Adaptive control of a class of discrete-time nonlinear systems yielding linear-like behavior
| Autor: | Mohamad T. Shahab, Daniel E. Miller |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Adaptive control Computer science 020208 electrical & electronic engineering Stability (learning theory) Estimator 02 engineering and technology Convolution Nonlinear system 020901 industrial engineering & automation Exponential stability Control and Systems Engineering Bounded function 0202 electrical engineering electronic engineering information engineering Applied mathematics Electrical and Electronic Engineering Dykstra's projection algorithm |
| Zdroj: | Automatica. 130:109691 |
| ISSN: | 0005-1098 |
| DOI: | 10.1016/j.automatica.2021.109691 |
| Popis: | In adaptive control it is typically proven that an asymptotic form of stability holds, and that at best a bounded-noise bounded-state property is proven. Recently, however, it has been proven in a variety of scenarios that it is possible to carry out adaptive control for a linear time-invariant (LTI) discrete-time plant so that the closed-loop system enjoys linear-like behavior: exponential stability , a bounded noise gain, and a convolution bound on the exogenous signals; the key idea is to carry out parameter estimation by using the original projection algorithm together with restricting the parameter estimates to a convex set . In this paper, we extend this approach to a class of nonlinear plants and show how to carry out adaptive control so that we obtain the same desirable linear-like closed-loop properties. First, we consider plants with a known sign of the control gain; second, we consider the case when that sign is unknown, where two parameter estimators and a simple switching mechanism are used. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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