Abstract
Many current control systems are restricted to highly controlled environments. In complicated dynamic and unstructured environments such as autonomous vehicles, control systems must be able to deal with more and more complex state situations. In complex systems with large number of states, it is often too slow to use optimal planners and developing heuristic tactics for high level goals can be challenging. AI control is an attractive alternative to traditional control architectures due to their capability to approximate optimal solutions in high dimensional state spaces without requiring a human-designed heuristic. Explainable AI control attempts to produce a human readable control command which is both interpretable and manipulable. This paper is an attempt to propose an architecture for explainable AI control in edge-cloud environment in which there are connected autonomous agents that need to be controlled. In this architecture the designed controller is distributed across the edge and cloud platform using explainable AI. This architecture could be introduced as Internet of Control Systems (IoCS), which could be applied as distributed tactics to control of connected autonomous agents. The IoCS attempts to unleash AI services using resources at the edge near the autonomous agents and make intelligent edge for dynamic, adaptive, and optimized AI control.
Highlights
What is going on in the world of control systems today?New methods of designing control systems are continuously being developed and engineered
The first ideas of Artificial Intelligence (AI) in control started with three big conferences in the 1950s, but the first type of artificial intelligence, called Bayesian learning, was never fully developed until the 1980s [5]
Some methods of machine learning are called memory-based learning, case-based reasoning, decision trees, neural networks, unsupervised learning, and reinforcement learning [5]. All these methods follow the same idea of machine learning but are done in a different mathematical or logical approach to obtain the control system [5]
Summary
New methods of designing control systems are continuously being developed and engineered. Data driven control is a new method that engineers are still trying to implement into control systems, but they have been able to develop some theories. The first ideas of AI in control started with three big conferences in the 1950s, but the first type of artificial intelligence, called Bayesian learning, was never fully developed until the 1980s [5]. Some methods of machine learning are called memory-based learning, case-based reasoning, decision trees, neural networks, unsupervised learning, and reinforcement learning [5] All these methods follow the same idea of machine learning but are done in a different mathematical or logical approach to obtain the control system [5]. Artificial intelligence in control has paved the way to new inventions and technology that have been created in the last few decades These methods of AI control have been used to make robots and computer programs have human-like abilities [5]. AI control has just started and new developments like neural networks and cognitive control are still being developed and invented
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