Adaptive control is an approach used to deal with systems with uncertain or time-varying parameters. Recently, 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 presentation we show that we can prove these same desirable properties for various adaptive control problems without the convexity assumption using multiple models and a switching algorithm. We also show an application of this approach to controlling a rigid assembly of unmanned aerial vehicles under uncertainty.
Mohamad is a Postdoctoral Fellow at the Robotics, Intelligent Systems & Control (RISC) Lab in KAUST. He received the Ph.D. degree in Electrical and Computer Engineering from the University of Waterloo in 2020. He received the M.Sc. degree in Systems Engineering and the B.Sc. degree in Control and Instrumentation Systems Engineering, both from KFUPM, in 2007 and 2015, respectively. Between Jan 2012 and Dec 2015, he was a Research Engineer with Baker Hughes. His current research interests include adaptive control, optimization & control, and their applications to robotics.
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