Abstract
In this paper, we formulate a general class of Adaptive FSI (full-state information) optimal control problems, and develop a constructive method to solve it. An Adaptive FSI problem is defined in terms of state dynamics which are linear in the unknown constant parameters and additive state disturbances, but nonlinear otherwise, and a soft-constrained performance function that is quadratic in disturbances and the unknown parameters, but non-quadratic otherwise. In this formulation, the controller is assumed to have access to the current and past values of both the state and its derivative. Using the cost-to-come method, we show that the original problem with partial information can be converted into an equivalent full information (FI) minimax control problem of higher dimension, which can be solved using Dynamic Programming (DP) methods. Both the finite-and-infinite-horizon problems are considered, and in each case, a set of necessary and sufficient conditions are obtained. The methodology is illustrated on a numerical example.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2839-2844 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 3 |
| State | Published - Dec 1 1994 |
| Event | Proceedings of the 2nd IEEE International Symposium on Requirements Engineering - York, Engl Duration: Mar 27 1995 → Mar 29 1995 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
Cite this
Minimax adaptive control of uncertain plants. / Didinsky, Garry; Basar, M Tamer.
In: Proceedings of the IEEE Conference on Decision and Control, Vol. 3, 01.12.1994, p. 2839-2844.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Minimax adaptive control of uncertain plants
AU - Didinsky, Garry
AU - Basar, M Tamer
PY - 1994/12/1
Y1 - 1994/12/1
N2 - In this paper, we formulate a general class of Adaptive FSI (full-state information) optimal control problems, and develop a constructive method to solve it. An Adaptive FSI problem is defined in terms of state dynamics which are linear in the unknown constant parameters and additive state disturbances, but nonlinear otherwise, and a soft-constrained performance function that is quadratic in disturbances and the unknown parameters, but non-quadratic otherwise. In this formulation, the controller is assumed to have access to the current and past values of both the state and its derivative. Using the cost-to-come method, we show that the original problem with partial information can be converted into an equivalent full information (FI) minimax control problem of higher dimension, which can be solved using Dynamic Programming (DP) methods. Both the finite-and-infinite-horizon problems are considered, and in each case, a set of necessary and sufficient conditions are obtained. The methodology is illustrated on a numerical example.
AB - In this paper, we formulate a general class of Adaptive FSI (full-state information) optimal control problems, and develop a constructive method to solve it. An Adaptive FSI problem is defined in terms of state dynamics which are linear in the unknown constant parameters and additive state disturbances, but nonlinear otherwise, and a soft-constrained performance function that is quadratic in disturbances and the unknown parameters, but non-quadratic otherwise. In this formulation, the controller is assumed to have access to the current and past values of both the state and its derivative. Using the cost-to-come method, we show that the original problem with partial information can be converted into an equivalent full information (FI) minimax control problem of higher dimension, which can be solved using Dynamic Programming (DP) methods. Both the finite-and-infinite-horizon problems are considered, and in each case, a set of necessary and sufficient conditions are obtained. The methodology is illustrated on a numerical example.
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M3 - Conference article
AN - SCOPUS:0028738651
VL - 3
SP - 2839
EP - 2844
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
SN - 0191-2216
ER -