What is the elaboration of various control laws in comparison to each other. It means, in the level of comparison, when to take Adaptive control? when to adopt Robust control? when for Neuro-fuzzy control? when for Optimal control? when for model predictive control?
Well, that is not a "unique response" answer. The controller's design strongly depends on the requirements, knowledge, and resources of the whole system. But, considering some mild conditions, in my opinion, a good way of star answering that hard question is understanding the "The Internal Model Principle" (See https://www.control.utoronto.ca/~wonham/W.M.Wonham_IMP_20180617.pdf)
In this sense, a controller is good as it can "capture" the system's dynamics. A common practice for the controller design is using a model of the system. The parts of the system that are not included in the model, as external disturbances, unmodeled dynamics, noise, discretization effects, and other possible variations, are assumed as fulfilling some hypotheses.
Then, for this case, those hypotheses on the unknown effects are used for performing a stability analysis according to the proposed framework. An example is assuming bounded uncertainty, then looking for an ultimate bound for the system's trajectories. Another example is assuming the terms of the system are all Lipschitz, then the system and the uncertainties can be "dominated" by linear control terms. Hence the design reduces to solving an LMI.
Therefore, taking care of the details, the better the model, the better the controller. However, no model is perfect. There will always be external disturbances, unmodeled dynamics, and other possible variations that the model does not include. So, a controller is good as it can incorporate the system's dynamics while attenuating the effect of the unmodeled dynamics and other sources of uncertainty.
This is a general question !!! You may see the related books in this field.
Adaptive control can be used when the parameters of the system to be controlled are being varied over time. A good controller should have the capability of handling uncertainties.
well answered by @Juan Diego Sánchez Torres , I am agree with him that there is no unique criteria. It depends upon the model, system dynamics and states primarily and depends upon the requirements, knowledge, and desires of the user. What are the objectives and task, etc., complimentary. For example robust control can be used for optimal solution. MPC is also a robust controller in some extent.
This is a multifaced question and there is no a single answer to it. Already a few solid arguments are mentioned how to select and why to select particular control algorithms, such as adaptive vs. robust vs. MPC vs. Neuro-Fuzzy. The initial question in selecting the control algorithm or designing the controller is to consider dynamics of the given system. To answer the next questions: what is the nature of the given system dynamics; what are the system requirements (responses); how fast and how smoothly the system to be controlled; as already mentioned above, what resources are available; etc.
- https://www.researchgate.net/post/Do_we_have_misunderstanding_in_control_theory
- https://www.researchgate.net/post/Non-model_based_control_or_model_free_control_Why_not_Pseudo-Non-Model_Control
- https://www.researchgate.net/post/What_do_you_think_about_universal_feedback_controller_illustrated_in_attached_video
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