We study localization and control problems in which agent dynamics are described by difference or differential equations, while output measurements are collected at discrete times and given by finite-valued maps depending on possibly unknown landmark locations. Guided by the goal of understanding fundamental limitations imposed by such coarse measurements, we focus on characterizing indistinguishable states, i.e., agent-landmark pairs that produce identical observations under all control inputs. We show that indis-tinguishability relations can be checked automatically under mild assumptions and, being a special type of bisimulation, we develop an iterative algorithm for approximately computing them. We then introduce an analytical approach, rooted in observability theory of linear control systems, which iteratively computes a sequence of subspaces converging in finitely many steps to the indistinguishable subspace; a differential-geometric extension to nonlinear systems is also outlined.

This paper studies exponential stability of linear systems with slow and fast time variation and switching. We use averaging to eliminate the fast dynamics and only retain the slow dynamics. We then use a recent stability criterion for slowly time-varying and switched systems, combined with perturbation analysis, to prove stability of the original system. The analysis involves working with an impulsive system in new coordinates, which enables us to treat a more general class of systems compared to previous work.

In this paper, we present a quantization scheme that reconstructs the state of switched linear systems with a prescribed exponential decaying rate for the state estimation error. We show how to use the Lyapunov exponents and a geometric object called Oseledets' filtration to design such a quantization scheme. Then, we prove that this algorithm works at an average data-rate close to the estimation entropy of the given system. Furthermore, we can choose the average data-rate to be arbitrarily close to the estimation entropy whenever the switched linear system has the so-called regularity property. We show that, under the regularity assumption, the quantization scheme is completely causal in the sense that it only depends on information that is available at the current time instant. Finally, we present simulation results for a Markov Jump Linear System, a class of systems for which the realizations are known to be regular with probability 1.

This paper studies topological entropy of switched nonlinear systems. We construct a general upper bound for the topological entropy in terms of an average of the asymptotic suprema of the measures of Jacobian matrices of individual modes, weighted by the corresponding active rates. A general lower bound is constructed in terms of an active-rate-weighted average of the asymptotic infima of the traces of these Jacobian matrices. For switched systems with diagonal modes, we construct upper and lower bounds that only depend on the eigenvalues of Jacobian matrices, their relative order among individual modes, and the active rates. For both cases, we also construct more conservative upper bounds that require less information on the switching, with their relations illustrated by numerical examples of a switched Lotka-Volterra ecosystem model.

This paper studies topological entropy and stability properties of switched linear systems. First, we show that the exponential growth rates of solutions of a switched linear system are essentially upper bounded by its topological entropy. Second, we estimate the topological entropy of a switched linear system by decomposing it into a part that is generated by scalar multiples of the identity matrix and a part that has zero entropy, and proving that the overall topological entropy is upper bounded by that of the former. Third, we prove that a switched linear system is globally exponentially stable if its topological entropy remains zero under a destabilizing perturbation. Finally, the entropy estimation via decomposition and the entropy-based stability condition are applied to three classes of switched linear systems to construct novel upper bounds for topological entropy and novel sufficient conditions for global exponential stability.

We investigate the problem of constructing exponentially converging estimates of the state of a continuous-time system from state measurements transmitted via a limited-data-rate communication channel, so that only quantized and sampled measurements of continuous signals are available to the estimator. Following prior work on topological entropy of dynamical systems, we introduce a notion of estimation entropy which captures this data rate in terms of the number of system trajectories that approximate all other trajectories with desired accuracy. We also propose a novel alternative definition of estimation entropy which uses approximating functions that are not necessarily trajectories of the system. We show that the two entropy notions are actually equivalent. We establish an upper bound for the estimation entropy in terms of the sum of the system's Lipschitz constant and the desired convergence rate, multiplied by the system dimension. We propose an iterative procedure that uses quantized and sampled state measurements to generate state estimates that converge to the true state at the desired exponential rate. The average bit rate utilized by this procedure matches the derived upper bound on the estimation entropy. We also show that no other estimator (based on iterative quantized measurements) can perform the same estimation task with bit rates lower than the estimation entropy. Finally, we develop an application of the estimation procedure in determining, from the quantized state measurements, which of two competing models of a dynamical system is the true model. We show that under a mild assumption of exponential separation of the candidate models, detection is always possible in finite time. Our numerical experiments with randomly generated affine dynamical systems suggest that in practice the algorithm always works.

This paper deals with control of hybrid systems based on limited information about their state. Specifically, measurements being passed from the system to the controller are sampled and quantized, resulting in finite data-rate communication. The main ingredient of our solution to this control problem is a novel method for propagating over-approximations of reachable sets for hybrid systems through sampling intervals, during which the discrete mode is unknown. In addition, slow-switching conditions of the (average) dwell-time type and multiple Lyapunov functions play a central role in the analysis.

This paper presents a characterization of observability and an observer design method for a class of hybrid systems. A necessary and sufficient condition is presented for observability, globally in time, when the system evolves under predetermined mode transitions. A relatively weaker characterization is given for determinability, the property that concerns with recovery of the original state at some time rather than at all times. These conditions are then utilized in the construction of a hybrid observer that is feasible for implementation in practice. The observer, without using the derivatives of the output, generates the state estimate that converges to the actual state under persistent switching.

Average dwell time (ADT) properties characterize the rate at which a hybrid system performs mode switches. In this article, we present a set of techniques for verifying ADT properties. The stability of a hybrid system A can be verified by combining these techniques with standard methods for checking stability of the individual modes of A. We introduce a new type of simulation relation for hybrid automata— switching simulation —for establishing that a given automaton A switches more rapidly than another automaton B. We show that the question of whether a given hybrid automaton has ADT τ a can be answered either by checking an invariant or by solving an optimization problem. For classes of hybrid automata for which invariants can be checked automatically, the invariant-based method yields an automatic method for verifying ADT; for automata that are outside this class, the invariant has to be checked using inductive techniques. The optimization-based method is automatic and is applicable to a restricted class of initialized hybrid automata. A solution of the optimization problem either gives a counterexample execution that violates the ADT property, or it confirms that the automaton indeed satisfies the property. The optimization and the invariant-based methods can be used in combination to find the unknown ADT of a given hybrid automaton.