In this note we have presented two algorithms for discrete time adaptive control which utilize prior information about the plant, including some known poles and zeros. If the plant is completely unknown. the algorithms are identical to those proposed by Goodwin el al. in [7]. However, the algorithms presented here have better transient performance and faster convergence rite when the system is partially known. In future work we show the added robustness margins obtainable from our scheme. REFERENCES E. W. Bai and S. S. S a s t r y, “Persistency of excitation sufficient richness and parameter convergence in discrete time adaptive control. ” Sysr. Contr. Lett., E. W. Bai and S. S. Sastry. “Parameter identification using prior information.” v...
The purpose of this note is to study the robustness properties of a specific adaptive pole placemen...
A computationally efficient pole-zero placement algorithm for explicit adaptive control of discrete-...
Reduction of the parameter est-mation time for an adaptive control system S Y N O P S I S The follow...
In adaptive control and online parameter estimation, recursive identification algorithms, such as Re...
In this paper, a new adaptive control framework for linear systems in which the matched uncertainty ...
The following work is concerned with the use of the Method of Least Squares in the parameter estima...
The controllability of the estimated model can be secured in a stochastic framework by a suitable mo...
Abstract—The key issue for adaptive pole-placement control of linear time-invariant systems is the p...
International audienceIn this paper, we extend convergence conditions for the parameter adaptation a...
The key issue for adaptive pole-placement control of linear time-invariant systems is the possible s...
The. problem of self-tuning reference signal tracking 1s considered for systems represented by autor...
Abstract—Two new improved recursive least-squares adap-tive-filtering algorithms, one with a variabl...
We propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal ...
This thesis studies the indirect adaptive control for discrete linear time invariant systems. The ad...
We propose a new reinforcement learning method in the framework of Recur-sive Least Squares-Temporal...
The purpose of this note is to study the robustness properties of a specific adaptive pole placemen...
A computationally efficient pole-zero placement algorithm for explicit adaptive control of discrete-...
Reduction of the parameter est-mation time for an adaptive control system S Y N O P S I S The follow...
In adaptive control and online parameter estimation, recursive identification algorithms, such as Re...
In this paper, a new adaptive control framework for linear systems in which the matched uncertainty ...
The following work is concerned with the use of the Method of Least Squares in the parameter estima...
The controllability of the estimated model can be secured in a stochastic framework by a suitable mo...
Abstract—The key issue for adaptive pole-placement control of linear time-invariant systems is the p...
International audienceIn this paper, we extend convergence conditions for the parameter adaptation a...
The key issue for adaptive pole-placement control of linear time-invariant systems is the possible s...
The. problem of self-tuning reference signal tracking 1s considered for systems represented by autor...
Abstract—Two new improved recursive least-squares adap-tive-filtering algorithms, one with a variabl...
We propose a new reinforcement learning method in the framework of Recursive Least Squares-Temporal ...
This thesis studies the indirect adaptive control for discrete linear time invariant systems. The ad...
We propose a new reinforcement learning method in the framework of Recur-sive Least Squares-Temporal...
The purpose of this note is to study the robustness properties of a specific adaptive pole placemen...
A computationally efficient pole-zero placement algorithm for explicit adaptive control of discrete-...
Reduction of the parameter est-mation time for an adaptive control system S Y N O P S I S The follow...