Intelligent control mimics human intelligence in learning, decision making, and problem solving. It has practical applications in computer technology, military and aeronautical fields, and robotics. Bayesian probability and neural networks are the best-known control techniques. The need for more advanced intelligent control is growing in commercial, military, and industrial applications. Robotics and artificial intelligence are seeking a more viable control method than pre-programming. The biomimetic-bodied baby robot (CB2) is an example of robotics and AI using intelligent control.
Intelligent control is the control method that mimics human intelligence when it comes to learning, decision making and problem solving. Humans can experiment, learn, adapt and change their methods of approaching and solving problems. Computer engineers are looking for a way to recreate that natural intelligence with artificial intelligence. The practical applications for this control method are in a variety of fields including computer technology, military applications, aeronautical applications and robotics.
Although many AI approaches such as neural networks, genetic algorithms, and Bayesian probability already exist, the field of intelligent control is still developing and creating more control methods. Intelligent control is supported by computer science, mathematics, operations research and control theory, while also drawing ideas from the life sciences. The best known control techniques, however, are neural networks and Bayesian probability.
Bayesian probability is also known as probability interpretations. This control method uses mathematical algorithms to learn the problem and then applies mathematics to solve a problem. Neural networks use system identification and control theory to work. It is applied in speech recognition, image analysis and adaptive control. Perhaps the best-known application is the Xbox® Kinect, a console gaming hardware that uses video and audio sensors to make users interact with a game using their physical actions.
There is a growing need for more advanced intelligent control in commercial, military and industrial applications. Problems in these fields will always arise, hence the need for a self-organized/learning control that can address these problems on its own. A good example of practical application in intelligent control is unmanned aeronautical navigation, where unmanned aircraft learn to identify objects and avoid them. The most sought-after application for intelligent control in these fields is robotics and artificial intelligence.
The fields of robotics and artificial intelligence are most widely known for the application of intelligent control. The robots are pre-programmed with their own programming, so scientists and researchers are looking for a more viable control method than currently available. The future of the field of robotics has already been explored in science fiction, but the present is still trying to get a working artificial intelligence that doesn’t rely on pre-programmed instructions. A prime example of robotics and AI using intelligent control is the biomimetic-bodied baby robot (CB2), an android that learns through its sensors and programming to function just like a human baby can develop. It also records emotional expressions and matches them to physical sensations.
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