What is intelligent control?

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

What is intelligent control?

Intelligent control is a class of control techniques that utilize various artificial intelligence computing approaches to emulate important characteristics of human intelligence. These techniques include neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation, and genetic algorithms.

The goal of intelligent control is to build systems that can reason, learn, and act autonomously. These systems are designed to replace a human who performs a control task, or to emulate biological intelligence. They are capable of making decisions and taking actions based on their understanding, and can handle unexpected situations and safely recover from errors.

Intelligent control systems comprise sensing elements, actuators, control algorithms, and decision-making modules, all integrated into a cohesive system. They leverage AI and machine learning techniques to analyze data, make intelligent decisions, and optimize processes based on changing conditions.

Applications of intelligent control are diverse and expanding. They are used in various fields including industrial automation, robotics, aeronautics, and electrical engineering. For instance, in electrical engineering, intelligent control systems are revolutionizing power distribution and management in smart grids, and are instrumental in integrating renewable energy sources. In industrial automation, intelligent control technology is adopted to automatically obtain operation information of equipment based on intelligent control systems.

In terms of development, although there are already many artificial intelligence approaches like neural networks, genetic algorithms, and Bayesian probability, the field of intelligent control is still evolving and creating more control methods. Future research directions include interdisciplinary approaches, scalability, and security and privacy.

How does intelligent control differ from traditional control systems?

Intelligent control systems differ from traditional control systems in several key ways:

  1. Learning and Adaptability — Intelligent control systems exhibit learning capabilities and adaptability to varying conditions, which is not a characteristic of traditional control systems. They can learn from their environment and improve their performance over time, which is not possible with traditional control systems that follow predefined rules.

  2. Handling of Complex Systems — Intelligent control systems can effectively control complex systems, such as nonlinear, fast time-varying, complex multi-variable, and systems with environmental disturbances. They have strong fault tolerance and can solve generalized problems. Traditional control systems, on the other hand, often struggle with these types of complex systems.

  3. Model Dependence — Traditional control methods rely heavily on the model of the system they are controlling. In contrast, intelligent control systems can solve control problems of non-modeled systems. This means they can handle systems where the underlying mathematical model is unknown or too complex to be used effectively.

  4. High-Level Control — The core of intelligent control is high-level control, which can use non-mathematical generalized models and adopt multi-modal control methods combining open and closed-loop control. Traditional control systems, in contrast, typically operate at a lower level and use mathematical models for control.

  5. Performance — Intelligent control systems often outperform traditional control systems. For example, a study showed that intelligent controllers have better performance than classical controllers and are easier to design.

  6. Autonomy — Intelligent control systems are designed to achieve high-level goals autonomously, even when their components, control goals, plant models, and control laws are not completely defined. Traditional control systems, in contrast, require a clear definition of these elements and do not operate autonomously to the same extent.

Intelligent control systems offer several advantages over traditional control systems, including learning capabilities, adaptability, ability to handle complex systems, less reliance on system models, high-level control, better performance, and greater autonomy. However, the choice between intelligent control and traditional control will depend on the specific requirements of the system to be controlled.

What are some examples of intelligent control systems?

Intelligent control systems are widely used across various industries and applications. Here are some examples:

  1. Neural Network Control — Neural networks have been used to solve problems in almost all spheres of science and technology. They involve two steps: system identification and control. Feedforward networks with nonlinear, continuous, and differentiable activation functions have universal approximation capability. Recurrent networks have also been used for system identification.

  2. Servo Systems — Intelligent control is widely used in servo systems, which include expert control, fuzzy control, learning control, neural network control, and predictive control methods. These systems can handle complex tasks and have strong fault tolerance.

  3. Manufacturing Process Control — Intelligent control systems can optimize manufacturing processes, making them more efficient and reducing the need for human intervention. These systems can make decisions and take actions based on their understanding of the process.

  4. Robot Machine Loading/Unloading — Intelligent control systems are used in robotics for tasks like machine loading and unloading at a milling workstation. These systems can autonomously achieve high-level goals.

  5. Autonomous Undersea Vehicles — Intelligent control systems are used in multiple autonomous undersea vehicles. These systems can handle complex tasks and adapt to changing conditions.

  6. NASA Space Station Telerobotics — Intelligent control systems are used in NASA's space station telerobotics. These systems can handle complex tasks and adapt to changing conditions.

  7. Coal Mine Automation — Intelligent control systems are used in coal mine automation. These systems can handle complex tasks and adapt to changing conditions.

  8. Army Unmanned Land Vehicles — Intelligent control systems are used in army unmanned land vehicles. These systems can handle complex tasks and adapt to changing conditions.

These examples illustrate the versatility and adaptability of intelligent control systems, making them suitable for a wide range of applications.

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