scholarly journals Adaptive control of single-input single-output hybrid systems possessing interacting discrete- and continuous-time dynamics

2005 ◽  
Vol 2005 (3) ◽  
pp. 299-329 ◽  
Author(s):  
M. de la Sen

This paper deals with the problem of synthesizing a robust adaptive controller for a specific class of single-input single-output (SISO) time-invariant hybrid controlled object (plant) which can operate under bounded disturbances and/or unmodeled dynamics. The hybrid plant dealt with is composed of two coupled subsystems, one of them being of continuous-time type while the other is digital. As a result there are also mixed continuous-time and discrete signals present in the system associated either with the solutions of differential equations which depend at the same time on both discrete-time and continuous-time forcing terms and on generalized difference equations associated with discretized and digital signals. The estimation algorithm is of a continuous-time nature since the plant parameter estimates are updated for all time. It also incorporates a relative adaptation dead-zone as a robust stabilization mechanism which prevents against instability in the presence of a common class of unmodeled dynamics and bounded noise.

Author(s):  
KACZOREK TADEUSZ

The realization problem for positive, continuous-time linear single-input, single-output systems with delays is formulated and solved. Sufficient conditions for the existence of positive realizations of a given proper transfer function are established. A procedure for computation of positive minimal realizations is presented and illustrated by an example.


2009 ◽  
Vol 18 (05) ◽  
pp. 993-1003
Author(s):  
BEHNAM SHAHRRAVA

An adaptive predictor that is optimal in the minimum mean-square error (MMSE) sense at each step is obtained for single-input, single-output (SISO) linear time-invariant discrete-time systems having general delay and white noise perturbation. The adaptive d-step-ahead predictor includes the uncertainty associated with the parameter and state estimates whereas conventional adaptive predictors, that are asymptotically optimal, ignore the uncertainty in the parameter estimates.


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