Phase mixing of Alfvén waves is one of the most promising mechanisms for heating of the solar atmosphere. The damping of waves in this case requires small transversal scales, relative to the magnetic field direct...
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The present survey paper provides a historical overview of the method of passification and its applications to nonlinear and adaptive control problems from 1980 to present days.
ISBN:
(数字)9783907144022
ISBN:
(纸本)9781728188133
The present survey paper provides a historical overview of the method of passification and its applications to nonlinear and adaptive control problems from 1980 to present days.
There is an increasing need for effective control of systems with complex dynamics, particularly through data-driven approaches. System Level Synthesis (SLS) has emerged as a powerful framework that facilitates the co...
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In this paper, we consider a multi-objective optimization problem over networks in which agents aim to maximize their own objective function, while satisfying both local and coupling constraints. This set up includes,...
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In this paper, we consider a multi-objective optimization problem over networks in which agents aim to maximize their own objective function, while satisfying both local and coupling constraints. This set up includes, e.g., the computation of optimal steady states in multi-agent controlsystems. Since fairness is a key feature required for the solution, we resort to Cooperative Game Theory and search for the Nash bargaining solution among all the efficient (or Pareto optimal) points of a bargaining game. We propose a negotiation mechanism among the agents to compute such a solution in a distributed way. The problem is reformulated as the maximization of a properly weighted sum of the objective functions. The proposed algorithm is then a two step procedure in which local estimates of the Nash bargaining weights are updated online and existing distributed optimization algorithms are applied. The proposed method is formally analyzed for a particular case, while numerical simulations are provided to corroborate the theoretical findings and to demonstrate its efficacy
The article simulates the process of self-excitation of an autonomous induction generator by the method of regression equations, which adequately describe the dependence of the time of self-excitation and the magnitud...
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Recently, a regional MPC approach has been proposed that exploits the piecewise affine structure of the optimal solution (without computing the entire explicit solution before). Here, regional refers to the idea of us...
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Model predictive control (MPC) samples a generally unknown and complicated feedback law point by point. The solution for the current state x contains, however, more information than only the optimal signal u for this ...
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The performance of controlsystems with packet loss as a result of an attack over the actuation communication channel is analysed. The operator is assumed to monitor the state of the channel by measuring the average n...
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The performance of controlsystems with packet loss as a result of an attack over the actuation communication channel is analysed. The operator is assumed to monitor the state of the channel by measuring the average number of packet losses and an attack detection criteria is established based on the statistic. The performance of the attacker is measured in terms of the increase of the linear quadratic cost function of the operator subject to a given detection constraint. Within that setting, the optimal denial of service (DoS) attack strategy is formulated for UDP-like and TCP-like communication protocols. For both communication protocols, DoS attack constructions that are independent and identically distributed (IID) are compared to those that are non-stationary. The main contributions of this paper are (i) explicit characterisation of the expected cost increase of the optimal attack constructions and the associated packet loss parameter for the IID case, (ii) proof, by example, that non-stationary random attacks outperform IID attacks in the presence of detection constraints.
Model predictive control (MPC) is based on perpetually solving optimization problems. The solution of the optimization is usually interpreted as the optimal input for the current state. However, the solution of the op...
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