No Arabic abstract
A convex parameterization of internally stabilizing controllers is fundamental for many controller synthesis procedures. The celebrated Youla parameterization relies on a doubly-coprime factorization of the system, while the recent system-level and input-output characterizations require no doubly-coprime factorization but a set of equality constraints for achievable closed-loop responses. In this paper, we present explicit affine mappings among Youla, system-level and input-output parameterizations. Two direct implications of the affine mappings are 1) any convex problem in Youla, system level, or input-output parameters can be equivalently and convexly formulated in any other one of these frameworks, including the convex system-level synthesis (SLS); 2) the condition of quadratic invariance (QI) is sufficient and necessary for the classical distributed control problem to admit an equivalent convex reformulation in terms of Youla, system-level, or input-output parameters.
In this paper, we consider the systems with trajectories originating in the nonnegative orthant becoming nonnegative after some finite time transient. First we consider dynamical systems (i.e., fully observable systems with no inputs), which we call eventually positive. We compute forward-invariant cones and Lyapunov functions for these systems. We then extend the notion of eventually positive systems to the input-output system case. Our extension is performed in such a manner, that some valuable properties of classical internally positive input-output systems are preserved. For example, their induced norms can be computed using linear programming and the energy functions have nonnegative derivatives.
We consider the effect of parametric uncertainty on properties of Linear Time Invariant systems. Traditional approaches to this problem determine the worst-case gains of the system over the uncertainty set. Whilst such approaches are computationally tractable, the upper bound obtained is not necessarily informative in terms of assessing the influence of the parameters on the system performance. We present theoretical results that lead to simple, convex algorithms producing parametric bounds on the $mathcal{L}_2$-induced input-to-output and state-to-output gains as a function of the uncertain parameters. These bounds provide quantitative information about how the uncertainty affects the system.
This paper addresses problems on the structural design of control systems taking explicitly into consideration the possible application to large-scale systems. We provide an efficient and unified framework to solve the following major minimization problems: (i) selection of the minimum number of manipulated/measured variables to achieve structural controllability/observability of the system, and (ii) selection of the minimum number of feedback interconnections between measured and manipulated variables such that the closed-loop system has no structurally fixed modes. Contrary to what would be expected, we show that it is possible to obtain a global solution for each of the aforementioned minimization problems using polynomial complexity algorithms in the number of the state variables of the system. In addition, we provide several new graph-theoretic characterizations of structural systems concepts, which, in turn, enable us to characterize all possible solutions to the above problems.
We show {it semidefinite programming} (SDP) feasibility problem is equivalent to solving a {it convex hull relaxation} (CHR) for a finite system of quadratic equations. On the one hand, this offers a simple description of SDP. On the other hand, this equivalence makes it possible to describe a version of the {it Triangle Algorithm} for SDP feasibility based on solving CHR. Specifically, the Triangle Algorithm either computes an approximation to the least-norm feasible solution of SDP, or using its {it distance duality}, provides a separation when no solution within a prescribed norm exists. The worst-case complexity of each iteration is computing the largest eigenvalue of a symmetric matrix arising in that iteration. Alternate complexity bounds on the total number of iterations can be derived. The Triangle Algorithm thus provides an alternative to the existing interior-point algorithms for SDP feasibility and SDP optimization. In particular, based on a preliminary computational result, we can efficiently solve SDP relaxation of {it binary quadratic} feasibility via the Triangle Algorithm. This finds application in solving SDP relaxation of MAX-CUT. We also show in the case of testing the feasibility of a system of convex quadratic inequalities, the problem is reducible to a corresponding CHR, where the worst-case complexity of each iteration via the Triangle Algorithm is solving a {it trust region subproblem}. Gaining from these results, we discuss potential extension of CHR and the Triangle Algorithm to solving general system of polynomial equations.
This article surveys the System Level Synthesis framework, which presents a novel perspective on constrained robust and optimal controller synthesis for linear systems. We show how SLS shifts the controller synthesis task from the design of a controller to the design of the entire closed loop system, and highlight the benefits of this approach in terms of scalability and transparency. We emphasize two particular applications of SLS, namely large-scale distributed optimal control and robust control. In the case of distributed control, we show how SLS allows for localized controllers to be computed, extending robust and optimal control methods to large-scale systems under practical and realistic assumptions. In the case of robust control, we show how SLS allows for novel design methodologies that, for the first time, quantify the degradation in performance of a robust controller due to model uncertainty -- such transparency is key in allowing robust control methods to interact, in a principled way, with modern techniques from machine learning and statistical inference. Throughout, we emphasize practical and efficient computational solutions, and demonstrate our methods on easy to understand case studies.