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8th International Conference on Bio-Inspired systems and Signal Processing, BIOSIGNALS 2015

作者： Baldacchino, Tara Jacobs, William Anderson, Sean R. Worden, Keith Rowson, Jennifer Dynamics Research Group Department of Mechanical Engineering University of Sheffield Sheffield United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9789897580697

Improving the dexterity of active prostheses is a major research area amalgamating machine learning algorithms and biosignals. A recent research niche has emerged from this-providing proportional control to a prosthetic hand by modelling the force applied at the fingertips using surface electromyography (sEMG). The publicly released NinaPro database contains sEMG recording for 6 degree-of-freedom force activations for 40 intact subjects. In this preliminary study the authors successfully perform multivariate force regression using Bayesian mixture of experts (MoE). The accuracy of the model is compared to the benchmark set by the authors of NinaPro;comparable performance is achieved, however in this work a lower dimensional feature extraction representation obtains the best modelling accuracies, hence reducing training time. Inherent to the Bayesian framework is the inclusion of uncertainty in the model structure, providing a natural step in obtaining confidence bounds on the predictions. The MoE model used in this paper provides a powerful method for modelling force regression with application to actively controlling prosthetic and robotic arms for rehabilitation purposes, resulting in highly refined movements.

关键词： Learning algorithms

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Communication scheduling in robust self-triggered MPC for linear discrete-time systems 5

Communication scheduling in robust self-triggered MPC for li...

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5th IFAC Workshop on Distributed Estimation and control in Networked systems, NecSys 2015

作者： Brunner, Florian D. Gommans, T.M.P. Heemels, W.P.M.H. Allgöwer, Prank Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart70569 Germany Control System Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands

We consider a networked control system consisting of a physical plant, an actuator, a sensor, and a controller that is connected to the actuator and sensor via a communication network. The plant is described by a linear discrete-time system subject to additive disturbances. In order to reduce the required number of communications in the system, we propose a robust self-triggered model predictive controller based on rollout techniques that robustly asymptotically stabilizes a certain periodic sequence of sets in the state space while guaranteeing robust satisfaction of hard state and input constraints. At periodically occurring scheduling times, the self-triggered model predictive control algorithm determines the times at which the control input and plant measurement are updated in the time span until the next scheduling time. We establish a certain upper bound on the average sampling rate in the closed-loop system. Moreover, we show how increasing the asymptotic bound on the system state, which is a design parameter in the control scheme, can be used to further reduce the average number of communications in the system. © 2015, IF AC (International Federation of automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Closed loop systems

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Robust output feedback NMPC with guaranteed constraint satisfaction 8

Robust output feedback NMPC with guaranteed constraint satis...

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8th IFAC Symposium on Robust control Design, ROCOND 2015

作者： Subramanian, Sankaranarayanan Lucia, Sergio Paulen, Radoslav Engell, Sebastian Process Dynamics and Operations Group TU Dortmund Germany Otto-von-Guericke-University Magdeburg Laboratory for Systems Theory and Automatic Control Institute of Automation Engineering Germany

Nonlinear Model Predictive control (NMPC) employs a plant model to compute a sequence of optimal control inputs for a finite horizon. As, in reality, there always exists a plant-model mismatch and not all states of the plant can be measured, the NMPC scheme must be robust to plant uncertainties and to estimation errors. Different robust NMPC strategies have been proposed to deal with these uncertainties. Among them, a multi-stage NMPC, which is based upon a scenario tree of future plant evolutions, is less conservative compared to worst-case open-loop approaches because the presence of feedback at future sampling instants is explicitly considered. In multi-stage output feedback NMPC, additional scenarios are created by sampling the innovations that are used to estimate the future states of the plant along the scenario tree. In this paper, we refine our previously published approach to include state estimation errors. Moreover we extend the scheme to guarantee robust constraint satisfaction by calculating reachable sets using Taylor models. The method is demonstrated for a nonlinear chemical process example. © 2015, IFAC (International Federation of automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Robust control

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Online fault diagnosis for nonlinear power systems

Online fault diagnosis for nonlinear power systems

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作者： Pan, Wei Yuan, Ye Sandberg, Henrik Gonçalves, Jorge Stan, Guy-Bart Centre for Synthetic Biology and Innovation Department of Bioengineering Imperial College London United Kingdom Control Group Department of Engineering University of Cambridge United Kingdom Department of Automatic Control School of Electrical Engineering KTH Royal Institute of Technology Sweden Luxembourg Centre for Systems Biomedicine Luxembourg

This paper considers the problem of automatic fault diagnosis for transmission lines in large scale power networks. Since faults in transmission lines threatens stability of the entire power network, fast and reliable fault diagnosis is an important problem in transmission line protection. This work is the first paper exploiting sparse signal recovery for the fault-diagnosis problem in power networks with nonlinear swing-type dynamics. It presents a novel and scalable technique to detect, isolate and identify transmission faults using a relatively small number of observations by exploiting the sparse nature of the faults. Buses in power networks are typically described by second-order nonlinear swing equations. Based on this description, the problem of fault diagnosis for transmission lines is formulated as a compressive sensing or sparse signal recovery problem, which is then solved using a sparse Bayesian formulation. An iterative reweighted 1-minimisation algorithm based on the sparse Bayesian learning update is then derived to solve the fault diagnosis problem efficiently. With the proposed framework, a real-time fault monitoring scheme can be built using only measurements of phase angles at the buses.

关键词： Electric lines

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Network aggregative games: Distributed convergence to Nash equilibria

Network aggregative games: Distributed convergence to Nash e...

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IEEE Annual Conference on Decision and control

作者： Francesca Parise Basilio Gentile Sergio Grammatico John Lygeros Automatic Control Laboratory ETH Zurich Switzerland Control Systems group Department of Electrical Engineering Eindhoven University of Technology The Netherlands

ISBN: (纸本)9781479978878

We consider quasi-aggregative games for large populations of heterogeneous agents, whose interaction is determined by an underlying communication network. Specifically, each agent minimizes a quadratic cost function, which depends on its own strategy and on a convex combination of the strategies of its neighbors, and is subject to heterogeneous convex constraints. We suggest two distributed algorithms that can be implemented to steer the best responses of the rational agents to a Nash equilibrium configuration. The convergence of these schemes is guaranteed under different sufficient conditions depending on the matrices defining the agents' cost functions and on the communication network.

关键词： Games Aggregates Nash equilibrium Sociology Statistics Convergence Cost function

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Constrained linear quadratic deterministic mean field control: Decentralized convergence to Nash equilibria in large populations of heterogeneous agents

Constrained linear quadratic deterministic mean field contro...

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IEEE Annual Conference on Decision and control

作者： Sergio Grammatico Francesca Parise John Lygeros Control Systems group Department of Electrical Engineering Eindhoven University of Technology The Netherlands Automatic Control Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781479978878

This paper considers the linear quadratic deterministic mean field control problem for large populations of heterogeneous agents, subject to convex state and input constraints, and coupled via a quadratic cost function which depends on the average population state. To control the optimal responses of the rational agents to a Nash equilibrium, we propose feedback iterative solutions based on operator theory arguments. Contrary to the state of the art, global convergence is ensured, under mild sufficient conditions on the matrices defining the cost functions, and not on the convex constraints.

关键词： Sociology Statistics Cost function Games Nash equilibrium Aggregates Convergence

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Model Predictive control approaches for Centrifugal Compression systems

Model Predictive Control approaches for Centrifugal Compress...

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IEEE Annual Conference on Decision and control

作者： Giampaolo Torrisi Sergio Grammatico Manfred Morari Roy S. Smith Automatic Control Laboratory ETH Zurich Switzerland Control Systems group Department of Electrical Engineering Eindhoven University of Technology The Netherlands

ISBN: (纸本)9781479978878

Model Predictive control (MPC) techniques are considered for industrial centrifugal compression systems with nonlinear dynamics. We consider the torque provided by an external drive and a recycle valve as control actuators for the system. Closed-loop stability in the presence of control constraints is studied via the contractive control Lyapunov function method. We solve the nonlinear MPC problem to assess a performance benchmark, and then design a Sequential Quadratic Programming (SQP) MPC approach which is computationally affordable. We show in several numerical simulations based on a realistic centrifugal compressor case study that the SQP MPC technique outperforms the classic linearized MPC and performs similarly to the nonlinear MPC approach.

关键词： Valves Recycling Mathematical model Predictive control Torque Surges Actuators

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Communication Scheduling in Robust Self-Triggered MPC for Linear Discrete-Time systems ∗

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IFAC-PapersOnLine 2015年第22期48卷 132-137页

作者： Florian D. Brunner T.M.P. Gommans W.P.M.H. Heemels Frank Allgöwer Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 70569 Stuttgart Germany Control System Technology Group Department of Mechanical Engineering Eindhoven University of Technology The Netherlands

关键词： predictive control self-triggered control robustness

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Robust output feedback NMPC with guaranteed constraint satisfaction

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IFAC-PapersOnLine 2015年第14期48卷 326-331页

作者： Sankaranarayanan Subramanian Sergio Lucia Radoslav Paulen Sebastian Engell Process Dynamics and Operations Group TU Dortmund Otto-von-Guericke-University Magdeburg Laboratory for Systems Theory and Automatic Control Institute of Automation Engineering Germany

关键词： Predictive control Robust control Output feedback Nonlinear control systems

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On the price of being selfish in large populations of plug-in electric vehicles

On the price of being selfish in large populations of plug-i...

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IEEE Annual Conference on Decision and control

作者： Marina Gonzalez Vaya Sergio Grammatico Goran Andersson John Lygeros Power Systems Laboratory ETH Zurich Switzerland Control Systems group Department of Electrical Engineering Eindhoven University of Technology Automatic Control Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781479978878

We consider the problem of optimally scheduling the flexible electricity demand of a fleet of plug-in electric vehicles (PEVs). More specifically, we analyze the solutions of the following charging optimization problems: a) the welfare-optimal problem, where the overall system cost is minimized;b) the fleet-optimal problem, where the charging cost of the fleet as a whole is minimized by a central agent, that is the PEV aggregator;c) the selfish-optimal problem, where the noncooperative PEVs aim at minimizing their individual charging cost. For a homogenous PEV fleet and a simplified problem setup, we show that the solutions of the three different approaches correspond to different valley-filling results. A main insight is that, as the population of PEVs grows, the selfish-optimal solution converges to the welfare-optimal solution. On the other hand, we show that the centralized fleet-optimal solution of the PEV aggregation can be recovered via decentralized selfish-optimal solutions with respect to an appropriate price signal as the population size grows. Finally, we demonstrate our technical results on a realistic PEV fleet case study.

关键词： Electric vehicles Fleet population size optimal solution Prices

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