Name
Abstract | ||
|---|---|---|
In our early work, we show that one way to solve a robust control problem of an uncertain system is to translate the robust control problem into an optimal control problem. If the system is linear, then the optimal control problem becomes a linear quadratic regulator (LQR) problem, which can be solved by solving an algebraic Riccati equation. In this article, we extend the optimal control approach to robust tracking of linear systems. We assume that the control objective is not simply to drive the state to zero but rather to track a non-zero reference signal. We assume that the reference signal to be tracked is a polynomial function of time. We first investigated the tracking problem under the conditions that all state variables are available for feedback and show that the robust tracking problem can be solved by solving an algebraic Riccati equation. Because the state feedback is not always available in practice, we also investigated the output feedback. We show that if we place the poles of the observer sufficiently left of the imaginary axis, the robust tracking problem can be solved. As in the case of the state feedback, the observer and feedback can be obtained by solving two algebraic Riccati equations. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1080/00207170802187239 | INTERNATIONAL JOURNAL OF CONTROL |
Keywords | Field | DocType |
robust control, optimal control, LQR problem, tracking problem, observer | Mathematical optimization,Optimal control,Separation principle,Linear system,Linear-quadratic-Gaussian control,Control theory,Riccati equation,Algebraic Riccati equation,Robust control,Linear-quadratic regulator,Mathematics | Journal |
Volume | Issue | ISSN |
82 | 3 | 0020-7179 |
Citations | PageRank | References |
7 | 0.98 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haihua Tan | 1 | 7 | 0.98 |
| Shaolong Shu | 2 | 174 | 12.13 |
| Feng Lin | 3 | 177 | 18.34 |