This paper analyzes array pattern tolerance against excitation errors. The nonprobabilistic interval analysis algorithm is used for tolerance analysis of the nonideal uniform linear array in this work. Toward this pur...
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This paper analyzes array pattern tolerance against excitation errors. The nonprobabilistic interval analysis algorithm is used for tolerance analysis of the nonideal uniform linear array in this work. Toward this purpose, corresponding interval models of the power pattern functions are established, respectively, with the consideration of the amplitude errors, phase errors, or both simultaneously, in antenna arrays. The tolerance for the amplitude-phase error of the main function parameters including the beamwidth, sidelobe level, and the directivity is simulated by computer according to the indicators and the actual requirements. Accordingly, the worst admissible performance of an array can be evaluated, which may provide theoretical reference for optimal antenna array design. As for the problem of array synthesis in the presence of various array errors, interval analysis-convex programming (IA-CP) is presented. Simulation results show that the proposed IA-CP based synthesis technique is robust for the amplitude and phase errors.
We study the approximability of constraint satisfaction problems (CSPs) by linear programming (LP) relaxations. We show that for every CSP, the approximation obtained by a basic LP relaxation is at least as strong as ...
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We study the approximability of constraint satisfaction problems (CSPs) by linear programming (LP) relaxations. We show that for every CSP, the approximation obtained by a basic LP relaxation is at least as strong as the approximation obtained using relaxations given by c . log n/ log log n levels of the Sherali-Adams hierarchy (for some constant c > 0) on instances of size n. It was proved by Chan et al. [FOGS 2013] (and recently strengthened by Kothari et al. [STOC 2017]) that for CSPs, any polynomial-size LP extended formulation is at most as strong as the relaxation obtained by a constant number of levels of the Sherali-Adams hierarchy (where the number of levels depend on the exponent of the polynomial in the size bound). Combining this with our result also implies that any polynomial-size LP extended formulation is at most as strong as the basic LP, which can be thought of as the base level of the Sherali-Adams hierarchy. This essentially gives a dichotomy result for approximation of CSPs by polynomial-size LP extended formulations. Using our techniques, we also simplify and strengthen the result by Khot et al. [STOC 2014] on (strong) approximation resistance for LPs. They provided a necessary and sufficient condition under which o(log log n) levels of the Sherali-Adams hierarchy cannot achieve an approximation better than a random assignment. We simplify their proof and strengthen the bound to o(log n/loglog n) levels.
The problem of regional pole placement by static output feedback is studied for a class of linear descriptor systems that presents two caracteristics found in some physical models: regularity and absence of direct act...
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The problem of regional pole placement by static output feedback is studied for a class of linear descriptor systems that presents two caracteristics found in some physical models: regularity and absence of direct action of control inputs on the algebraic variables. Thus the structure of the mathematical model of this class of systems is exploited. We show, in particular, how some results from the classical theory of normal (differential) linear systems can be used to solve the considered problem. The paper provides a set of necessary and sufficient conditions to characterize the existence of an output feedback that places the finite closed-loop poles in particular regions of the complex plane. A technique based on the use of an orthogonal decomposition and on the solution of two-coupled Lyapunov equations is also proposed. A numerical example illustrates the approach.
A convex formulation is proposed for optimal energy management in aircraft with hybrid propulsion systems consisting of gas turbine and electric motor components. By combining a point-mass aircraft dynamical model wit...
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A convex formulation is proposed for optimal energy management in aircraft with hybrid propulsion systems consisting of gas turbine and electric motor components. By combining a point-mass aircraft dynamical model with models of electrical and mechanical powertrain losses, the fuel consumed over a planned future flight path is minimised subject to constraints on the battery, electric motor and gas turbine. The resulting optimisation problem is used to define a predictive energy management control law that takes into account the variation in aircraft mass during flight. A simulation study based on a representative 100-seat aircraft with a prototype parallel hybrid electric propulsion system is used to investigate the properties of the controller. We show that an optimisation-based control strategy can provide significant fuel savings over heuristic energy management strategies in this context.
For discrete-time linear parameter-varying (LPV) systems, this paper proposes a new guaranteed cost LPV output-feedback controller to minimize an upper bound of state and input energy called LQ performance under all a...
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For discrete-time linear parameter-varying (LPV) systems, this paper proposes a new guaranteed cost LPV output-feedback controller to minimize an upper bound of state and input energy called LQ performance under all admissible grades of time-varying parameters. Using the current-time and one-step-past information of the time-varying parameters, this paper designs the new controller associated with a new parameter-dependent quadratic Lyapunov function in terms of parameterized linear matrix inequalities (PLMIs).
A proximal bundle methcxi is presented for minimizing a nonsmooth convex function f. At each iteration it requires only one approximate evaluation of f and its £-subgradient, and finds a search direction via quad...
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A proximal bundle methcxi is presented for minimizing a nonsmooth convex function f. At each iteration it requires only one approximate evaluation of f and its £-subgradient, and finds a search direction via quadratic programming. When applied to Lagrangian decomposition of convex programs, it allows for inexact solutions of decomposed subproblems; yet, increasing their required accuracy automatically, it asymptotically ftnds both primal and dual solutions. Some encouraging numerical experience is reported.
In this paper, the energy management problem for an electric vehicle equipped with a range extender is addressed. For the first time, the considered global driving cost takes into account also the range extender noise...
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In this paper, the energy management problem for an electric vehicle equipped with a range extender is addressed. For the first time, the considered global driving cost takes into account also the range extender noise emissions, which can be expressed as function of the generated power. It is shown that the design of the optimal energy management policy can be formalized as a convex optimal control problem under some mild assumptions on the system dynamics. A numerical application considering an electric bus operating in a urban environment, where the noise plays a key role, illustrates the potential of the proposed approach as a tool for hybrid vehicles design.
General nonfeasible (price-coordination, interaction balance) hierarchical optimization algorithms for large-scale systems with multiple objectives are considered. The systems studied consist of connected subsystems w...
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General nonfeasible (price-coordination, interaction balance) hierarchical optimization algorithms for large-scale systems with multiple objectives are considered. The systems studied consist of connected subsystems with multiple objectives (subgoals, indicators); the overall objectives are functions of the subsystem objectives. It is shown that, unlike in the single objective case, there is no general transformation, modification, of the objective vectors of the subsystems (cf. the additional price term in the single objective case). However, a series of transformed subproblems can be defined such that the limit solution can be taken as the subsystem solution. That is, in the general case where the way the decision-maker expresses his preference is free, an additional iteration is needed in each subproblem. A multicriteria duality theory is reviewed. Based on this theory a nonfeasible algorithm is rederived, where the subproblems are solved by multicriteria methods using explicit trade-offs (such as the SWT and Geoffrion's method). The derivation using the duality theory conveniently gives us a coordination algorithm, sufficient convexity properties, and a new suboptimal stopping rule.
The Path Flow Estimator (PFE) is a software tool for estimating flows and travel times in transportation networks. It has been developed to support both on-line urban traffic management and off-line transportation pla...
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The Path Flow Estimator (PFE) is a software tool for estimating flows and travel times in transportation networks. It has been developed to support both on-line urban traffic management and off-line transportation planning. Given data from vehicle detectors and other forms of sensor, path flows and path travel times are inferred on the basis of a logit path choice model. The delays incurred by congestion are taken into account. The most significant paths in the network are generated by the iterative use of a shortest path algorithm, using link costs reduced by shadow prices incurred by active constraints (like traffic counts). In order to better represent the transitory overloads which characterise congested conditions, a time-dependent PFE has been formulated. Various versions of the PFE have been specified and are being validated for a number of European test sites.
This work tackles the problem of reconstructing vehicle trajectories with the side information of physical constraints, such as inter-vehicular distance and speed limits. It is notoriously difficult to perform a regre...
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This work tackles the problem of reconstructing vehicle trajectories with the side information of physical constraints, such as inter-vehicular distance and speed limits. It is notoriously difficult to perform a regression while enforcing these hard constraints on time intervals. Using reproducing kernel Hilbert spaces, we propose a convex reformulation which can be directly implemented in classical solvers such as CVXGEN. Numerical experiments on a simple dataset illustrate the efficiency of the method, especially with sparse and noisy data.
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