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Model-based and Model-free Designs for an Extended Continuous-time LQR with Exogenous Inputs

Journal Article · · Systems and Control Letters
 [1];  [2];  [3]
  1. BATTELLE (PACIFIC NW LAB)
  2. Oklahoma State University
  3. 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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