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The application of a Moving Horizon Estimator (MHE) to the problem of vehicle side-slip angle estimation is studied. In particular, this work focuses on how MHEs can be designed in order to provide estimates of the variable of interest with guaranteed estimation error. The problem is solved using a nonlinear regression observer identified directly from data.
The subject of this paper is modeling of the influence of non-minimum phase plant dynamics on the performance possible from gradient based norm optimal iterative learning control algorithms. It is established that performance in the presence of right-half plane plant zeros typically has two phases. These consist of an initial fast monotonic reduction of the L2 error norm followed by a very slow asymptotic...
Iterative Learning Control (ILC) is now well established for linear and nonlinear dynamics in terms of both the underlying theory and experimental application. This approach is specifically targeted at applications where the same operation is repeated over a finite duration with resetting between successive executions. Each execution is known as a trial and the novel principle behind ILC is to suitably...
Identifying and discriminating plausible treatment targets for remodeling related bone disorders is a difficult task often involving medical studies and clinical experiments. We propose to apply a global sensitivity analysis approach to a mathematical model describing the process of force-induced bone growth and adaptation. The considered sensitivity analysis approach finds an outer bound on the set...
In this paper we propose a Fault Detection and Isolation (FDI) filter design method for Spark Injection Engines. Starting from a detailed nonlinear mean-value representation of the engine, a LPV approximation is obtained based on a judicious convex interpolation of a family of linearized models. A LPV-FDI filter based on a bank of Luenberger observers is synthesized by ensuring guaranteed levels of...
The paper presents a novel approach to identification of stochastic nonlinear dynamic systems using efficient approximation methods. The motivation behind this work is to develop a computationally efficient and robust algorithm for estimation of wastewater treatment plant model parameters. The mathematical model of the plant is required for the application of advanced predictive control algorithms...
This article presents a way of modeling patient response to a pharmacotherapy by means of dynamic models with quantized output. The proposed modeling technique is exemplified by treatment of Parkinson's disease with Duodopa ®, where the drug is continuously administered via duodenal infusion. Titration of Duodopa ® is currently performed manually by a nurse judging the patient's motor symptoms on...
Identification and online prediction of lifetime of cutting tools using cheap sensors is crucial to reduce production costs and down-time in industrial machines. In this paper, we use the acoustic emission from an embedded sensor for computation of features and prediction of tool wear. A reduced feature subset which is optimal in both estimation and clustering least square errors is then selected...
This paper addresses the problem of idle speed control of hydrogen fueled internal combustion engine (H2ICE) using model predictive control (MPC) and sampled data control (SDC) theories. In the first step, results from SDC theory and a version of MPC are collectively employed to obtain a rigorously developed new generic control strategy. Here, a controller, based on a family of approximate discrete...
Considering obstacle avoidance for mobile robots, it is effective to generate optimal trajectory dynamically in terms of safety and efficiency. Since it usually requires iterative calculation to get an optimal trajectory, however, high computational cost becomes a problem for practical application, especially for low performance computers like industrial embedded CPUs. In this paper, we propose a...
Superfluid helium is used in the cryogenic circuit that cools down and stabilizes temperature of more than 1600 high performance, main superconducting magnets of the Large Hadron Collider (LHC) - the new particle accelerator at European Organization for Nuclear Research (CERN). This paper presents a simulation study of the application of Nonlinear Model Predictive Control (NMPC) to the Superfluid...
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