In this article we focus on the balanced truncation linear quadratic regulator (LQR) with constrained states and inputs. For closed-loop, we want to use the LQR to find an optimal control that minimizes the objective function which called “the quadratic cost function” with respect to the constraints on the states and the control input. In order to do that we have used formal asymptotes for the Pontryagin maximum principle (PMP) and we introduce an approach using the so called The Hamiltonian Function and the underlying algebraic Riccati equation. The theoretical results are validated numerically to show that the model order reduction based on open-loop balancing can also give good closed-loop performance. 

العنوان

Archives of Control Sciences

الناشر

Degruyter

بلد الناشر

فلسطين

Indexing

Thomson Reuters

معامل التأثير

1,559

نوع المنشور

مطبوع فقط

المجلد

29

السنة

2019

الصفحات

323–337