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— We highlight an essential difference between path-following and reference-tracking for non-minimum phase systems. It is well-known that in the reference-tracking, for non-minimum phase systems, there exists a fundamental performance limitation in terms of a lower bound on the L 2-norm of the tracking error, even when the control effort is free. We show(More)
— We address the problem of position trajectory-tracking and path-following control design for underactuated autonomous vehicles in the presence of possibly large modeling parametric uncertainty. For a general class of vehicles moving in either two or three-dimensional space, we demonstrate how adaptive switching supervisory control can be combined with a(More)
— In this paper we address the actuator/sensor allocation problem for linear time invariant (LTI) systems. Given the structure of an autonomous linear dynamical system, the goal is to design the structure of the input matrix (commonly denoted by B) such that the system is structurally controllable with the restriction that each input be dedicated, i.e., it(More)
This paper addresses the problem of steering a group of vehicles along given spatial paths while holding a desired time-varying geometrical formation pattern. The solution to this problem, henceforth referred to as the coordinated path-following (CPF) problem, unfolds in two basic steps. First, a path-following (PF) control law is designed to drive each(More)
We investigate limits of performance in reference-tracking and path-following and highlight an essential difference between them. For a class of nonlinear systems, we show that in reference-tracking, the smallest achievable L2 norm of the tracking error is equal to the least amount of control energy needed to stabilize the zero-dynamics of the error system.(More)
This paper studies the problem of, given the structure of a linear-time invariant system and a set of possible inputs, finding the smallest subset of input vectors that ensures system's structural controllability. We refer to this problem as the minimum constrained input selection (minCIS) problem, since the selection has to be performed on an initial given(More)
This paper addresses the state estimation of a class of continuous-time affine systems with implicit outputs. We formulate the problem in the deterministic H∞ filtering setting by computing the value of the state that minimizes the induced L2-gain from disturbances and noise to estimation error, while remaining compatible with the past observations. To(More)
This paper addresses the state estimation of systems with perspective outputs. We derive a minimum-energy estimator which produces an estimate of the state that is " most compatible " with the dynamics, in the sense that it requires the least amount of noise energy to explain the measured outputs. Under suitable observability assumptions, the estimate(More)