A problem of synthesis of optimal measurement feedbacks for dynamical systems under uncertainty is under consideration. An online control scheme providing a guaranteed result under the worst-case conditions is described.
ISBN:
(纸本)9783540726982
A problem of synthesis of optimal measurement feedbacks for dynamical systems under uncertainty is under consideration. An online control scheme providing a guaranteed result under the worst-case conditions is described.
In this paper an internal model based approach to periodic input disturbance suppression for port-Hamiltonian systems is presented, more specifically, an adaptive solution able to deal with unknown periodic signal bel...
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ISBN:
(纸本)9783540707004
In this paper an internal model based approach to periodic input disturbance suppression for port-Hamiltonian systems is presented, more specifically, an adaptive solution able to deal with unknown periodic signal belonging to a given class is introduced. After an introductive section, the adaptive internal model design procedure is presented in order to solve the input disturbance problem. This theoretical machinery is specialized for the energy-based port-Hamiltonian framework in order to prove the global asymptotical stability of the solution. Finally, in order to clearly point out the effectiveness of the presented design procedure a tracking problem is solved for a robotic manipulator affected by torque ripples.
Motivated by recent research on multiobjective optimization, we focus on the problem of m interconnected systems characterized by multiple decision makers with limited centralized information. Two types of variables o...
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ISBN:
(纸本)9783540743545
Motivated by recent research on multiobjective optimization, we focus on the problem of m interconnected systems characterized by multiple decision makers with limited centralized information. Two types of variables occur: local variables that appear in a single component and global variables which provide the connection between the M systems and appear in all of them. From the point of view of one system, the problem is seen as optimization of local costs using local control variables coupled with global variables, subject to local constraints. This is a decomposition of the general centralized vector optimization problem into a set of decentralized cooperative optimization problems with local mathematical models, coupled through constraints. In this chapter, we provide a method for solving this problem by using a decomposition technique combined with an exact penalty method.
In the average consensus a set of linear systems has to be driven to the same final state which corresponds to the average of their initial states. This mathematical problem can be seen as the simplest example of coor...
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ISBN:
(纸本)9783540707004
In the average consensus a set of linear systems has to be driven to the same final state which corresponds to the average of their initial states. This mathematical problem can be seen as the simplest example of coordination task and in fact it can be used to model both the control of multiple autonomous vehicles which all have to be driven to the centroid of the initial positions, and to model the decentralized estimation of a quantity from multiple measure coming from distributed sensors. In general we can expect that the performance of a consensus strategy will be strongly related to the amount of information the agents exchange each other. This contribution presents a consensus strategy in which the exchanged data are symbols and not real numbers. This is based on a logarithmic quantizer based state estimator. The stability of this technique is then analyzed.
Completely centralized control of large, networked systems is impractical. Completely decentralized control of such systems, on the other hand, frequently results in unacceptable control performance. In this article, ...
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ISBN:
(纸本)9783540726982
Completely centralized control of large, networked systems is impractical. Completely decentralized control of such systems, on the other hand, frequently results in unacceptable control performance. In this article, a distributed MPC framework with guaranteed feasibility and nominal stability properties is described. All iterates generated by the proposed distributed MPC algorithm are feasible and the distributed controller, defined by terminating the algorithm at any intermediate iterate, stabilizes the closed-loop system. The above two features allow the practitioner to terminate the distributed MPC algorithm at the end of the sampling interval, even if convergence is not attained. Further, the distributed MPC framework achieves optimal systemwide performance (centralized control) at convergence. Feasibility, stability and optimality properties for the described distributed MPC framework are established. Several examples are presented to demonstrate the efficacy of the proposed approach.
Engineering and control technology have played and continue to play a major role in medicine over the past half-century, from the invention of the pacemaker in 1950 and ventricular assist devices in the 1980’s, to mo...
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Stochastic uncertainty is present in many control engineering problems, and is also present in a wider class of applications, such as finance and sustainable development. We propose a receding horizon strategy for sys...
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ISBN:
(纸本)9783540726982
Stochastic uncertainty is present in many control engineering problems, and is also present in a wider class of applications, such as finance and sustainable development. We propose a receding horizon strategy for systems with multiplicative stochastic uncertainty in the dynamic map between plant inputs and outputs. The cost and constraints are defined using probabilistic bounds. Terminal constraints are defined in a probabilistic framework, and guarantees of closed-loop convergence and recursive feasibility of the online optimization problem are obtained. The proposed strategy is compared with alternative problem formulations in simulation examples.
The Hamiltonian approach has turned out to be an effective tool for modeling, system analysis and controller design in the lumped parameter case. There exist also several extensions to the distributed parameter case. ...
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ISBN:
(纸本)9783540707004
The Hamiltonian approach has turned out to be an effective tool for modeling, system analysis and controller design in the lumped parameter case. There exist also several extensions to the distributed parameter case. This contribution presents a class of extended distributed parameter Hamiltonian systems, which preserves some useful properties of the well known class of Port controlled Hamiltonian systems with Dissipation. In addition, special ports are introduced to take the boundary conditions into account. Finally, an introductory example and the example of a piezoelectric structure, a problem with two physical domains, show, how one can use the presented approach for modeling and design.
The goal of this paper is to propose a unique vision able to frame a number of results recently proposed in literature to tackle problems of output regulation for nonlinear systems. This is achieved by introducing the...
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ISBN:
(纸本)9783540707004
The goal of this paper is to propose a unique vision able to frame a number of results recently proposed in literature to tackle problems of output regulation for nonlinear systems. This is achieved by introducing the so-called asymptotic internal model property as the crucial property which, if fulfilled, leads to the design of the regulator for a fairly general class of nonlinear systems satisfying a proper minimum-phase condition. It is shown that recent frameworks based upon the use of nonlinear high-gain and adaptive observer techniques for the regulator design can be cast in this setting. A recently proposed technique for output regulation without immersion is also framed in these terms.
The Multidimensional Assignment Problem (MAP) is a combinatorial optimization problem that arises in many important practical areas including capital investment, dynamic facility location, elementary particle path rec...
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ISBN:
(纸本)9783540743545
The Multidimensional Assignment Problem (MAP) is a combinatorial optimization problem that arises in many important practical areas including capital investment, dynamic facility location, elementary particle path reconstruction, multiple target tracking and sensor fusion. Since the solution space of the MAP increases exponentially with the problem parameters, and the problem has exponentially many local minima, only moderate-sized instances can be solved to optimality. We investigate the combinatorial structure of the solution space by extending a concept of Hamming distance. The results of numerical experiments indicate a linear trend for average Hamming distance to optimal solution for the cases where one of the parameters is fixed.
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