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Power systems with high wind power experience increased variability and uncertainty. Therefore accurate forecast technique is essential to cope with the uncertainty in day-ahead electricity market. Statistical forecast model is considered as a powerful technique for wind-power forecast, however, the forecast accuracy significantly drops as the forecast horizon grows. Focusing on wind speed persistence,...
The future power grid will need to incorporate systems and processes with a higher degree of variability and randomness due to the penetration of renewable energy resources and the increase of energy demand. Forecasting variables in a more uncertain environment poses new challenges and revisions of the existing forecasting methodologies will have to be made to maintain forecasting accuracy. This paper...
In view of the uncertainty nature, integrating large-scale renewable generation aggravates the complexity of the system scheduling due to the uncertainty existing in both supply and demand sides. To reduce the quantity of uncertain variables, the uncertain variables are divided into two parts: the certain forecast and uncertain forecast error, and a total system forecast error integrating with all...
In this paper we present an approach to deal with structural uncertainties (structure and dimension of the model are not perfectly known) in the framework of nonlinear model predictive control (NMPC). The presented method is based on robust multi-stage NMPC which represents the uncertainty as a scenario tree with the possibility of recourse, reducing the conservativeness of the approach. In the case...
This contribution presents the design of a stochastic tube-based model predictive controller for the application of energy management in hybrid electric vehicles. While previously proposed strategies commonly assume the driving profile to be perfectly known, the effects of uncertain driver behavior, i.e. uncertain driver torque demands, are neglected. In this paper, a stochastic control strategy is...
Disturbances and uncertainties make very difficult the control of Unmanned Aerial Vehicles (UAV). The problem is even worse, if delays are involved in the process. The uncertainty and disturbance estimator (UDE) is a technique based on the idea that the unknown dynamics and disturbances of a system can be accurately approximated by passing them through a filter with the appropriate bandwidth. This...
The large-scale integration of stochastic generation requires additional operating reserves to cope with the uncertainty in power system operation, due to the large forecast error with existing methodologies. Previous research shows that allocating power reserves dynamically according to conditional distribution of power forecast error would benefit the generation scheduling in day-ahead electricity...
Prognostics and health management has become a subject of great interest to many electrical systems. However, the lithium-ion batteries are a core component of many machines and critical to system's functional capabilities. Remaining useful life prediction is central to the PHM of the lithium-ion batteries. The remaining useful life of lithium-ion batteries is defined as length of time from current...
Prosodic structure contributes to speech production and comprehension. One of the crucial problems in achieving natural-sounding synthesized speech is the prediction of appropriate phrase boundaries. Unfortunately, obtaining human annotations of prosodic phrases to train a supervised system can be laborious and costly. Active learning has been proven effective in reducing labeling efforts for supervised...
In the context of transmission system planning, research proposes methods to assess the effect of uncertainties of power system operating condition due to forecasting errors of intermittent generation and loads. In particular probabilistic power flow methods are illustrated to calculate the probability distributions of the voltages and the branch currents, starting from the distributions of power...
Reliable precision grasping is a pre-condition for manipulation tasks e.g. in assembly and packaging tasks. Especially for small and light objects robust grasping is extremely challenging since even slight errors in the object pose or dimensions lead to irreparable failures caused by unintended finger-object contacts. State of the art techniques address the problem of grasping in the presence of uncertainty...
This paper proposes a model-based predictive sampled-data controller with a large fixed sampling rate h. Although the linear-time-invariant (LTI) plant is unknown, a nominal model is available. This nominal model is used to predict and compensate the influence of the large sampling using the measured information from the plant. The controller is designed on the basis of the nominal model. The robustness...
Many, perhaps even most, applications of low-temperature plasmas involve mixtures of molecular gases that give rise to complex chemistry. Often, an important task in developing, optimizing and perhaps controlling such applications is understanding which chemical species are important, and by what mechanisms these important species are created and destroyed. Developing a model can be an important step...
This study investigates the performance of a multivariate Ensemble Kalman Filter coupled with a relocatable limited-area configuration of the Regional Ocean Modeling System to predict ocean states by assimilating a heterogeneous data set involving underwater gliders and ship observations. In particular, two different ensemble initialization techniques are exploited and evaluated with the dataset collected...
This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated...
The inclusion of footprint of uncertainty (FOU) in Interval Type-2 Fuzzy Logic Systems (IT2FLSs) made them suitable for modelling uncertainty. This paper investigates the impact of FOU size and number of membership functions (MFs) on the model's prediction performance. An IT2FLS trained using a fast learning method is designed here. The uncertainty in data is captured by designing the IT2FLS with...
This paper proposes an approach to extend the mixed logical dynamical modelling framework for synthesizing robust optimal control actions for constrained piecewise affine systems subject to bounded additive input disturbances. Rather than using closed-loop dynamic programming arguments, robustness is achieved here with an open-loop optimization strategy, such that the optimal control sequence optimizes...
This work describes the application of a Min-Max predictive controller to a control laboratory plant. Min-max formulations of Model Based Predictive Control (MBPC) are one of the possible approaches in the literature to deal with the control of plants subject to bounded uncertainties. One of the drawbacks of Min-Max MBPC is the amount of calculation required to find a control sequence. The controller...
This paper introduces a mechanism for testing multivariable models on which model-based controllers are designed. Although external excitation is not necessary, the data collection includes a stage where the controller is switched to open-loop operation (manual mode). The main idea is to measure a certain "distance" between closed-loop and open-loop signals, and then trigger a flag if this...
This paper presents a tuning method of Smith predictor controller based on the difference between model parameters and real ones, in contrast with standard methods that consider an exact model match. For first order process models, without and with integrator and parametric uncertainties, was demonstrated that proposed tuning solution ensures the robustness of stability and performances.
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