1.5 KiB
| title | allDay | date | endDate | completed | type |
|---|---|---|---|---|---|
| State of the Art | true | 2024-10-03 | 2024-10-10 | null | single |
1.25 Pages TARGET: ~700 words
Outline
- Robust Control
- How is robust control validation done?
- How do people generate unstructured perturbations?
Take 1
Attempt
Robust control as a field determines how resilient a control system is to a difference in plant dynamics for a given characteristic. In a real system, there will always be some inaccuracy in the model of plant dynamics, disturbances, or other noise. These unmodeled features will affect plant behavior if they are not anticipated. Robust control gives us tools to design for these perturbations proactively. We can design characteristics such as performance and stability to guarantee as 'robust'.
Robustness is dependent on two features: the characteristic to be guaranteed, and the set of reasonably possible perturbed plants \mathcal{P}. Usually the characteristic we're interested in is internal stability or performance. The possible set of plants, however, is less straightforward. The set \mathcal{P} can be structured or unstructured. A structured set in this instance can be a discrete number of possible perturbed plants, or possibly a parametric study with a finite number of parameters. Let's consider an example.
Suppose a plant representing a spring-mass-damper system is described as follows:
P = \frac{X(s)}{F(s)} = \frac{1}{ms^2 + bs +k}
(The disk multiplicative perturbation)
(Explain how actually getting to W_2 isn't really trivial).