Structural Controllability of Switched Continuous and Discrete Time Linear Systems

This paper explores the structural controllability of switched continuous and discrete time linear systems. It identifies a gap in the proof for a pivotal criterion for structural controllability of switched continuous time systems in the literature. To address this void, we develop novel graph-theoretic concepts, such as multi-layer dynamic graphs, generalized stems/buds, and generalized cactus configurations, and based on them, provide a comprehensive proof for this criterion. Our approach also induces a new, generalized cactus based graph-theoretic criterion for structural controllability. This not only extends Lin's cactus-based graph-theoretic condition to switched systems for the first time, but also provides a lower bound for the generic dimension of controllable subspaces (which is conjectured to be exact). Finally, we present extensions to reversible switched discrete-time systems, which lead to not only a simplified necessary and sufficient condition for structural controllability, but also the determination of the generic dimension of controllable subspaces.Comment: Submitted to a journa

Adaptive Backstepping Controller Design for Stochastic Jump Systems

In this technical note, we improve the results in a paper by Shi et al., in which problems of stochastic stability and sliding mode control for a class of linear continuous-time systems with stochastic jumps were considered. However, the system considered is switching stochastically between different subsystems, the dynamics of the jump system can not stay on each sliding surface of subsystems forever, therefore, it is difficult to determine whether the closed-loop system is stochastically stable. In this technical note, the backstepping techniques are adopted to overcome the problem in a paper by Shi et al.. The resulting closed-loop system is bounded in probability. It has been shown that the adaptive control problem for the Markovian jump systems is solvable if a set of coupled linear matrix inequalities (LMIs) have solutions. A numerical example is given to show the potential of the proposed techniques

Observability Robustness under Sensor Failures: Complexities and algorithms

The problem of determining the minimal number of sensors whose removal destroys observability of a linear time invariant system is studied. This problem is closely related to the ability of unique state reconstruction of a system under adversarial sensor attacks, and the dual of it is the inverse to the recently studied minimal controllability problems. It is proven that this problem is NP-hard both for a numerically specific system, and for a generic system whose nonzero entries of its system matrices are unknown but can take values freely (also called structured system). Two polynomial time algorithms are provided to solve this problem, respectively, on a numerical system with bounded maximum geometric multiplicities, and on a structured system with bounded matching deficiencies, which are often met by practical engineering systems. The proposed algorithms can be easily extended to the case where each sensor has a non-negative cost. Numerical experiments show that the structured system based algorithm could be alternative when the exact values of system matrices are not accessible.Comment: 8 pages, 2 figures, add some materials, fix some type error

On real structured controllability/stabilizability/stability radius: Complexity and unified rank-relaxation based methods

This paper addresses the real structured controllability, stabilizability, and stability radii (RSCR, RSSZR, and RSSR, respectively) of linear systems, which involve determining the distance (in terms of matrix norms) between a (possibly large-scale) system and its nearest uncontrollable, unstabilizable, and unstable systems, respectively, with a prescribed affine structure. This paper makes two main contributions. First, by demonstrating that determining the feasibilities of RSCR and RSSZR is NP-hard when the perturbations have a general affine parameterization, we prove that computing these radii is NP-hard. Additionally, we prove the NP-hardness of a problem related to the RSSR. These hardness results are independent of the matrix norm used. Second, we develop unified rank-relaxation based algorithms for these problems, which can handle both the Frobenius norm and the $2$-norm based problems and share the same framework for the RSCR, RSSZR, and RSSR problems. These algorithms utilize the low-rank structure of the original problems and relax the corresponding rank constraints with a regularized truncated nuclear norm term. Moreover, a modified version of these algorithms can find local optima with performance specifications on the perturbations, under appropriate conditions. Finally, simulations suggest that the proposed methods, despite being in a simple framework, can find local optima as good as several existing methods.Comment: To appear in System & Control Letter