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Given a convex potential in a space with convex obstacles, an artificial potential is used to navigate to the minimum of the natural potential while avoiding collisions. The artificial potential combines the natural potential with potentials that repel the agent from the border of the obstacles. This is a popular approach to navigation problems because it can be implemented with spatially local information that is acquired during operation time. Artificial potentials can, however, have local minima that prevent navigation to the minimum of the natural potential. This paper derives conditions that guarantee artificial potentials to have a single minimum that is arbitrarily close to the minimum of the natural potential. The qualitative implication is that artificial potentials succeed when either the condition number—the ratio of the maximum and the minimum eigenvalue—of the Hessian of the natural potential is not large and the obstacles are not too flat, or when the destination is not close to the border of an obstacle. Numerical analyses explore the practical value of these theoretical conclusions. [ABSTRACT FROM AUTHOR]

This paper investigates the pinning control for the disturbance decoupling problem (DDP) of Boolean networks (BNs) with disturbances. First, the solvability of DDP in BCNs is defined. Then, rank-conditions-based pinning control is proposed. Moreover, rank-conditions-based pinning state feedback controllers are designed for the DDP of BNs and the range of controllers’ number is obtained. In addition, rank-conditions-based pinning output feedback controllers for the DDP of BNs are also discussed. An example is given to show the effectiveness of the obtained results. [ABSTRACT FROM PUBLISHER]

This paper considers time-average optimization, where a decision vector is chosen every time step within a (possibly nonconvex) set, and the goal is to minimize a convex function of the time averages subject to convex constraints on these averages. Such problems have applications in networking, multiagent systems, and operation research, where decisions are constrained to a discrete set and the decision average can represent average bit rates or average agent actions. This time-average optimization extends traditional convex formulations to allow a nonconvex decision set. This class of problems can be solved by Lyapunov optimization. A simple drift-based algorithm, related to a classical dual subgradient algorithm, converges to an $\epsilon$ -optimal solution within O(1/\epsilon ^2)$ time steps. Furthermore, the algorithm is shown to have a transient phase and a steady-state phase, which can be exploited to improve convergence rates to O(1/\epsilon)$ and when vectors of Lagrange multipliers satisfy locally polyhedral and locally smooth assumptions, respectively. Practically, this improved convergence suggests that decisions should be implemented after the transient period. [ABSTRACT FROM AUTHOR]

This paper investigates the state observer design of a class of unknown input switched linear systems via mode-dependent dwell time switchings. The distinguishing feature of the proposed method is that strong detectability condition of subsystems of the switched systems is unnecessarily required. First, a time-varying coordinate transformation is introduced to design a suitable reduced-order observer for each subsystem. Then, computable sufficient conditions on the synthesis of the observers are proposed in the framework of a mode-dependent dwell time technique. Since the observer of an individual subsystem cannot be designed due to unavailability of strong detectability condition of the subsystem, the state of the switched system is estimated under the condition of confining the dwell time by a pair of upper and lower bounds, restricting the growth of the Lyapunov function of the active subsystem and forcing “energy” of the overall switched system to decrease at switching instants. Finally, an example is presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we develop stochastic dissipativity theory for nonlinear dynamical systems using basic input-output and state properties. Specifically, a stochastic version of dissipativity using both an input-output as well as a state dissipation inequality in expectation for controlled Markov diffusion processes is presented. The results are then used to derive extended Kalman–Yakubovich–Popov conditions for characterizing necessary and sufficient conditions for stochastic dissipativity of stochastic dynamical systems using two-times continuously differentiable storage functions. In addition, feedback interconnection stability in probability results for stochastic dynamical systems are developed thereby providing a generalization of the small gain and positivity theorems to stochastic systems. [ABSTRACT FROM AUTHOR]

The paper deals with the problem of reconstructing the tree-like topological structure of a network of linear dynamical systems. A distance function is defined in order to evaluate the "closeness" of two processes and some useful mathematical properties are derived. Theoretical results to guarantee the correctness of the identification procedure for networked linear systems characterized by a tree topology are provided as well. The paper also suggests the approximation of a complex connected network with a tree in order to detect the most meaningful interconnections. The application of the techniques to the analysis of an actual complex network, i.e., to high frequency time series of the stock market, is extensively illustrated. [ABSTRACT FROM AUTHOR]

Inspired by the so-called `tugs" problem front mathematics, we study the geometric formations of multivehicle systems under cyclic pursuit. First, we introduce the notion of cyclic pursuit by examining a system of identical linear agents in the plane. This idea is then extends to a system of wheeled vehicles, each subject to a single nonholonomic constraint (i.e., unicycles), which is the principal focus of this paper. The pursuit framework is particularly simple in that the n identical vehicles are ordered such that vehicle i pursues vehicle i + 1 modulo n. In this paper, we as- suine each vehicle has the same constant forward speed. We show that the system's equilibrium formations are generalized regular polygons and it is exposed how the multiivehicle system's global behavior can be shaped through appropriate controller gain assignments. We then study the local stability of these equilibrium polygons, revealing which formations are stable and which are not. [ABSTRACT FROM AUTHOR]

Communication data rate and energy constraints are important factors which have to be considered when investigating distributed coordination of multi-agent networks. Although many proposed average-consensus protocols are available, a fundamental theoretic problem remains open, namely, how many bits of information are necessary for each pair of adjacent agents to exchange at each time step to ensure average consensus? In this paper, we consider average-consensus control of undirected networks of discrete-time first-order agents under communication constraints. Each agent has a real-valued state but can only exchange symbolic data with its neighbors. A distributed protocol is proposed based on dynamic encoding and decoding. It is proved that under the protocol designed, for a connected network, average consensus can be achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. An explicit form of the asymptotic convergence rate is given. It is shown that as the number of agents increases, the asymptotic convergence rate is related to the scale of the network, the number of quantization levels and the ratio of the second smallest eigenvalue to the largest eigenvalue of the Laplacian of the communication graph. We also give a performance index to characterize the total communication energy to achieve average consensus and show that the minimization of the communication energy leads to a tradeoff between the convergence rate and the number of quantization levels. [ABSTRACT FROM AUTHOR]

This paper presents a proof that existence of a polynomial Lyapunov function is necessary and sufficient for exponential stability of a sufficiently smooth nonlinear vector field on a bounded set. The main result states that if there exists an n-times continuously differentiable Lyapunov function which proves exponential stability on a bounded subset of Rn, then there exists a polynomial Lyapunov function which proves exponential stability on the same region. Such a continuous Lyapunov function will exist if, for example, the vector field is at least n-times continuously differentiable. The proof is based on a generalization of the Weier- strass approximation theorem to differentiable functions in several variables. Specifically, polynomials can be used to approximate a differentiable function, using the Sobolev norm W1∞ to any desired accuracy. This approximation result is combined with the second-order Taylor series expansion to show that polynomial Lyapunov functions can approximate continuous Lyapunov functions arbitrarily well on bounded sets. The investigation is motivated by the use of polynomial optimization algorithms to construct polynomial Lyapunov functions. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback stabilization is considered for a class of stochastic nonlinear systems, whose nonlinear terms admit an incremental rate depending only on the measured output. First, a high-gain observer is designed with a gain coming from a Riccatti equation. Second, by the backstepping approach, an output feedback controller is obtained, which guarantees global stability in probability and regulation of the state of the plant to zero with probability one. Third, an example is delivered to illustrate the proposed technique. [ABSTRACT FROM AUTHOR]

Network utility maximization (NUM) problem formulations provide an important approach to conduct network resource allocation and to view layering as optimization decomposition. In the existing literature, distributed implementations are typically achieved by means of the so-called dual decomposition technique. However, the span of decomposition possibilities includes many other elements that, thus far, have not been fully exploited, such as the use of the primal decomposition technique, the versatile introduction of auxiliary variables, and the potential of multilevel decompositions. This paper presents a systematic framework to exploit alternative decomposition structures as a way to obtain different distributed algorithms, each with a different tradeoff among convergence speed, message passing amount and asymmetry, and distributed computation architecture. Several specific applications are considered to illustrate the proposed framework, including resource-constrained and direct-control rate allocation, and rate allocation among Q0S classes with multipath routing. For each of these applications, the associated generalized NUM formulation is first presented, followed by the development of novel alternative decompositions and numerical experiments on the resulting new distributed algorithms. A systematic enumeration and comparison of alternative vertical decompositions in the future will help complete a mathematical theory of network architectures. [ABSTRACT FROM AUTHOR]

This note shows that one of the results presented by Tatikonda and Mitter in the above paper is incorrect. Moreover, we prove that any unstable discrete time linear plant affected by arbitrarily small external disturbances with probability I can be neither stabilized nor observed over any erasure channel with nonzero erasure probability. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the problem of stabilization of uncertain systems in the presence of input constraint First algebraic conditions are derived for input-to-state stability of linear system with saturated linear feedback of low dimension. Then a recursive design procedure is derived for robust stabilization of block upper triangular nonlinear systems with feedforward structure. [ABSTRACT FROM AUTHOR]

In this paper, balanced truncation of linear time-varying systems is studied in discrete and continuous time. Based on relatively basic calculations with time-varying Lyapunov equations/inequalities we are able to derive both upper and lower error bounds for the truncated models. These results generalize well-known time-invariant formulas. The case of time-varying state dimension is considered. Input-output stability of all truncated balanced realizations is also proven. The method is finally successfully applied to a high-order model. [ABSTRACT FROM AUTHOR]

In the above paper, it was claimed that the necessary and sufficient conditions for the quadratic stability and stabilization of dynamic interval systems are given in terms of linear matrix inequalities (LMIs). In this note, numerical examples are presented to show that the necessity of the conditions given in [1] does not hold. [ABSTRACT FROM AUTHOR]

A numerical example is given to show that the model reduction method of interval systems proposed by Dolgin and Zeheb in the above paper cannot guarantee the stability of the reduced-order interval models. [ABSTRACT FROM AUTHOR]