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Abstract We study a reaction–diffusion partial differential equation (PDE) system with a distributed input, subject to multiple unknown plant parameters with arbitrarily large uncertainties. Using Lyapunov-based techniques, we design a delay-adaptive predictor feedback controller that ensures local boundedness of system trajectories and asymptotic regulation of the closed-loop system in terms of the plant state. Specifically, we model the input delay as a one-dimensional transport PDE with a spatial variable, effectively transforming the time delay into a spatially distributed shift. For the resulting coupled transport and reaction–advection–diffusion PDE system, we employ a PDE backstepping approach combined with the certainty-equivalence principle to derive an adaptive control law that compensates for both the unknown time delay and the unknown functional parameters. Simulation results are provided to illustrate the feasibility of our control design.
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Data-Driven Superstabilizing Control of Error-in-Variables Discrete-Time Linear Systems
This paper proposes a method to find superstabilizing controllers for discrete-time linear systems that are consistent with a set of corrupted observations. The L-infinity bounded measurement noise introduces a bilinearity between the unknown plant parameters and noise terms. A superstabilizing controller may be found by solving a feasibility problem involving a set of polynomial nonnegativity constraints in terms of the unknown plant parameters and noise terms. A sequence of sum-of-squares (SOS) programs in rising degree will yield a super-stabilizing controller if such a controller exists. Unfortunately, these SOS programs exhibit very poor scaling as the degree increases. A theorem of alternatives is employed to yield equivalent, convergent (under mild conditions), and more computationally tractable SOS programs.
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- Award ID(s):
- 2208182
- PAR ID:
- 10447843
- Date Published:
- Journal Name:
- 60th IEEE Conf. Decision and Control
- Page Range / eLocation ID:
- 4924 to 4929
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC51059.2022.9992363
