Nonequivalence of Controllability Properties for Piecewise Linear Markov Switch Processes
Dan Goreac

TL;DR
This paper investigates controllability properties of switch-dependent piecewise linear Markov processes, revealing that exact null-controllability is stronger than approximate null-controllability and that no hierarchy exists between approximate and exact controllability.
Contribution
It establishes a controllability metric linked to backward stochastic Riccati equations and demonstrates the non-equivalence of various controllability notions in switch processes.
Findings
Exact null-controllability induces a controllability metric.
Exact null-controllability is strictly stronger than approximate null-controllability.
No hierarchy exists between approximate and exact null-controllability.
Abstract
In this paper we study the exact null-controllability property for a class of controlled PDMP of switch type with switch-dependent, piecewise linear dynamics and multiplicative jumps. First, we show that exact null-controllability induces a con-trollability metric. This metric is linked to a class of backward stochastic Riccati equations. Using arguments similar to the euclidian-valued BSDE in [4], the equation is shown to be equivalent to an iterative family of deterministic Riccati equations that are solvable. Second, we give an example showing that, for switch-dependent coefficients, exact null-controllability is strictly stronger than approximate null-controllability. Finally, we show by convenient examples that no hierarchy holds between approximate (full) controllability and exact null-controllability. The paper is intended as a complement to [15] and [14].
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStability and Controllability of Differential Equations · Gene Regulatory Network Analysis · Stochastic processes and financial applications
