TY - GEN
T1 - Automatic synthesis of control alphabet policies
AU - Mavrommati, Anastasia
AU - Murphey, Todd D.
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under awards CMMI-1200321 and IIS-1426961.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/14
Y1 - 2016/11/14
N2 - This paper presents a method for synthesis of control alphabet policies, given continuum descriptions of physical systems and tasks. First, we describe a model predictive control scheme, called switched sequential action control (sSAC), that generates global state-feedback control policies with low computational cost, given a control alphabet. During synthesis, sSAC alphabet policies are directly encoded into finite state machines using a cell subdivision approach. As opposed to existing automata synthesis methods, controller synthesis is based entirely on the original nonlinear system dynamics and thus does not rely on but rather results in a lower-complexity symbolic representation. The method is validated for the cart-pendulum inversion problem and the double-tank system. The approach presents an opportunity for real-time task-oriented control of complex robotic platforms using exclusively sensor data with no online computation involved.
AB - This paper presents a method for synthesis of control alphabet policies, given continuum descriptions of physical systems and tasks. First, we describe a model predictive control scheme, called switched sequential action control (sSAC), that generates global state-feedback control policies with low computational cost, given a control alphabet. During synthesis, sSAC alphabet policies are directly encoded into finite state machines using a cell subdivision approach. As opposed to existing automata synthesis methods, controller synthesis is based entirely on the original nonlinear system dynamics and thus does not rely on but rather results in a lower-complexity symbolic representation. The method is validated for the cart-pendulum inversion problem and the double-tank system. The approach presents an opportunity for real-time task-oriented control of complex robotic platforms using exclusively sensor data with no online computation involved.
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U2 - 10.1109/COASE.2016.7743422
DO - 10.1109/COASE.2016.7743422
M3 - Conference contribution
AN - SCOPUS:85001114606
T3 - IEEE International Conference on Automation Science and Engineering
SP - 313
EP - 320
BT - 2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
PB - IEEE Computer Society
T2 - 2016 IEEE International Conference on Automation Science and Engineering, CASE 2016
Y2 - 21 August 2016 through 24 August 2016
ER -