Model-based and Model-free Designs for an Extended Continuous-time LQR with Exogenous Inputs
Journal Article
·
· Systems and Control Letters
- BATTELLE (PACIFIC NW LAB)
- Oklahoma State University
- North Carolina State University
We present an extended linear quadratic regulator (LQR) design for continuous-time linear time-invariant (LTI) systems in the presence of exogenous inputs. We first propose a model-based solution with cost minimization guarantees for states and inputs using dynamic programming (DP). The control law consists of a combination of the optimal state feedback and an additional optimal term dependent on the exogenous inputs. The control gains for the two components are obtained by solving a set of matrix differential equations. We provide these solutions for both finite horizons and steady-state cases. In the second part of the paper, we formulate a reinforcement learning (RL) based algorithm which does not need any model information except the input matrix, and can compute an approximate steady-state LQR gain using measurements of the states, the control inputs, and the exogenous inputs. Both model-based and data-driven optimal control algorithms are tested with a numerical example under different exogenous inputs showcasing the effectiveness of the designs.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1819881
- Report Number(s):
- PNNL-SA-156098
- Journal Information:
- Systems and Control Letters, Journal Name: Systems and Control Letters Vol. 154
- Country of Publication:
- United States
- Language:
- English
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