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We consider robust adaptive control designs for relative degree one, minimum phase linear systems of known high frequency gain. The designs are based on the dead-zone and projection modifications, and we compare their performance w.r.t. a worst case transient cost functional penalising the L∞ norm of the output, control and control derivative. If a bound on the L∞ norm of the disturbance is known,...
The paper addresses tracking of a piecewise constant reference for a nonlinear system subject to control and/or state constraints. The proposed controller, called dual-mode, extends to the nonlinear case an approach formerly introduced for linear systems. The dual-mode controller is based on the knowledge of the set of feasible state-setpoint pairs and operates in two different modes: as a regulator...
In this paper, a new robust higher order sliding mode controller for uncertain minimum-phase nonlinear systems is designed. The problem is solved in three steps: a) the higher order sliding mode problem is formulated in input-output term; b) the problem is viewed in uncertain linear context by considering uncertain nonlinear functions as bounded non structured parametric uncertainties; c) following...
Over-actuated linear systems with nonlinear output maps are studied, taking into account input and state constraints. Using a control Lyapunov function approach, we develop an optimizing dynamic controller that includes a dynamic reference feedforward. This allows optimizing control to be implemented with low real-time computational complexity since the optimization algorithm converges only asymptotically...
The minimum-time bounded control of linear systems is generically bang-bang and the number of switchings does not exceed the dimension of the system if the eigenvalues of the system matrix are real or if the initial condition is sufficiently close to the target. This paper extends the method of [8] for computing the switching times of time-optimal controllers to linear systems with complex poles and...
Many divergence results for sampling series are in terms of the limit superior and not the limit. This leaves the possibility of a convergent subsequence. If there exists a convergent subsequence, adaptive signal processing techniques can be used. In this paper we study sampling-based signal reconstruction and system approximation processes for the space PWπ1 of bandlimited signals with absolutely...
The approximation of linear time-invariant (LTI) systems by sampling series is an important topic in signal processing. However, the convergence of the approximation series is not guaranteed: there exist stable LTI systems and bandlimited input signals such that the approximation series diverges, regardless of the oversampling factor and the sampling pattern. Recently, it has been shown that this...
This paper presents a new nonlinear bilateral control method for telerobotics based on the state convergence framework. Considering the nonlinear systems of the master and slave, the exact linearization is applied in order to obtain the equivalent linear systems. This way, the convergence methodology is used to design the control scheme considering the equivalent linear systems. Then, the control...
A method for solving the linear quadratic problem of Markov jump linear systems is developed in this paper, relying on the assumption of weak detectability. This concept of detectability generalizes previous concepts relevant to this class of systems, and most importantly, it allows us to revisit the quadratic control problem. In the main result of the paper, we show for weakly detectable systems...
In this paper, a procedure for obtaining approximate solutions to H2/H∞ problems is described which involves solving a sequence of auxiliary Hi problems with a single weighted H2 constraint. This procedure hinges upon a weighting updating scheme in which, roughly speaking, a multiplication operator is applied to an additively-corrected version of the weighting function at each step. Conditions are...
Efficiently solving a large linear system of equations, Ax = b, is still a challenging problem. Such a system appears in many applications in signal processing, especially in some problems in acoustics where we deal with very long impulse responses, i.e. x is long. In this paper, we show how to efficiently use the so-called basic iterative algorithms when the matrix A is Toeplitz, symmetric, and positive...
A general framework for the analysis of perfect electric conductor structures is presented; the proposed strategy is based in the decomposition of single structure into non-connected domains, whose individual analysis are used as preconditioner, obtaining good convergence properties for the iterative solution of the entire structure. Transmission conditions between the sub-domains are imposed using...
Robust pole assignment problem for linear systems with uncertainty is studied in this paper. The proposed gradient flow optimization algorithm is used to solve the Sylvester equations, in order that the close-loop control systems have the desired robust poles, namely, the uniformly asymptotically stable performance. The feedback gain matrix of the synthesized system can be derived from the gradient...
In a typical design cycle many iterations on the package-board-system layout may be performed to meet design specifications. In the process, the analysis step needs to be repeated as many times as the number of layout variants. The cost of analysis, especially if using a 3D fullwave extraction methodology, therefore becomes prohibitive for large-scale analysis in the design process. In this paper,...
In this paper, we present a non-overlapping domain decomposition method using integral equations in the frequency domain for the resolution of wave scattering from metallic objects. The objective is to use multi-domain modelling to process multi-scale structures. This method is flexible thanks to an independance between sub-domains and relying on the local resolution of these sub-domains. Moreover,...
The generalized minimum residual (GMRES) method is a popular method for solving a large-scale sparse nonsymmetric linear system of equations. On modern computers, especially on a large-scale system, the communication is becoming increasingly expensive. To address this hardware trend, a communication-avoiding variant of GMRES (CA-GMRES) has become attractive, frequently showing its superior performance...
HPCG has become a new metric for the design and ranking of HPC. By incorporating a local symmetric Gauss-Seidel preconditioned, HPCG implements the Conjugate Gradient method to solve a sparse linear system. HPCG performs poorly with irregular memory access and may consume a great deal of MPI resources when it is executed on supercomputers. This paper focuses on optimizing SpMV and the Gauss-Seidel...
In this paper, we present a new algorithm for solving linear programsthat requires only Õ(√rank(A)L) iterationswhere A is the constraint matrix of a linear program with mconstraints, n variables, and bit complexity L. Each iterationof our method consists of solving Õ(1) linear systems andadditional nearly linear time computation. Our method improves uponthe previous best iteration bounds...
Image restoration can be attributed to solving a linear systems and conjugate gradient method is an effective iteration algorithm for solving various linear systems. However the convergence rate of CGM is determined by condition number of coefficient matrix. The level1 and level2 preconditioner were used to reduce the condition number of coefficient matrix and to accelerate the convergence rate. Simulation...
Large-scale nonlinear optimal power flow (OPF) problems have been solved lately by primal-dual interior point (IP) methods. In spite of their success, there are many situations in which IP-based OPF programs can fail to find a solution. On the other hand, with power systems operating heavily loaded there is an increasing need for globally convergent OPF solvers. Trust region schemes have been used...
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