1,721,175 research outputs found
Model predictive control of linear systems with multiplicative unbounded uncertainty and chance constraints
Full text linkAn observer for mass-action chemical reaction networks.
No full textIn biological research, experimental data analysis plays an important role since it enables quantitative understanding of biochemical processes. On the other hand, today's measurement techniques, in continuous development, generally allow measuring a subset of the major system's variables.
Such major issue can be tackled by relying on a system's and mathematical approach. For instance, first principles modelling of metabolic or signal transduction networks typically leads to a set of nonlinear differential equations.
In this paper we devise a nonlinear observer specifically suited for models of biochemical reaction networks. We show that the observer is locally convergent under certain observability conditions which can be inferred by elementary network analysis.
The applicability and performance of the outlined observer are shown considering the state estimation problem for a benchmark biochemical reaction
network
Model predictive control of linear systems with multiplicative unbounded uncertainty and average constraints
No full textDistributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems
No full textThis paper presents a novel Distributed Predictive Control (DPC) algorithm for linear discrete-time systems. This method enjoys the following properties: (i) state and input constraints can be considered; (ii) under mild assumptions, convergence of the closed loop control system is proved; (iii) it is not necessary for each subsystem to know the dynamical models of the other subsystems; (iv) the transmission of information is limited, in that each subsystem only needs the reference trajectories of the state variables of its neighbors. A simulation example is reported to illustrate the main characteristics and performance of the algorithm
Simulation error minimization identification based on multi-stage prediction
No full textClassical prediction error minimization (PEM) methods are widely used for model identification, but they are also known to provide satisfactory results only in specific identification conditions, e.g. disturbance model matching. If these conditions are not met, the obtained model may have quite different dynamical behavior compared with the original system, resulting in poor long range prediction or simulation performance, which is a critical factor for model analysis, simulation, model-based control design. In the mentioned non-ideal conditions a robust and reliable alternative is based on the minimization of the simulation error. Unfortunately, direct optimization of a simulation error minimization (SEM) criterion is an intrinsically complex and computationally intensive task. In this paper a low-complexity approximate SEM approach is discussed, based on the iteration of multi-step PEM methods. The soundness of the proposed approach is demonstrated by showing that, for sufficiently high prediction horizons, the k-steps ahead (single- or multi-step) PEM criteria converge to the SEM one. Identifiability issues and convergence properties of the algorithm are also discussed. Some examples are provided to illustrate the mentioned properties of the algorithm
Data-based control design for linear discrete-time systems with robust stability guarantees
No full textThis paper proposes a method based on virtual reference feedback tuning with robust closed-loop stability guarantees in a linear single-input and single-output setting. The proposed method is not a fully direct data-driven approach since an uncertainty set for the system is obtained through a set membership identification. Based on the uncertainty set, robust stability conditions are enforced as linear matrix inequality constraints within an optimization problem whose cost function relies on virtual reference feedback tuning. The effectiveness of the algorithm is demonstrated in a simulation example
Tube-based robust sampled-data MPC for linear continuous-time systems
No full textThis note extends to the continuous-time case the “tube-based” approach for the design of discrete-time robust model predictive control (MPC) algorithms developed in Mayne, Seron, and Raković (2005). This extension is of interest in view of the simplicity and popularity of the method as well as of the industrial relevance of continuous-time implementations of MPC. The proposed robust control law is composed of two terms: (1) a sampled-data MPC control law and (2) a continuous-time state feedback term
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