The paper reports on recent progress in the real-time computation of constrained closed-loop optimal control, in particular the special case of nonlinear model predictive control, of large differential algebraic equat...
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ISBN:
(纸本)9783540726982
The paper reports on recent progress in the real-time computation of constrained closed-loop optimal control, in particular the special case of nonlinear model predictive control, of large differential algebraic equations (DAE) systems arising e.g. from a MoL discretization of instationary PDE. Through a combination of a direct multiple shooting approach and an initial value embedding, a so-called "real-time iteration" approach has been developed in the last few years. One of the basic features is that in each iteration of the optimization process, new process data are being used. Through precomputation - as far as possible - of Hessian, gradients and QP factorizations the response time to perturbations of states and system parameters is minimized. We present and discuss new real-time algorithms for fast feasibility and optimality improvement that do not need to evaluate Jacobians online.
A positive dynamical system is said to be persistent if every solution that starts in the interior of the positive orthant does not approach the boundary of this orthant. For chemical reaction networks and other model...
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ISBN:
(纸本)9783540719878
A positive dynamical system is said to be persistent if every solution that starts in the interior of the positive orthant does not approach the boundary of this orthant. For chemical reaction networks and other models in biology, persistence represents a non-extinction property: if every species is present at the start of the reaction, then no species will tend to be eliminated in the course of the reaction. This paper provides checkable necessary as well as sufficient conditions for persistence of chemical species in reaction networks, and the applicability of these conditions is illustrated on some examples of relatively high dimension which arise in molecular biology. More specific results are also provided for reactions endowed with mass-action kinetics. Overall, the results exploit concepts and tools from Petri net theory as well as ergodic and recurrence theory.
This paper presents a novel approach for nonlinear model predictive control based on the concept of passivity. The proposed nonlinear model predictive control scheme is inspired by the relationship between optimal con...
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ISBN:
(纸本)9783540726982
This paper presents a novel approach for nonlinear model predictive control based on the concept of passivity. The proposed nonlinear model predictive control scheme is inspired by the relationship between optimal control and passivity as well as by the relationship between optimal control and model predictive control. In particular, a passivity-based state constraint is used to obtain a nonlinear model predictive control scheme with guaranteed closed loop stability. Since passivity and stability are closely related, the proposed approach can be seen as an alternative to control Lyapunov function based approaches. To demonstrate its applicability, the passivity-based nonlinear model predictive control scheme is applied to control a quadruple tank system.
This article discusses why novel modelling and analysis methods are required for biological systems, presents recent advances and outlines some future challenges. In this respect, the main focus is placed upon methods...
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ISBN:
(纸本)9783540719878
This article discusses why novel modelling and analysis methods are required for biological systems, presents recent advances and outlines some future challenges. In this respect, the main focus is placed upon methods for parameter estimation and sensitivity analysis as they are encountered in systems biology.
Model predictive control (MPC) is a very effective approach to control nonlinear systems, especially when the systems are high dimensional and/or constrained. MPC formulates the problem of input trajectory generation ...
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ISBN:
(纸本)9783540726982
Model predictive control (MPC) is a very effective approach to control nonlinear systems, especially when the systems are high dimensional and/or constrained. MPC formulates the problem of input trajectory generation as an optimization problem. However, due to model mismatch and disturbances, frequent re-calculation of the trajectories is typically called for. This paper proposes a two-time-scale control scheme that uses less frequent repeated trajectory generation in a slow loop and time-varying linear feedback in a faster loop. Since the fast loop reduces considerably the effect of uncertainty, trajectory generation can be done much less frequently. The problem of trajectory generation can be treated using either optimization-based MPC or flatness-based system inversion. As proposed, the method cannot handle hard constraints. Both MPC and the two-time-scale control scheme are tested via the simulation of a flying robotic structure. It is seen that the MPC scheme is too slow to be considered for real-time implementation on a fast system. In contrast, the two-time-scale control scheme is fast, effective and robust.
Markov chain Monte Carlo methods can be used to make optimal decisions in very complex situations in which stochastic effects are prominent. We argue that these methods can be viewed as providing a class of nonlinear ...
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ISBN:
(纸本)9783540726982
Markov chain Monte Carlo methods can be used to make optimal decisions in very complex situations in which stochastic effects are prominent. We argue that these methods can be viewed as providing a class of nonlinear MPC methods. We discuss decision taking by maximising expected utility, and give an extension which allows constraints to be respected. We give a brief account of an application to air traffic control, and point out some other problem areas which appear to be very amenable to solution by the same approach.
Cooperative surveillance problems require members of a team to spread out in some fashion to maximize coverage. In the case of single target surveillance, a team of UAVs angularly spaced (i.e. in the splay state confi...
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ISBN:
(纸本)9783540743545
Cooperative surveillance problems require members of a team to spread out in some fashion to maximize coverage. In the case of single target surveillance, a team of UAVs angularly spaced (i.e. in the splay state configuration) provides the best coverage of the target in a wide variety of circumstances. In this chapter we propose a decentralized algorithm to achieve the splay state configuration for a team of UAVs tracking a moving target. We derive the allowable bounds on target velocity to generate a feasible solution as well as show that, near equilibrium, the overall system is exponentially stable. Monte Carlo simulations indicate that the surveillance algorithm is asymptotically stable for arbitrary initial conditions. We conclude with high fidelity simulation tests to show the applicability of the splay state controller to actual unmanned air systems.
Model predictive control (MPC) has been a field with considerable research efforts and significant improvements in the algorithms. This has led to a fairly large number of successful industrial applications. However, ...
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ISBN:
(纸本)9783540726982
Model predictive control (MPC) has been a field with considerable research efforts and significant improvements in the algorithms. This has led to a fairly large number of successful industrial applications. However, many small and medium enterprises have not embraced MPC, even though their processes may potentially benefit from this control technology. We tackle one aspect of this issue with the development of a nonlinear model predictive control package NEWCON that will be released as free software. The work details the conceptual design, the control problem formulation and the implementation aspects of the code. A possible application is illustrated with an example of the level and reactor temperature control of a simulated CSTR. Finally, the article outlines future development directions of the NEWCON package.
Advanced model based control is a promising technology that can improve the productivity of industrial processes. In order to find its way into regular applications, advanced control must be integrated with the indust...
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ISBN:
(纸本)9783540726982
Advanced model based control is a promising technology that can improve the productivity of industrial processes. In order to find its way into regular applications, advanced control must be integrated with the industrial control systems. Modern control systems, on the other hand, need to extend the reach of traditional automation systems - beyond control of the process - to also cover the increasing amount of information technology (IT) required to successfully operate industrial processes in today's business markets. The Industrial IT System 800xA from ABB provides a scalable solution that spans and integrates loop, unit, area, plant, and interplant controls. This paper introduces the 800xA and the underlying Aspect Object technology. It is shown how model knowledge and optimization solver technology are integrated into the 800xA framework. This way, advanced model based control solutions can be set up in an open and modularly structured way. New model and solver aspects can be combined with available aspects covering standard functionality like process connectivity, management of process data, trend&history data and application data, as well as operator graphics. A Nonlinear Model-based Predictive controller (NMPC) for power plant start-up is treated as example. This paper discusses how NMPC can be integrated with a modern control system so that standard concepts are re-used for this advanced model based control concept.
Plant-wide control is attracting considerable interest, both as a challenging research field and because of its practical importance. It is a topic [1] characterized by complexity in terms of the number and type of eq...
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