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In this paper, a preconditioning technique based on the null field method is presented to accelerate the convergence of fast near-linear complexity iterative Method of Moments (MoM) solution. For large-scale package-board 3D full-wave parasitic extraction, the solution time is often prohibitive for use in a design-cycle which might necessitate several analysis stages. The bottleneck is often the slow-convergence...
The problem of optimal switching and control of switching systems with nonlinear subsystems is investigated in this paper. An approximate dynamic programming-based algorithm is proposed for learning the optimal cost-to-go function based on the switching instants and the initial conditions. The global optimal switching times for every selected initial condition are directly found through the minimization...
The Hamilton-Jacobi-Bellman partial differential equation, which is needed to be solved for finite-horizon optimal control of nonlinear systems, is reduced to a state-dependent differential Riccati equation subject to a final condition through some approximations. Afterward, a method, called Finite-SDRE, is developed for finite-horizon near-optimal control synthesis. This technique allows for easier...
Fundamental to the problem of moving target tracking is the estimation of its state with respect to the sensing device(s). However, in sensor networks, often characterized by random ad hoc deployment possibly in inaccessible or hostile environment, the locations of the sensing devices are known only to a crude approximation. We propose ConSLAT, a smoothing algorithm for Simultaneous Localization and...
Approximate dynamic programming formulation (ADP) implemented with an Adaptive Critic (AC) based neural network (NN) structure has evolved as a powerful technique for solving the Hamilton-Jacobi-Bellman (HJB) equations. As interest in the ADP and the AC solutions are escalating, there is a dire need to consider enabling factors for their possible implementations. A typical AC structure consists of...
Online trained neural networks have become popular in recent years in the design of robust and adaptive controllers for dynamic systems with uncertainties due to their universal function approximation capabilities. This paper discusses a technique that dynamically reoptimizes a Single Network Adaptive Critic (SNAC) based optimal controller in the presence of unmodeled plant uncertainties. The SNAC...
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