Linear-quadratic optimal sampled-data control problems: Convergence result and Riccati theory

We consider linear-quadratic optimal sampled-data control problems, where the state evolves continuously in time according to a linear control system and the control is sampled, i.e., is piecewise constant over a subdivision of the time interval, and the cost is quadratic. As a first result, we prove that, as the sampling periods tend to zero, the optimal sampled-data controls converge pointwise to the optimal permanent control. Then, we extend the classical Riccati theory to the sampled-data control framework, by developing two different approaches: the first one uses a recently established version of the Pontryagin maximum principle for optimal sampled-data control problems, and the second one uses an adequate version of the dynamic programming principle. In turn, we obtain a closed-loop expression for optimal sampled-data controls of linear-quadratic problems.