This paper proposes a distributed controller to optimize a sum of state-dependent vector objective functions for multi-Autonomous Underwater Vehicle (AUV) systems. In particular, each AUV has a local ...private objective and can only interact with its neighboring AUVs, which are described by undirected graphs, to measure relative states. Using 6 degree of freedom (DOF) AUV equations of motions, we design a distributed controller by integrating both the consensus and optimization algorithms to the dynamics of AUVs. If the undirected communication graph is connected, we adopt a Lyapunov functional candidate to show that each AUV eventually approaches the same optimal position with a desired attitude. Moreover, simulations with the Remote Environmental Measuring UnitS (REMUS) AUVs for source-seeking tasks are included to illustrate the effectiveness of the proposed controller.
This paper proposes distributed algorithms to solve robust convex optimization (RCO) when the constraints are affected by nonlinear uncertainty. We adopt a scenario approach by randomly sampling the ...uncertainty set. To facilitate the computational task, instead of using a single centralized processor to obtain a "global solution" of the scenario problem (SP), we resort to multiple interconnected processors that are distributed among different nodes of a network to simultaneously solve the SP. Then, we propose a primal-dual subgradient algorithm and a random projection algorithm to distributedly solve the SP over undirected and directed graphs, respectively. Both algorithms are given in an explicit recursive form with simple iterations, which are especially suited for processors with limited computational capability. We show that, if the underlying graph is strongly connected, each node asymptotically computes a common optimal solution to the SP with a convergence rate <inline-formula><tex-math notation="LaTeX">O(1/(\sum _{t=1}^k\zeta ^t))</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">\lbrace \zeta ^t\rbrace</tex-math></inline-formula> is a sequence of appropriately decreasing stepsizes. That is, the RCO is effectively solved in a distributed way. The relations with the existing literature on robust convex programs are thoroughly discussed and an example of robust system identification is included to validate the effectiveness of our distributed algorithms.
This paper studies the cooperative source seeking problem via a networked multi-vehicle system. In contrast to existing literature, the multi-vehicle system is controlled to the source position that ...maximizes aggregated multiple unknown scalar fields and each sensor-enabled vehicle only samples measurements of one scalar field. Thus, a single vehicle is unable to localize the source and has to cooperate with its neighboring vehicles. By jointly exploiting the ideas of the consensus algorithm and the stochastic extremum seeking (ES), this paper proposes novel distributed stochastic ES controllers, which are gradient-free and do not need any absolute information, such that the multi-vehicle system simultaneously approaches the source position. The effectiveness of the proposed controllers is proved for quadratic scalar fields. Finally, illustrative examples are included to validate the theoretical results.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The computational efficiency of the asynchronous stochastic gradient descent (ASGD) against its synchronous version has been well documented in recent works. Unfortunately, it usually works only for ...the situation that all workers retrieve data from a shared dataset. As data get larger and more distributed, new ideas are urgently needed to maintain the efficiency of ASGD for decentralized training. This article proposes a novel ASGD over decentralized datasets where each worker can only access its local privacy-preserved dataset. We first observe that due to the heterogeneity of decentralized datasets and/or workers, the ASGD will progress at wrong directions, leading to undesired solutions. To tackle this issue, we propose a decentralized asynchronous stochastic gradient descent (DASGD) method by weighting the SG via the importance sampling technique. We prove that the DASGD achieves a convergence rate of <inline-formula><tex-math notation="LaTeX">O(1/K^{\frac{1}{2}})</tex-math></inline-formula> on nonconvex training problems under mild conditions. Numerical results also substantiate the performance of the proposed algorithm.
This paper proposes an optimal mobile sensor-scheduling algorithm for recovering the failure sensors in hybrid wireless sensor networks (WSNs). To maintain a guaranteed coverage over the area of ...interest, spare mobile sensors in WSNs will be activated to replace the failure sensors. The optimal scheduling problem is formulated into two optimization problems, one of which precisely determines the minimum value of the largest distance required to travel for mobile sensors, while the other one gives the optimal dispatch for mobile sensors to minimize the total travel distance. Furthermore, a distributed suboptimal scheduling, which only requires the local matching information of mobile sensors, is developed as well. Both regular and random network topologies are provided to illustrate the proposed algorithms in the simulation.
This article proposes a flight controller for an unmanned aerial vehicle (UAV) to loiter over a ground moving target (GMT). We are concerned with the scenario that the stochastically time-varying ...maneuver of the GMT is unknown to the UAV, which renders it challenging to estimate the GMT's motion state. Assuming that the state of the GMT is available, we first design a discrete-time Lyapunov vector field for the loitering guidance and then design a discrete-time integral sliding mode control (ISMC) to track the guidance commands. By modeling the maneuver process as a finite-state Markov chain, we propose a Rao-Blackwellised particle filter (RBPF), which only requires a few number of particles, to simultaneously estimate the motion state and the maneuver of the GMT with a camera or radar sensor. Then, we apply the principle of certainty equivalence to the ISMC and obtain the flight controller for completing the loitering task. Finally, the effectiveness and advantages of our controller are validated via simulations.
This paper investigates the asymptotic behavior of opinion dynamics in relative-opinion-dependent networks, which is motivated by the observation that a social agent tends to accept opinions close to ...itself. That is, the interpersonal influence between social agents decreases gradually in their relative opinion. It is interesting that this model can also provide a unified framework to study both the celebrated Degroot model and Deffuant-Weisbuch (DW) model. If the social network is connected, we show that the Degroot model with opinion-dependent influence asymptotically reaches consensus. The effect of the relative-opinion on the Friedkin-Johnsen (FJ) model is also illustrated via simulations.
While the techniques in optimal control theory are often model-based, the policy optimization (PO) approach directly optimizes the performance metric of interest. Even though it has been an essential ...approach for reinforcement learning problems, there is little theoretical understanding of its performance. In this article, we focus on the risk-constrained linear quadratic regulator problem via the PO approach, which requires addressing a challenging nonconvex constrained optimization problem. To solve it, we first build on our earlier result that an optimal policy has a time-invariant affine structure to show that the associated Lagrangian function is coercive, locally gradient dominated, and has a local Lipschitz continuous gradient, based on which we establish strong duality. Then, we design policy gradient primal-dual methods with global convergence guarantees in both model-based and sample-based settings. Finally, we use samples of system trajectories in simulations to validate our methods.
In this work, we study the isoline tracking problem in the GPS-denied environment where a sensing vehicle is controlled to slide on a desired isoline of a scalar field. By only using the ...strength-based measurements, our key idea lies in the design of a Proportional Integral (PI) controller to drive a new error term, which resembles the role of the sliding surface in the sliding mode control method, to zero. If such a goal is achieved, the isoline tracking task is then automatically completed, leading to our PI-like controller. We show the convergence of the closed-loop tracking system, and include both numerical simulations and real experiments to validate the performance of the proposed controllers by using various types of vehicles.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This work studies the field prediction and smoothing problems, where the spatio-temporal field in 2-D is described by a stochastic dynamical system and observed by a number of spatially deployed ...sensors. We adopt a finite-element technique to approximate the field dynamics with piece-wise Gaussian functions, leading to a high-dimensional linear stochastic system. By exploiting its sparsity, a local covariance intersection-based filter and smoother are developed in each sensor only for a moderate number of state variables via communications with nearby sensors. Such a cooperative scheme is both communication and computation efficient. We prove the uniform stability of the local filter and smoother under mild conditions, and validate their effectiveness on two application examples: the temperature prediction of a metal rod and the source localization of a PM<inline-formula><tex-math notation="LaTeX">_{2.5}</tex-math></inline-formula> field with a real dataset in a city of China.
PDF
