In this paper, we demonstrate two methods for solving the inverse problem of continuous-time LQG control. This problem can be defined as: given a known LTI system with feedback controller K and Kalman gain L, can we find the weighting matrices Q,R (for state and input, respectively) and estimated noise intensities W, V (for process and measurement noise, respectively) such that the LQG control synthesis problem using these weights generates K and L? We formulate a regularized version of this problem as a minimization problem subject to a set of Linear Matrix Inequalities (LMIs). If feasible, a unique exact solution to the inverse LQR problem exists. If the LMIs are infeasible, we show a gradient descent algorithm that will find Q,R,W, and V to minimize the error in the recovered gain matrices K and L. We demonstrate these techniques through several numerical examples and formulate a human postural control case study to which we intend to apply our proposed techniques.
|Title of host publication||Industrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy|
|Publisher||American Society of Mechanical Engineers|
|Publication status||Published - 2014|
|Event||ASME 2014 Dynamic Systems and Control Conference, DSCC 2014 - San Antonio, United States|
Duration: 2014 Oct 22 → 2014 Oct 24
|Name||ASME 2014 Dynamic Systems and Control Conference, DSCC 2014|
|Other||ASME 2014 Dynamic Systems and Control Conference, DSCC 2014|
|Period||14/10/22 → 14/10/24|
Bibliographical notePublisher Copyright:
© 2014 by ASME.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering