Influence of Control Theory ( Feedback Control Systems and Model Predictive Control) on AI

 

Instrumentation and Control covers broad range of engineering techniques, practices covering Feedback Control System, Individual Controls loops, Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition(SCADA) and specialised large scale control systems known as DCS ( Distributed Control Systems). Sensor and Transducers along with Actuators are other components in Instrumentation.

Control Theory covers specific foundations covering controller workings, feedback control systems and advance process control. At the beginning of the computers, sometime in 1950’s under Cybernetics and Macy Conferences, when modern computer was yet to be born Norbert Weiner was the tallest name in Control Theory. Control Theory progressed well independent of modern computing. Even today control theory remains one of the best hereditary ancestor of Artificial Intelligence.

Enough has been said into the AI research about influence of psychology, cognitive biology and neuroscience on AI, at the same time control theory influence on AI has been less explored as well in research papers the paradigm is not fully exploited. Need in AI in next few years is to really manage better and better fusion of its( AI’s) ancestral genetic properties.

AI is a field created at the convergence of Philosophy, Linguistics, Computer Science, Neuroscience, Control Theory and Mathematics(plus statistics). Following diagram will give clear idea about how Control Theory is one of the ancestors of AI.


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Feedback Control Systems


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Basic components of FCS are Controller and Sensor/Transducer along-with Actuators

Feedback control systems work in various environments where the goal of FCS is to minimize energy consumption, control certain variable or process parameters in a typical plant having multiple control loops like flow, level, temperature, composition and many more variables. Control loop accepts input from sensors and transducers, process the signals in a simple manner or run some controller logic like PID ( Proportional Integral and Derivative) which generates the output signal which then is fed to actuator like control value which delivers a corrective action to get the measured variable near the setpoint.

FCS works in a manner where controller tries to minimize the error between Measured Output and Set point.

Model Predictive Control


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MPC works on creating model of plant along with calculating predicted error. This error is minimized by the algorithm to come up with optimal values.

This is part 1 of the series on how Control Theory Influenced AI just to get high level ideas. More on this in next few weeks.

Intelligence Agent in AI and MPC

MPC compares very well with the classical AI concept like below where goal or utility oriented intelligent AI . MPC also emulates the model just like the agent in below example. MPC creates ability to compare model with real data and come up with corrective set of actions to modify agent behaviour.


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Simple AI system works with Agent encapsulating Actuators, Sensor continually maintaining state and coming up with actions in interacting with environment. MPC is combined in the agent where the question is asked about “What the world is like now?”. Environment is equivalent to Plant when comparing with MPC and Intelligent agent in AI.

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