In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise.
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March 2012
Research Papers
Probabilistic Control for Uncertain Systems
Randa Herzallah
Randa Herzallah
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Randa Herzallah
J. Dyn. Sys., Meas., Control. Mar 2012, 134(2): 021018 (7 pages)
Published Online: January 12, 2012
Article history
Received:
May 1, 2010
Revised:
September 11, 2011
Accepted:
September 13, 2011
Online:
January 12, 2012
Published:
January 12, 2012
Citation
Herzallah, R. (January 12, 2012). "Probabilistic Control for Uncertain Systems." ASME. J. Dyn. Sys., Meas., Control. March 2012; 134(2): 021018. https://doi.org/10.1115/1.4005370
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