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Control Systems

Systems” have become a more normative word used outside university campus, research institutions etc. The word has created more solutions and problems to the society just like how the word people have been abused till now. Nevertheless, Systems view, Systems identification, Systems Integration, etc… are being used normally in and around Science & Technological forums and communities. Any Tool, Instrument created to reduce the Labour pain or make the work easier to achieve comfort or profit are closely tied with Systems Engineering.

However, before becoming a subject of science, it was primarily a subject of art that mostly deals with finding analogous patterns, behaviours, draw similarities, differences, and create models that can be shared between more than one discipline of research, that takes different interpretation of Nature's behaviour. This has a long history intertwined with Labour, Need to create new instruments & technologies, Theories, Mathematics, Social issues, Policy changes, Economics, etc…

Control Systems are Systems that are studied, used and deployed in order to control other systems which are by open default, i,e, unstudied, ungoverned. We realized that several diverse natural systems are self-adapting, non-linear & dynamic yet have their own way of sensing, feedback to self-adjust towards a stable state. Many natural ecosystems are illustrative to this. However, we are witnessing several chaotic unregulated or non-self-adapting behaviour in systems which humans have greater influence. And we have connections with everything around us. We are the influencer and get influenced by our influences temporally, indirectly and directly.

Thus control systems is an umbrella term that helps one to discuss about the kind of regulation, self-regulation, self-adaptation, self-governing, human synthesized artificial control systems to regulate other human made systems to reach a desired stable output.

This involves several interdisciplinary subjects like :

  1. Measurement & Statistics
  2. Data Collection
  3. Data Acquisition
  4. Systems Identification
  5. Control Systems Design
  6. Process identification, Modeling
  7. Design of Control Systems
  8. Mathematical & Visual tools to find Stable Regions
  9. Data Driven Modeling
  10. Supervised & Unsupervised Machine Learning
  11. Complex Systems, Chaos

We will be delving on basic Measurement & Instrumentation to start with, and how that is useful in recognizing behavior of a system (closed) through different stimuli and response observation. Further we shall see how to mathematically model systems in general - may it be mechanical, electrical, chemical, etc… and depending upon the observed number of variables the model can grow complicated to explain its behaviour we recorded earlier. From Experiment we verify the theoretical explanations, and in turn conduct the experiments further based on the proved explanation, which further influences the theory. Thus the practice becomes scientific.

More and more on trying to model a known system - even when it is simple - we did realized that understanding a systems behaviour within necessary boundaries is what we get from wishful testing, if we get what we need and if we know how to regulate the system to get the desired outcome we are happy and fulfilled in implementing the designed control system with known information. However, as the reality is not equal to the mathematical model implemented in our minds, papers, computational logic, we end up experiencing unintended behaviour at certain conditions which we earlier dictated as system's reaction to exogenous inputs or disturbances or unknown behaviour under which the system drives towards a chaotic behaviour/regime. Control systems does not help us.

We end up accepting that modeling a system within a certain boundary assumptions is not enough and start exploring the systems with several components connected with each other, while each one having its non-linear characteristics and dynamic with changing conditions, we learn how systems are much sensitive to initial conditions, driving us towards complex nature - requiring complexity theory to explain further.

csartsci.txt · Last modified: 2020/03/17 16:38 by Ganesh