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.
This chapter addresses the scheduling problem of a sensor that constantly collects information from multiple sites. In the existing literature, the problem is solved by probabilistic approaches, potentially generating...
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ISBN:
(纸本)9783540743545
This chapter addresses the scheduling problem of a sensor that constantly collects information from multiple sites. In the existing literature, the problem is solved by probabilistic approaches, potentially generating schedules in which a site is not visited for a long time. To overcome this deficiency, this chapter presents a deterministic approach formulated as an integer linear program. Upon showing that the problem is NP-Hard, the chapter develops valid lower and upper bounds and proposes two constructive heuristic methods. Tested via an extensive computational study, the heuristic methods axe proven efficient and effective in solving the problem.
A multi-stage nonlinear model predictive controller is derived for the real-time coordination of multiple aircraft. In order to couple the versatility of hybrid systems theory with the power of NMPC, a finite state ma...
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ISBN:
(纸本)9783540726982
A multi-stage nonlinear model predictive controller is derived for the real-time coordination of multiple aircraft. In order to couple the versatility of hybrid systems theory with the power of NMPC, a finite state machine is coupled to a real time optimal control formulation. This methodology aims to integrate real-time optimal control with higher level logic rules, in order to assist mission design for flight operations like collision avoidance, conflict resolution, and reacting to changes in the environment. Specifically, the controller is able to consider new information as it becomes available. Stability properties for nonlinear model predictive control are described briefly along the lines of a dual-mode controller. Finally, a small case study is presented that considers the coordination of two aircraft, where the aircraft are able to avoid obstacles and each other, reach their targets and minimize a cost function over time.
This paper gives an overview of a framework for analyzing hybrid dynamical systems. The emphasis is on modeling assumptions that guarantee robustness. These conditions lead to a general invariance principle and to res...
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ISBN:
(纸本)9783540707004
This paper gives an overview of a framework for analyzing hybrid dynamical systems. The emphasis is on modeling assumptions that guarantee robustness. These conditions lead to a general invariance principle and to results on the existence of smooth Lyapunov functions (converse theorems) for hybrid systems. In turn, the stability analysis tools motivate novel hybrid control algorithms for nonlinear systems.
A nonlinear model predictive control (NMPC) formulation is used to prevent an exothermic fed-batch chemical reactor from thermal runaways even in the case of total cooling failure. Detailed modeling of the reaction ki...
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ISBN:
(纸本)9783540726982
A nonlinear model predictive control (NMPC) formulation is used to prevent an exothermic fed-batch chemical reactor from thermal runaways even in the case of total cooling failure. Detailed modeling of the reaction kinetics and insight into the process dynamics led to the formulation of a suitable optimization problem with safety constraints which is then successively solved within the NMPC scheme. Although NMPC control-loops can exhibit a certain degree of inherent robustness, an explicit consideration of process uncertainties is preferable not only for safety reasons. This is approached by reformulating the open-loop optimization problem as a min-max problem. This corresponds to a worst-case approach and leads to even more cautious control moves of the NMPC in the presence of uncertain process parameters. All results are demonstrated in simulations for the esterification process of 2-butyl.
This paper discusses the implementation of nonlinear model predictive control on continuous industrial polymer manufacturing processes. Two examples of such processes serve to highlight many of the practical issues fa...
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ISBN:
(纸本)9783540726982
This paper discusses the implementation of nonlinear model predictive control on continuous industrial polymer manufacturing processes. Two examples of such processes serve to highlight many of the practical issues faced and the technological solutions that have been adopted. An outline is given of the various phases of deploying such a solution, and this serves as a framework for describing the relevant modeling choices, controller structures, controller tuning, and other practical issues.
We will in this paper highlight our experience with NMPC. In our context NMPC shall mean the use of a nonlinear mechanistic model, state estimation, and the solution of an online constrained nonlinear optimisation pro...
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ISBN:
(纸本)9783540726982
We will in this paper highlight our experience with NMPC. In our context NMPC shall mean the use of a nonlinear mechanistic model, state estimation, and the solution of an online constrained nonlinear optimisation problem. Our reference base is a number of applications of NMPC in a variety of processes. We discuss the use of mechanistic models in NMPC applications and in particular the merits and drawbacks of applying such models in online applications. Further, we focus on state estimation, and the use of Kalman filters and moving horizon estimation. Finally, we consider the design of the optimization problem itself and implementation issues.
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