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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]