A New Approach to Multi-Model Adaptive Control
dc.contributor.author | Shahab, Mohamad T. | |
dc.date.accessioned | 2020-04-20T19:37:40Z | |
dc.date.available | 2020-04-20T19:37:40Z | |
dc.date.issued | 2020-04-20 | |
dc.date.submitted | 2020-04-17 | |
dc.description.abstract | Adaptive control is an approach used to deal with systems with uncertain or time-varying parameters. A classical adaptive controller typically consists of a linear time-invariant (LTI) control law together with a tuning mechanism which adjusts its parameters. Usually, though not exclusively, discrete-time adaptive controllers provide only asymptotic stability and possibly bounded-noise bounded-state stability; neither exponential stability nor a bounded noise gain is typically proven. Recently it has been shown that if we employ a parameter estimator based on the original Projection Algorithm together with projecting the parameter estimates onto a given compact and convex set, then the adaptive controller guarantees linear-like closed-loop behavior: exponential stability, a bounded noise gain and a convolution bound on the exogenous inputs. In this thesis, the overarching objective is to show that we can prove these same desirable linear-like properties in a wide range of adaptive control problems without the convexity assumption: the main idea is to use multiple estimators and a switching algorithm. Indeed, we show that those properties arise in a surprisingly natural way. We first prove a general result that exponential stability and a convolution bound on the closed-loop behavior can be leveraged to show tolerance to a degree of time-variations and unmodelled dynamics, i.e. such closed-loop properties guarantee robustness. After reviewing the original Projection Algorithm and introducing the reader to our slightly revised version, we turn our attention to controller design, with a focus on a non-convex set of plant uncertainty. As a starting point, we first consider first-order plants incorporating a simple switching algorithm. We then extend the approach to a class of nonlinear plants (which have stable zero dynamics); we consider both cases of convex and non-convex sets of parameter uncertainty. Afterwards, we turn to possibly non-minimum phase LTI plants; first we consider the stabilization problem for which we have two convex sets of uncertainty; then, we turn to the problem of tracking the sum of a finite number of sinusoids of known frequencies subject to an unknown plant order and a general compact set of uncertainty. | en |
dc.identifier.uri | http://hdl.handle.net/10012/15766 | |
dc.language.iso | en | en |
dc.pending | false | |
dc.publisher | University of Waterloo | en |
dc.subject | adaptive control | en |
dc.subject | switching adaptive control | en |
dc.subject | exponential stability | en |
dc.subject | robust adaptive control | en |
dc.subject | control theory | en |
dc.subject | projection algorithm | en |
dc.subject | multi-model | en |
dc.subject.lcsh | Adaptive control systems | en |
dc.subject.lcsh | Control theory | en |
dc.title | A New Approach to Multi-Model Adaptive Control | en |
dc.type | Doctoral Thesis | en |
uws-etd.degree | Doctor of Philosophy | en |
uws-etd.degree.department | Electrical and Computer Engineering | en |
uws-etd.degree.discipline | Electrical and Computer Engineering | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws.contributor.advisor | Miller, Daniel | |
uws.contributor.affiliation1 | Faculty of Engineering | en |
uws.peerReviewStatus | Unreviewed | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.scholarLevel | Graduate | en |
uws.typeOfResource | Text | en |
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