Mathematical modeling has become an indispensable tool in the analysis, prediction and control of chemical and biological systems. However, the estimation of consistent model parametrizations and model invalidation ar...
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Mathematical modeling has become an indispensable tool in the analysis, prediction and control of chemical and biological systems. However, the estimation of consistent model parametrizations and model invalidation are challenging tasks, but crucial for reliable model-based analysis and prediction. Set-based estimation methods are useful to determine guaranteed outer approximations of consistent parameter sets, i. e. consistent parametrizations are never excluded. However, these conservative outer approximating sets often include inconsistent parametrizations which lead to inconsistent models and hence possibly wrong model-based predictions. This paper proposes a set-based framework to determine inner approximations, i.e. the model is guaranteed consistent with measurement data for all parametrizations from this set. Our approach is based on the reformulation and inversion of measurement data constraints and by imposing nonlinear constraints on binary variables. The relaxation of the mixed-integer nonlinear feasibility problem into a mixed-integer linear feasibility problem allows the inner approximations to be determined efficiently. The applicability of this approach is demonstrated considering a nonlinear biochemical reaction network.
The paper presents a tube model predictive control (MPC) scheme of continuous-time nonlinear systems based on robust control invariant sets with respect to unknown but bounded disturbances. The cost functional of the ...
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The paper presents a tube model predictive control (MPC) scheme of continuous-time nonlinear systems based on robust control invariant sets with respect to unknown but bounded disturbances. The cost functional of the optimization problem is not necessarily quadratic. The scheme has the same online computational burden as the standard MPC with guaranteed nominal stability. Robust stability, as well as recursive feasibility, is guaranteed if the optimization problem is feasible at the initial time instant. In particular, we consider a scheme to obtain robust control invariant sets for a class of Lipschitz nonlinear systems, and to show the effectiveness of the proposed schemes by a simple example.
Currently, the aging of the population has become the world's social problems. The increasing aging population and lots of disability, paralysis makes nursing care more difficult. Because of many elderly can not g...
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This article considers the problem of constrained stabilization of periodically time-varying discrete-time systems, or shortly, periodic systems. A modification of a recent result on periodic Lyapunov functions, which...
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This article considers the problem of constrained stabilization of periodically time-varying discrete-time systems, or shortly, periodic systems. A modification of a recent result on periodic Lyapunov functions, which are required to decrease at every period rather than at every time instant, is exploited to obtain a new stabilizing controller synthesis method for periodic systems. We demonstrate that for the relevant class of linear periodic systems subject to polytopic state and input constraints, the developed synthesis method is advantageous compared to the standard Lyapunov synthesis method. An illustrative example demonstrates the effectiveness of the proposed method.
A new knowledge-based Artificial Fish-swarm Algorithm (AFA) with crossover operator, namely CAFAC, is proposed to combat with the blind search of the original AFA. The crossover operator is explored, and the normative...
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The remaining driving range (RDR) has been identified as one of the main obstacles for the success of electric vehicles. Offering the driver accurate information about the RDR reduces the range anxiety and increases t...
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The remaining driving range (RDR) has been identified as one of the main obstacles for the success of electric vehicles. Offering the driver accurate information about the RDR reduces the range anxiety and increases the acceptance of electric vehicles. The RDR is a random variable that depends not only on deterministic factors like the vehicle's weight or the battery's capacity, but on stochastic factors such as the driving style or the traffic situation. A reliable RDR prediction algorithm must account the inherent uncertainty given by these factors. This paper introduces a model-based approach for predicting the RDR by combining a particle filter with Markov chains. The predicted RDR is represented as a probability distribution which is approximated by a set of weighted particles. Detailed models of the battery, the electric powertrain and the vehicle dynamics are implemented in order to test the prediction algorithm. The prediction is illustrated by means of simulation based experiments for different driving situations and an established prognostic metric is used to evaluate its accuracy. The presented approach aims to provide initial steps towards a solution for generating reliable information regarding the RDR which can be used by driving assistance systems in electric vehicles.
This paper presents a novel approach for semantic classification of scenes and places with omnidirectional images. The objective of scene classification is to segment and classify different regions in the image wherea...
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This paper presents a novel approach for semantic classification of scenes and places with omnidirectional images. The objective of scene classification is to segment and classify different regions in the image whereas place recognition assigns a single category to the entire image. In scene classification image regions are classified into categories floor, vertical planar surfaces and isolated objects e.g. furniture. The semantic segmentation extracts multiple heterogeneous visual features at the superpixel-level that are labeled by randomized decision trees. The place recognition relies on a global image representation (GIST) and two local densely extracted shape and appearance representations(HOG, dense SIFT). A support vector machine predicts place categories such as room, corridor, doorway and open space from these visual features.
This paper proposes a novel approach for a derandomized covariance matrix adaptation for multi-objective optimization. Common derandomized multi-objective algorithms only utilize the information gained from successful...
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This paper proposes a novel approach for a derandomized covariance matrix adaptation for multi-objective optimization. Common derandomized multi-objective algorithms only utilize the information gained from successful mutations. However in case of optimization problems with a limited budget for fitness evaluations inferior mutations provide additional information to adjust the search. The proposed algorithm, called active-(μ+λ)-MO-CMA-ES, extends previous approaches as it reduces the covariance along directions of unsuccessful mutations. In experiments on a set of commonly accepted multi-objective test problems the presented algorithm outperforms other derandomized evolution strategies.
Harmonic disturbances in servo applications, in particular rotating actuators, are common and lead to a non-smooth motion with ripples. A novel variable proportional-integral-resonant (PIR) control which combines a PI...
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Harmonic disturbances in servo applications, in particular rotating actuators, are common and lead to a non-smooth motion with ripples. A novel variable proportional-integral-resonant (PIR) control which combines a PI and PR regulation is proposed. The main PIR control feature is its ability to track both dc and ac quantities at the same time. The design and analysis of the closed control loop are performed in frequency-domain via the open loop characteristics. The control gain boundaries due to the time delays are addressed. The experimental evaluation of compensating two first harmonics at different reference velocities discloses the variable PIR control as superior in comparison to the underlying optimal PI one.
The goal of multi-parametric quadratic programming (mpQP) is to compute analytic solutions to parameter-dependent constrained optimization problems, e.g., in the context of explicit linear MPC. We propose an improved ...
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