Journal: Report Institute of Automatic Control, ETH Zürich

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ETH Zürich, Institut für Automatik

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2012 - 20135
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Publications1 - 5 of 5
  • A Tractable Fault Detection and Isolation Approach for Nonlinear Systems with Probabilistic Performance
    Item type: Report
    Mohajerin Esfahani, Peyman; Lygeros, John (2013)
    Report Institute of Automatic Control, ETH Zürich
    The paper presents a novel perspective along with a scalable methodology to design a fault detection and isolation (FDI) filter for high dimensional nonlinear systems. Previous approaches on FDI problems are either confined to linear systems or they are only applicable to low dimensional dynamics with specific structures. In contrast, we propose an optimization-based approach to robustify a linear residual generator given some statistical information of the disturbance patterns, shifting attention from the system dynamics to the disturbance inputs. We then invoke the existing randomized techniques to provide theoretical guarantees for the performance of the proposed filter. Finally, motivated by a cyber-physical attack emanating from the vulnerabilities introduced by the interaction between IT infrastructure and power system, we deploy the developed theoretical results to diagnose and mitigate such an intrusion before the functionality of the power system is disrupted.
  • Two-stage optimization procedure of LCL filters for the design of high-efficiency grid inverters
    Item type: Report
    Mariéthoz, S.; Fischer, C. (2013)
    Report Institute of Automatic Control, ETH Zürich
    Grid inverters with resonant LCL filters allow to exchange energy with the utility grid while achieving high power quality. The design of resonant LCL filters is a challenging task because it requires considering many different constraints such as distortion, cost, losses and robustness. In terms of optimization, it is computationally complex because it involves dealing with many discrete components. The paper deals with these challenges by proposing a two stage constrained optimization procedure that allows compute efficiently the best filter that optimizes the design objective and satisfy the constraints. Given the maximum allowed distortion and budget (cost, volume, mass, etc.), the filter that minimizes the total converter system losses is found. It is shown that the losses can be substantially reduced following the proposed approach. The numerical comparison with two existing approaches shows that lower losses are achieved at equal budget equal distortion. The approach is validated experimentally.
  • On the road between robust optimization and the scenario approach for chance constrained optimization problems
    Item type: Report
    Margellos, Kostas; Goulart, Paul; Lygeros, John (2012)
    Report Institute of Automatic Control, ETH Zürich
    We propose a new method for solving chance constrained optimization problems which lies between robust (or worst-case) optimization and scenario-based methods. Our method does not require prior knowledge of the underlying probability distribution as in standard robust optimization methods, nor is it based entirely on randomization as in the scenario approach. It instead involves solving a robust optimization problem with bounded uncertainty, where the uncertainty bounds are randomized and are computed using the scenario approach. Specifically, we provide two alternatives to the standard scenario approach and show that for a given performance level, the number of scenarios is not proportional to the number of decision variables, but is proportional to the dimension of the uncertainty vector or the number of constraints respectively. Our results lead immediately to guidelines under which each of the three methods (the scenario approach and the two proposed methods) is preferable. The proposed solution methods are compared with the scenario approach by means of numerical examples.
  • Optimization Based Autonomous Racing of 1:43 Scale RC Cars
    Item type: Report
    Liniger, A.; Domahidi, A.; Morari, M. (2013)
    Report Institute of Automatic Control, ETH Zürich
    This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two- level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization based approaches to autonomous racing.
  • Distributionally robust control of constrained stochastic systems
    Item type: Report
    Van Parys, Bart P.; Kuhn, Daniel; Goulart, Paul J.; et al. (2013)
    Report Institute of Automatic Control, ETH Zürich
    We investigate the control of constrained stochastic linear systems when faced with only limited information regarding the disturbance process, i.e. when only the first two moments of the disturbance distribution are known. We consider two types of distributionally robust constraints. The constraints of the first type are required to hold with a given probability for all disturbance distributions sharing the known moments. These constraints are commonly referred to as distributionally robust chance constraints with second-order moment specifications. In the second case, we impose conditional value-at-risk (CVaR) constraints to bound the expected constraint violation for all disturbance distributions consistent with the given moment information. Such constraints are referred to as distributionally robust CVaR constraints with second-order moment specifications. We argue that the design of linear controllers for systems with such constraints is both computationally tractable and practically meaningful for both finite and infinite horizon problems. The proposed methods are illustrated for a wind turbine blade control design case study where flexibility issues play an important role, and for which distributionally robust constraints constitute sensible design objectives.
Publications1 - 5 of 5