzy control systems lie in their robustness, versatility, tolerance for uncertainty, ease of understanding, effectiveness with nonlinear systems, ability to manage smooth transitions, problem-solving capabilities, and wide range of applications.

1. Robustness — Fuzzy control systems can handle imprecise, vague, distorted, or imprecise data, making them robust even when precise inputs are not available.

2. Versatility — They can accommodate various types of inputs and are extensively used for practical and commercial purposes, including controlling machines and consumer goods.

3. Tolerance for Uncertainty — Fuzzy logic deals effectively with uncertainty in engineering and design processes. It might not provide precise reasoning, but it offers acceptable reasoning.

4. Ease of Understanding — Fuzzy logic relies on logical sets and reasoning, which can be easily understood by users, making it user-friendly.

5. Effective with Nonlinear Systems — Fuzzy control systems are particularly effective with nonlinear systems that are hard to model. They outperform traditional PID controllers in such scenarios because they don't have an integral accumulator to wind up.

6. Smooth Transitions — Fuzzy logic controllers have an inherent capability to manage transitions seamlessly, which can prevent sudden jolts or shocks, enhancing overall control performance and safety.

7. Problem Solving — Fuzzy logic can solve complex problems that cannot be easily solved by other systems. It can provide accurate outputs even with imprecise or inaccurate data.

8. Applications in Various Fields — Fuzzy control systems are used in a wide range of applications, from gear selection in automobiles based on factors like road conditions, engine load, and driving style, to managing the altitude of satellites and spacecraft in aerospace. They're also used in process industries for controlling pH and conductivity, and in neural networks to make decisions faster.

## What are some of the challenges associated with fuzzy control systems?

Fuzzy control systems, while offering numerous advantages, also come with certain disadvantages:

1. Inaccuracy — Fuzzy control systems often work on inaccurate data and inputs, which can compromise the accuracy of the results.

2. Lack of Systematic Approach — There is no single systematic approach to solve a problem using fuzzy logic. This can make the implementation of fuzzy logic difficult and complex.

3. Validation and Verification Challenges — The validation and verification of a fuzzy information-based system require extensive testing with hardware. This can be time-consuming and resource-intensive.

4. Difficulty in Setting Accurate Rules and Membership Functions — Setting accurate fuzzy rules and membership functions can be a challenging task. This is because the rules and functions need to be carefully designed to accurately represent the problem at hand.

5. Limited Acceptance — Due to the inaccuracy in results, fuzzy control systems are not always widely accepted. This can limit their application in certain fields or industries.

6. Complexity in Implementation — Sometimes, the implementation of fuzzy logic can be difficult due to its inherent complexity and the need for a deep understanding of the system and its variables.

Despite these disadvantages, it's important to note that fuzzy control systems are still widely used in various fields due to their ability to handle uncertainty and imprecision, and their robustness in dealing with complex, nonlinear systems.

## More terms

### What is a heuristic?

A heuristic is a rule of thumb that helps us make decisions quickly and efficiently. In artificial intelligence, heuristics are used to help computers find solutions to problems faster than they could using traditional methods.

Google DeepMind is a pioneering artificial intelligence company known for its groundbreaking advancements in AI technologies. It has developed several innovative AI systems, including the renowned DeepMind AI, a learning machine capable of self-improvement over time. DeepMind Technologies is also actively involved in the development of other AI technologies such as natural language processing and computer vision.